Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
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Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
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Dr. Kyuchang Kang, ETRI, Korea
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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Jin REN, North china university of technology, China
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Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2020 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2020 GiRI (Global IT Research Institute)Volume. 9 Issue. 1

5J

1 Requirements Knowledge Model Construction and Requirements Elicitation Method of

1265

Avionics Systems Software based on Multi-ontology
Xuan Hu, Jie Liu
Information Security Research Center, CEPREI, No. 110, Dongguanzhuang Road, Guangzhou, China
Key laboratory of Ministry of Industry and Information Technology, No. 110, Dongguanzhuang Road, Guangzhou, China

Volume. 9 Issue. 2

6J
1280

1 Evaluation of A Mesh Network based on LoRa Technology
Van Dai Pham*, Victor Kisel*, Ruslan Kirichek *, **, Andrey Koucheryavy*, Alexander Shestakov** Bonch-Bruevich St. Petersburg State University of Telecommunications, Saint-Petersburg, 193232, Russia** V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russia

Volume. 9 Issue. 3

6J

1 Orthogonal Defect Classification-based Ontology Construction and Application of Software-1286

hardware Integrated Error Pattern of Software-intensive Systems
Xuan Hu, Jie Liu
Information Security Research Center, CEPREI, No. 110, Dongguanzhuang Road, Guangzhou, China
Key laboratory of Ministry of Industry and Information Technology, No. 110, Dongguanzhuang Road, Guangzhou, China

Volume. 9 Issue. 4

6J

1 Estimation of Network Type Based on The Response Delay Property

1299

Rui Teng, Kazuto Yano, and Yoshinori Suzuki
Wave Engineering Laboratories, Advanced Telecommunications Research Institute International
Kyoto 619-0288, Japan

Copyright ⓒ 2020 GiRI (Global IT Research Institute)Volume. 9 Issue. 5

6J

1 Determination of Subscribers Coordinates using Flying Network for Emergencies

1309

Truong Duy Dinh*, Vladimir Vishnevsky**, Van Dai Pham*, Duc Tran Le***, Ruslan Kirichek*,**,
Andrey Koucheryavy**Department of Software Engineering and Computer Technology, Bonch-Bruevich Saint-Petersburg State University of
Telecommunications, 22 Prospekt Bolshevikov, St. Petersburg, Russia.
**V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street,
Moscow, Russia***Information Technology Faculty, The University of Danang - University of Science and Technology, 54
Nguyen Luong Bang, Danang, Vietnam.

1315

2 Role of Teachers in Developing Learning Pedagogy
Deepanjali Mishra*and Mangal Sain***Department School of Humanities KIT University Bhubaneswar, Orissa, India**Division of Information and Computer Engineering, Dongseo University, South Korea

Volume. 9 Issue. 6

6J

1 A Survey on the Security in Cyber Physical System with Multi-Factor Authentication

1322

Mangal Sain*, Oloviddin Normurodov*, Chen Hong**and Kueh Lee Hui****Division of Information and Computer Engineering, Dongseo University, South Korea**Department of Linton School of Global Business, Hannam University, Daejeon, Republic of Korea***Dept. of Electrical Engineering, Dong-A University, South Korea

2 Web based Learning: A Methodology to Teach Literature in a Classroom

1329

Deepanjali Mishra*and Mangal Sain***Department School of Humanities KIT University Bhubaneswar, Orissa, India**Division of Information and Computer Engineering, Dongseo University, South Korea

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

1265

Requirements Knowledge Model Construction
and Requirements Elicitation Method ofAvionics Systems Software based on
Multi-ontology
Xuan Hu, Jie Liu
Information Security Research Center, CEPREI, No. 110, Dongguanzhuang Road, Guangzhou, China
Key laboratory of Ministry of Industry and Information Technology, No. 110, Dongguanzhuang Road, Guangzhou, China
huxuan@ceprei.com, 47507346@qq.com

Abstract—Avionics systems software usually has thecharacteristics of complex structure, wide function coverage,
high reliability, and high safety. These characteristics have led
to the increasing scale and complexity of software systems, andthe increasing variety of research & development (R&D)
personnel. This further intensify the knowledge-intensive trendof software development, and make the software requirements
elicitation activities more complicated. In the knowledge
engineering community, an ontology is an explicit specificationof a conceptualization. This paper uses the ontology method toconstruct a requirement knowledge framework andrequirement knowledge is expressed as a clear, complete, andconsistent hierarchical ontology concept and association, which
is more conducive to knowledge sharing and reuse as well as
reflects multiple viewpoints of stakeholders. This paper builds a
requirements knowledge multi-ontology framework which is
divided into GO (generalization ontology), TO (task ontology),
DO (domain ontology), and AO (application ontology). And it
decomposes the GO into structure ontology and action ontology
in the framework. It makes up for the deficiency of
undifferentiated knowledge representation of the GO. Then,
this paper proposes and integrates the concept of software
requirement error pattern into the multi-ontology framework
in a consistent form. In addition, this paper evaluates thequality of the constructed ontology and the evaluation results
show that the constructed ontology in this paper has a highquality. Finally, this paper facilitates the requirements
elicitation based on the multi-ontology framework to avoid
errors and improve the quality and reliability of software
products.
Keyword—error, ontology, requirements elicitation, software
requirements———————————————————————
Manuscript received Dec. 2, 2019. This work was supported by the
National Key Research and Development Program of China under Grant
2019YFC0118800. and a follow-up of the invited journal to the accepted &presented paper entitled “Researches on Software Requirements Elicitation
approach of the Aviation Electronics Systems based on Multi-ontology” ofthe 22th International Conference on Advanced Communication Technology(ICACT2020).
Xuan Hu, she is a senior engineer of CEPREI Laboratory, China. (E-mail:huxuan@ceperi.com)
Jie Liu, he is the director of the Information Security Research Center of
CEPREI
Laboratory,
China.
(corresponding
author,email:
47507346@qq.com)

S

I. INTRODUCTION

OFTWARE requirements elicitation (SRE) is the mostcritical knowledge-intensive activity in a softwaredevelopment process; however, implementing effective
requirements elicitation and obtaining correct, complete,consistent, and unambiguous requirements specifications
remains a problem that plagues system analysts and softwaredevelopers. These issues also exist in avionics systems SRE
activities and have a significant impact on their quality. An
important reason for the above problems is the lack of aneffective knowledge sharing bridge between systemdevelopers and domain users [1]. In practical applications,some system developers lack knowledge about softwaresystem problem domains. They passively wait for domainusers to provide requirements and develop requirements
specifications based on their own understanding. This
inevitably cause a part of the requirements to be
misunderstood, resulting in the generation of low-quality
requirements specifications. On the other hand, although thedomain knowledge possessed by domain users and domain
experts plays an important role in high-quality requirements
elicitation activities, they do not know how to accurately
express requirements that developers can understand inaccordance with software development guidelines. The
above two reasons ultimately cause the ambiguity ofrequirements elicitation. In addition, the increasing scale andcomplexity of software systems increase the difficulties inacquiring complete knowledge and cause the incompletenessof requirements elicitation. Moreover, different teams withmultiple-views and multi-paradigm development methods
are widely used in the development of such complex softwaresystems, which increases the heterogeneity of software
requirements specifications (SRSs) and cause theinconsistency of SRSs [1].
A knowledge-based requirements elicitation method canbe used to solve the above problems; its purpose is to use
domain analysis and experience to help software system
stakeholders understand the application domain and define
requirements. The key is to model the domain knowledge as ashareable knowledge framework. Under this framework,
domain users can more easily and conveniently express theirneeds, while the domain developers can understand therequirements more accurately. Moreover, the heterogeneity
Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

brought about by multiple viewpoints and paradigms can be
minimized. Ontology is a logic theory that explains the
expected meaning of formal vocabulary, i.e., its ontologicalcommitment to a specific conceptual world. The expectedmodel of logic language uses the vocabulary constrained byontology commitment. And an ontology reflects thiscommitment (and potential conceptualization) indirectly byapproximating the expected model [2]. In the knowledge
engineering community, an ontology is a formal and explicit
specification of a shared conceptualization [3, 4]. Therefore,
introducing the ontology method into the SRE process is aneffective way to solve the above problems. By adopting theontology method, the requirement knowledge can be
expressed as an ontological concept and association;
therefore, it is clear, complete, and consistent and isconducive to the sharing and reuse of knowledge. Literature[5] designed an ontology in a case study for co-simulation ina model-based system engineering tool-chain. They argued
that an ontology refers to content about the types of objects,
their properties, and their relationships, which represent
domain-specific knowledge.
This paper proposes a requirements knowledge
multi-ontology
framework(RKMOF)
integrating
information about software requirement error pattern (SREP)
to solve the problems of ambiguity, incompleteness, andinconsistency in the requirements elicitation of avionics
systems software. Then, this paper uses a criteria-based
evaluation approach to evaluate the quality of the software
requirements elicitation ontology, including ontology
validation and ontology verification. Ontology validation
checks if the correct ontology has been built, whereas
ontology verification checks if the ontology has been built
correctly [6]. Moreover, this paper studies the software
requirements elicitation method based on the RKMOF. The
rest of this paper is divided into the following sections:section 2 presents the state of the art of ontology-based
requirements elicitation and ontology evaluation. Section 3
describes the construction of avionics systems RKMOF andobtains the quadruple representation including thegeneralized ontology (GO), the domain ontology (DO), thetask ontology (TO) and the application ontology (AO).
Section 4 describes the composition of avionics systems
software requirements knowledge ontology, including its
constituent elements and hierarchical structure, presents thedefinition and ontology representation of SREP, anddescribes the evaluation of avionics systems SRE ontology interms of ontology validation and ontology verification.
Ontology validation is achieved by applying two validation
methods [6]. The first is the ontology content evaluation, andthe second is answering competency questions. Ontology
verification is achieved using two methods, also [6]. The firstis the ontology taxonomy evaluation, and the second is the
improved FOCA methodology [7] adding the metrics ofontology cohesion and ontology coupling [8]. On this basis,
section 5 describes the RKMOF-based SRE method. Section
6 shows the results of engineering applications.

II. RELATED WORKThe current state of the art of ontology-based requirements
elicitation and ontology evaluation is presented in thissection.

1266

A. Ontology-based requirements elicitation
Nowadays the typical representatives of ontology-based
requirements elicitation methods include, the requirements
elicitation method based on a multi-ontology framework [9],the automatic elicitation method based on an enterprise
ontology [10], the method of using a DO as the domain
knowledge for requirements elicitation [11], the use of
semi-formal representation of semantic guidance consistingof domain concepts, associations, and axioms to assist
requirements elicitation activities [12], the use of formal
ontology representation of original requirements based on the
top-down refinement of meta-model to elicit and analyze
requirements [13]. The first two methods mainly solve theproblems of ambiguity and inconsistency in the process ofrequirements elicitation. The third method focuses on the
ontology-based requirements elicitation and analysis, and thequality measurement of requirements document; however, it
does not conduct in-depth research on how to construct ahigh-quality ontology. The semantic guidance proposed by
the fourth method uses the concepts, associations, andaxioms of a DO to provide a series of suggestions forrequirements engineers to obtain requirements. The fifth
method uses ontologies to elicit and analyze requirements,and studies how to evaluate the completeness andconsistency of requirements to a certain extent. None of theabove methods can simultaneously solve the problems ofambiguity, incompleteness, and inconsistency of
requirements elicitation; thus, the scope of use and effect arelimited.
Based on the above review and analysis, this paper
proposes an SRE method based on a RKMOF integrating theinformation about SREP to solve the problems of ambiguity,
incompleteness, and inconsistency simultaneously.

B. Ontology evaluation
Ontology evaluation can be defined as “a technicaljudgment of the content of the ontology with respect to aframe of reference during every phase and between phases of
their life cycle” [14]. To achieve the best results andhigh-quality ontology, one needs to choose from the available
list of aspects of ontology to be evaluated; the right approachto evaluation; the right mix of criteria to be evaluated; andalso the right tools to be used [15].
1 ASPECTS
Aspects include the vocabulary, syntax, structure,
semantics, representation, and context of the ontology, which
are defined according to literature [15-17].
2 APPROACHES
The different known methods and techniques can bemainly assigned to four different kinds of approaches:
technology-based, quality-attribute based, data-driven andapplicationortask-based
evaluation
[15,
18].
Technology-based evaluation investigates the syntax,
consistency and formal semantics and thereby ensures thecorrectness and usability of the ontology. Its typicalrepresentative is OOPS!, a web-based tool which isaccompanied by a catalogue of potential and common pitfalls[19]. However, this approach cannot tell anything about thequality of the content and applicability of the ontology [20,21]. Quality-based approach offers a quantitative evaluation
which relies on a set of predefined metrics that measureindividual quality attributes of an ontology. Yet, some of

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

those quality metrics tend to be hard to measure and mightneed human experts to evaluate [17]. Its typicalrepresentatives
include,OntoClean
methodology,
OntoMetric [22], OntoQA [23], etc. Data-driven evaluation
approach concentrates on the usability of an ontology
considering its future application and has also been thecurrent focus of recent research [24-26]. This approach
attempts to analyze how adequate an ontology covers thedomain but is not applicable to determine the correctness or
clarity of the ontology [27, 28]. Application or task-based
evaluation approach would typically involve evaluating how
effective an ontology is in the context of a specificapplication [29]. This approach exhibits a limitation: theresult obtained from one task may not be useful for anothertask as each task is different [18], i.e., it is not suited for ageneral evaluation, because every ontology must be
evaluated individually depending on the application context[27].
3 CRITERIAThis kind of evaluation approach is done by humans who
try to assess how well the ontology meets a set of predefined
criteria, standards, requirements, etc. [30] Various criteria
have been proposed in literature to evaluate the quality of
ontology [15-17, 31]: consistency, completeness, accuracy,
conciseness,correctness,computational
efficiency,
adaptability, clarity.
4 TOOLS
Various tools have been developed to support the task of
ontology evaluation, each concerned with different aspects ofevaluation. There exist tools for checking the consistency, thestructure or modeling mistakes of the ontology [21]. Various
available tools include: ODEClean, ODEval, AEON, Eyeball,Moki, XD-Analyzer, OQuaRE, OntoCheck, OntoQA,
OntoClean, OntoMetric, ACTiveRank, OOPS!, ODEval,
oQual [15].
Based on the above review and analysis, this paper adopts
a criteria-based evaluation approach to evaluate the ontology
quality, including ontology validation and ontology
verification.
III. AVIONICS SYSTEMS RKMOF
To achieve high-quality requirements elicitation requires
various of knowledge; therefore, knowledge aided design
system (KADS) can be used to study knowledge systemmodeling [9]. The knowledge layers in this model are clearly
divided, and each layer of knowledge has good
maintainability and reusability; however, its shortcomings
are also obvious: the lack of strong bonds between theknowledge at all layers; incomplete knowledge. Fig. 1. shows
an example of a portion of knowledge system of an avionics
system, which contains elements such as classes, instances,
relationships, etc. However, these are not enough for building
a complete knowledge system; in addition, there is also a lackof division of knowledge layers. In general, a complete andhigh-quality knowledge system should include: concepts(classes), object properties, data properties, restrictions on
properties, property characteristics, relationships, classhierarchy, instances, mappings and rules. Therefore, for theabove knowledge model to play a role in knowledge sharing
and reuse, it is also necessary to integrate relatively
independent knowledge layers to form a knowledge system.
Ontology method is an effective way to achieve this goal.

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Ontologies can be classified into different types, according
to the level of abstraction of the universe of discourse theontology models. According to Guarino [2], there are fourtypes of ontologies: top-level ontologies, domain ontologies
and task ontologies, application ontologies. Top-level
ontologies describe very general concepts which are
independent of a particular problem or domain. Domain
ontologies and task ontologies describe general domains.
These types of ontologies specialize in the concepts
introduced in the top-level ontology. Application ontologies
describe concepts depending on a particular domain and task,
System
Avonics
system
UAV system

Aircraft electronicsystemFlying control
systemCommunication
systemis_aNavigation
system

UAV flying control
systemisPartOf

Types of systems
making up UAV
systemA certain type of UAV
flying control systeminteracts
individual

Fig. 1. Example of portion of knowledge system of avionics system

which are often specializations of both the related ontologies.
On this basis, combined with existing research results [10, 32]
and practical experience, this paper obtains the tuple ofavionics systems RKMOF as follows:
Definition 1 RKMOF = <GO, DO, TO, AO>,
where:GO, DO, TO and AO respectively represent thegeneralization ontology, domain ontology, task ontology andapplication ontology of avionics systems.
The relationships between various ontologies in thisdefinition are shown in Fig. 2. A GO can be mapped to a TO,
instantiated to a DO and eventually an AO. The GO can bedivided into structure ontology and action ontology. Both the
DO and the TO can be reused in the same domain,transformed into a domain requirements model (DRM)
through domain analysis and an application requirements
model (ARM) by reuse. In this framework, error data,
domain knowledge and industry standards are the sources of
DO. This framework is multi-viewpoint because domain
experts, users and developers can all participate in theprocess of framework construction.
Structure ontology

AEGO

Action ontology
Domainanalysis

instantiation
uses

TO

basis

Error data

instantiation

Domain knowledge

Application
analysis

Industry standards

Application
requirements

DRM

DO

AO

reuses

ARM

Ontology
language
Ontology
edit toolFig. 2. Avionics systems RKMOF

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

IV. CONSTRUCTION OF AVIONICS SYSTEMSREQUIREMENTS KNOWLEDGE ONTOLOGYThis section presents the constituent elements of therequirements knowledge ontology of avionics systems andstudies the hierarchical structure of the ontology.

A. Constituent Elements of Avionics Systems Requirements

Knowledge Ontology
The universe of ontology constituent elements U is: U ={Concepts, Object properties, Data properties, PR, PC,
Inherit-hierarchies, Relationships, Instances, Mappings,
Rules}. Concepts, Inherit-hierarchies, Relationships, andInstances build the basic skeleton of a knowledge ontology.
All other elements are attached to and refine the basic
skeleton. Object properties and Data properties are two typesof properties. The Object properties connect instances
together, and the Data properties connect instances andvalues together. PR indicates the restrictions on properties,
including the restrictions on the type, range, andmaximum/minimum number of property values. P C indicatesproperty characteristics. Mappings represent the mappings
between different levels of ontology. Rules include axioms
and custom rules. The Rules can be used to constrain
information, prove correctness, or derive new information.
The Rules can also be used to express richer relationships
between concepts.

B. Hierarchies

of Avionics Systems Requirements
Knowledge Ontology
1 Avionics systems GO construction
Definition 2 Generalized ontology = <Concepts, Object
properties, PC, Inherit-hierarchies, Relationships, Rules>.
It is clearly that the construction of a GO can be achievedby constructing its concepts (classes), class hierarchy,
relationships, properties, and property characteristics. The
specific steps are shown in Fig. 3.
1 Determine the scope and purpose of an ontology and clarify the relevant knowledge of an avionics system
2 Acquire preliminary knowledge based on expert understanding and experience, and stakeholder needs
3 Acquire the description of conceptual terms/properties, hierarchies and relationships throughconceptualization-relationship and other data analysis
a Analyze the concept hierarchies of a GO
b Analyze the concept relationships of a GOc Build the concept space of a GO
4 Build an initial informal ontology

Fig. 3. Specific steps of GO construction

1) GO concept classes and class hierarchy
Fig. 4. shows a portion of the class hierarchy of GO. The
concept classes marked with an “*” are non-terminal concept
classes, and the rest are terminal concept classes. Define aninheritance relationship as:
Definition 3 An inheritance relationship is a mechanism of a
sub-class automatically sharing the properties and structureof a parent class in the GO concept class hierarchy.
Then, the sub-class and the parent class of non-terminal
concept classes form an inheritance relationship. Therefore, a
new class can be implemented based on an existing concept
class by taking the content defined by the existing class as its
own content and adding new content.
Fig. 4. also shows that the GO concepts contain both staticand dynamic ontology elements.

1268
GO
EffectState

EventFlow
StaticEntity*

State*
OriginalState

DynamicInteraction*

SeqAction
Objective*

System*Resource

Agent*

Component*

Task*

Constraint*

Action*Environment*
is_a
isPartOf

People

PreConstraint

CondAction
AlterAction

PostConstraint

DomainConstraint

LoopAction
ConcAction

Fig. 4. Portion of the class hierarchies and relationships of GO

2) Decomposition of GO
The GO concept classes and relationships areindistinguishable representations of knowledge. This paper
further divides the GO into structure ontology (O structure) andaction ontology (Oaction), which describe the most basic
structure and functions of a system, using the first-order
predicate logic as: GO = Ostructure ∪ Oaction.
⚫ Ostructure
Structure is a set of constraints on system actions,
including a series of systems, people, agents, components,
environments, static attributes, and dynamic interactions. A
system has one or more tasks, and each task is defined by aseries of goals that need to be facilitated. The system
consumes some resources when performing actions andfollows certain restrictions. The concept types of O structure areshown in Fig. 5.
Schema Structure
Signature
hasMember: P Environment | P System | P Objective | P Task | Resource
hasSubObjective: P Objective
hasSubTask: P Task
System:=People | Agent | Component
Environment:= ExpEn | ImpEn
has: Task+ Objective
need: Task+ Resource
interact: System+ Environment
interact: Environment+ System
Constraints
Member   , Objective   , Resource  Fig. 5. Concept types of Ostructure⚫ Oaction
An action describes the state transition of a system or anenvironment. An initial state triggers an action and the action
generates a new state. An action is usually executed in theform of transition from an input to an output (with constraints,
such as time, memory size, etc.). In addition, actions can bedivided into simple actions and complex actions. The simple
actions have the characteristics of the smallest granularity
and the indivisibility of actions. The complex actions include
sequential actions, conditional actions, alternative actions,loop actions, and concurrent actions. The concept types ofOaction are shown in Fig. 6.
3) Concept relationships and concept space of GO
GO concept relationships can be obtained based on theclass hierarchies. By integrating the concepts (classes), theclass hierarchies, the concept relationships, the properties,
and the property characteristics into a whole, a portion of theconcept space of GO is obtained as shown in Table I. The left
side of arrow is a source concept node, and the right side is a
destination concept node. It can be proved that theinheritance relationship is a partial order relationship,
expressed as: ab . The relationship “Interact” is asymmetric relationship.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020
Action := SimAction|ComAction
ComAction:=
SeqAction|CondAction|AlterAction|LoopAction|ConcAction
Actor := System|Environment
Schema SimAction//simple action
Signature
hasSubObjective: P Objective
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , operateOn  Schema SeqAction//sequence action
Signature
hasSubObjective: P Objective
subAction: SeqAction
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , subAction   , predecessor   , successor  
Schema CondAction //conditional action
Signature
hasSubObjective: P Objective
subAction: CondAction
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , subAction   , cond  Schema AlterAction//alternative action
Signature
hasSubObjective: P Objective
subAction: AlterAction
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , subAction  Schema LoopAction//loop action
Signature
hasSubObjective: P Objective
subAction: LoopAction
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , subAction  Schema ConcAction//concurrent action
Signature
hasSubObjective: P Objective
subAction: ConcAction
triggeredby: OriginalState
produce: EffectState
Constraints
hasSubObjective   , subAction  Fig. 6. Concept types of Oaction
TABLE IPORTION OF ONTOLOGY CONCEPT SPACE BASED ON STRUCTURE AND
ACTION CONCEPT TYPES
Concept
Concept
Relationship value types
PC
types
relationships
partialObjective
P Objective
order
partialTask
P Taskorder
Structurehas
Task+→Objective
needs
Task+→Resource
interacts
System+→Environment
symmetrysymmetryinteracts
Environment+→System
Actiontriggeredby
produces

OriginalState
EffectState-

2 Avionics systems DO construction
Definition 4 A domain is a collection of systems with similar
or identical functions used to address specific domain issues.
When dealing with different application needs, the systems
have variability.
A DO is a special ontology used to describe the knowledgeof the specified domain. It presents a description of domain
concepts and relationships, domain activities, and domaincharacteristics and laws obtained by instantiating thecorresponding content in the GO.
1) Definition of DO
Definition 5 DO = <DomConcepts, Object properties, Data
properties,
PR,
PC,
DomInherit-hierarchies,
DomRelationships, Rules, Dommappings>,
where:
DomConcepts = < DomConceptname, DomConcept-ID>;
DomInherit-hierarchies = < DomInherit-ID, Argument<Argument1 <cardinality, (status)>, Argument2< cardinality,(status)>>>, (Argument1, Argument2  DomConcepts);
DomRelationships=<
DomRelationships-name,
DomRelationships-ID, Argument<Argument1<cardinality,(status)>, Argument2< cardinality, (status)>>>, (Argument1,
Argument2  DomConcepts).

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DomInherit-hierarchies are the inheritance relationships of
domain concept classes. An inheritance relationship isdefined as a binary relationship possessing two parameters.
“Cardinality” and “Status” can be used to characterize eachparameter. DomRelationships refer to the relationships in the
DO except the inheritance relationships. Dommappings are
the generic relationships of domain concepts. They are a total
function from the DomConcepts to the Concepts. They map
the concept classes in the DO to the concept classes in the GO.
According to this function, the equivalence relationship on
the DomConcepts can be defined as:
Definition 6 a≡domainb iff Dommappings (a) = Dommappings(b) = t, a, b  DomConcepts, t  Concepts. This equivalence
relationship can be recorded as [t]≡domain.
2) Concepts set and concept relationships set of DO
The construction steps of DO concepts set and concept
relationships set can refer to the corresponding steps of GO,
and is not repeated here. This paper limits the domain toflying control (FC) systems. For the specific contents of theconcepts set and concept relationships set of FC systems,
please refer to the reference [32].
3 TO construction
A TO is a collection of vocabularies that describes the
structure of a problem-solving method (PSM). It provides
several primitives, according to which domain experts candescribe the PSM context and make the process of integrating
domain knowledge into the PSM context easier. Since thispaper studies the requirements elicitation method based on
the ontology, the definition of requirements elicitation TO(RETO) is given as follows [32]:Definition 7 RETO = <Requirements-Eliciting Task,
Requirements-Eliciting Task-PSM, Requirements-Eliciting
Taskmappings>,
Specifically expressed as:
Ontology name: RETO
Type: TO
{ID: // identifier
Requirements - Eliciting Task: = <Task ID, Circumstance>,
Requirements - Eliciting Task - PSM: = <Competence,
Operational specification, Requirements>, Competence: =<Input Action, Output Action, Objective>, Operational
specification: = <Inference Steps, Control Flow between theInference Steps, Data Flow between the Inference Steps>,
Requirements: = <Requirements - Eliciting Task Concepts,
Requirements - Eliciting Task Relationships, Requirements Eliciting Task Facts, Rules>, Requirements - Eliciting
Taskmappings:{Requirements - Eliciting Task Concepts →Concepts}
4 AO construction
The definition of AO is given below:
Definition 8 AO = <AppConcepts, Object properties, Data
properties, PR, PC, AppInherit-hierarchies, AppRelationships,
Appmappings, Rules>.
The detailed descriptions of AO constituent elements are
similar to the DO and are not repeated here.
Similarly, the equivalence relationship on the
AppConcepts can be defined according to the Appmapping.
Definition 9 a≡appb iff Appmappings (a) = Appmappings (b)= t, a, b  AppConcepts, t  Concepts. This equivalence
relationship can be recorded as [t]≡app.

C. Definition and Ontology Representation of SREPSREP is a summary of experience of errors in requirements

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020engineering. Therefore, the definition of SREP can be given
with reference to the definition of software error pattern [33].
Definition 10 The SREP refers to the error produced in thesoftware requirement development stage, which occurs
repeatedly in a specific error lifetime scenario, spreads in thesubsequent design and implementation, and may cause asystem (component) to fail to perform the expected function
or affect the maintainability of the system. Such errors are
general and common in a specific scenario and can becorrected by various means.
The definition shows that the core components of an SREP
are “scenario”, “error-manifestation” and “solution”. In
addition, “severity” should also be included.
Definition 11 The definition of SREP ontology (SREPO) is:
SREPO = <Concepts, Object properties, Data properties, PR,
PC, Inherit-hierarchies, Relationships, Rules, Instances>.
The SREPO concept space is shown in Fig. 7. The concept
classes marked with an “*” are non-terminal concept classes,
and the rest are terminal concept classes.
Errormanifestation

Objective*

System*Resource

Agent*

Component*

DynamicInteraction*

Expandability

Task*

Constraint*

Action*State*

PreConstraint

PostConstraint

DomainConstraint

OriginalState

EffectState

Environment*
is_aPeopleCompletenessConciseness

EventFlow

StaticEntity*

Criteria
Consistency

Solution

SREP

Severity

Meanwhile, users can also select an optimal ontology
between different ontology systems. It should be noted that,
this paper takes an unmanned aerial vehicles (UAV) FC
systems SRE ontology in the application layer of RKMOF ofthe avionics systems for ontology evaluation.
1 Ontology validation
1) Ontology content evaluation
This method checks the content of the ontology based on
the following main criteria [15-17, 31]: consistency,
completeness, accuracy, conciseness, expandability, andsensitiveness. The criteria and their compatibility to UAV FC
systems SRE ontology are shown in Table II.

Accuracy

Scenario

SensitivenessisPartOf
describes

Fig. 7. SREPO concept space

This paper improves the ontology representation of thedomain-related requirements error pattern proposed in [33],acquires a new ontology representation of requirements error
pattern, and integrates it into the RKMOF. As a summary ofexperience, the role of SREP is to restrict new requirements
elicitation activities in the form of rules. Since programmingin logic (Prolog) can be used not only to represent factual
knowledge such as states, concepts, etc., but also to represent
the causality of things, i.e., rules, this paper uses the Prolog torepresent the SREP. Then, this paper incorporates the SREP
into a rule base.
This paper first builds a fact library based on the RKMOF,
and then converts the Web Ontology Language (OWL)
instances in the fact library into the facts that conform to
Prolog syntax. The rules in the rule base are written in Prolog
grammar, and no further conversions are required. The
concepts and predicates used by these rules should also
follow the definitions in the RKMOF, and maintain
conceptual consistency with the fact library.

TABLE IIONTOLOGY CONCEPT EVALUATION
Satisfaction
Yes, since no contradictory knowledge can be inferredfrom all definitions and axioms. Also, reasoner shows noerrors.
Yes, it is complete based on specifications determined in
the design phase of the ontology.
Yes, the activity of interviewing experts has been
conducted. And experts participate in the ontology
construction process.
Yes, the ontology is concise since does not contain any
unnecessary concepts.
Yes, it is easily expanded since there is no need to makebig changes in a set of well-defined definitions when
adding new definitions.
The ontology is not sensitive since small changes indefinition will not alter a set of well-defined contents.

2) Competency questions evaluation
The competency questions for determining the scope anddesigning purposes of UAV FC systems SRE ontology areused here for the evaluation. Each competency question is
answered and justiﬁed based on the UAV FC systems SRE
ontology components. Answers and justiﬁcations are shownin Table III. Competency questions ensure that the ontology
implementation fulﬁlls the scope of UAV FC systems SRE
ontology.

CQ#
CQ1

D. Ontology Evaluation
The development of ontology description languages andtools aids developers in building ontologies according to
specific applications. However, due to the complexity of
application semantics, ensuring ontology quality remains animportant issue. In addition, the widespread use of ontologies
has led to an explosive growth in the number of ontologies on
the Internet. Ontologies enable reuse, but different ontologies
have notable differences in domain coverage,
comprehensibility, and accuracy. Thus, it is difficult for users
to grasp ontology features as a whole and understand theirapplication. Based on the above two points, it is necessary toevaluate ontology quality. According to ontology evaluation
results, developers can reconstruct an ontology to optimize its
structure, thereby creating high-quality ontologies.

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CQ2

TABLE IIICOMPETENCY QUESTION ANSWERS AND JUSTIFICATIONS
CQJustification
Why build aEngineering experience has shown that theUAV FCavionics systems software usually has thesystems SRE
characteristics of complex structure, wide
ontology?
function coverage, high reliability and highsafety. These characteristics have led to theincreasing scale and complexity of softwaresystems, and the increasing variety of research& development (R&D) personnel. This further
intensify the knowledge-intensive trend of
software development, make the software
requirements elicitation activities more
complicated, and also cause the issues ofambiguity, incompleteness, and inconsistency.
An ontology is an explicit specification of aconceptualization. Therefore, introducing theontology method into the SRE process is aneffective way to solve the above problems. By
adopting this ontology method, the requirement
knowledge is expressed as the ontology concept
and association; therefore, it is clear, complete,and consistent and is conducive to the sharing
and reuse of knowledge. In addition, it greatly
promotes the quality of UAV FC systems SRE
and SRSs.
Who needs thisThe
stakeholders,
includingsystemontology?
analysts/software developers, customers/users
and domain experts, all need this ontology. The
purpose of an ontology is to use domain

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

CQ3

Who isresponsible formanaging andmaintaining thisontology?

CQ4

What are themain contents inthis ontology?

CQ5

CQ6

When is thisontologyneeded?
How is thisontology
managed andmaintained?

analysis and experience to model domain
knowledge as a shareable knowledge
framework to help stakeholders understand theapplication domain and define requirements.
Under this framework, system analysts andsoftware developers can understand therequirements accurately, and the heterogeneity
brought about by multiple viewpoints andparadigms can be minimized. Moreover, theimplementation of a higher quality UAV FC
systems SRE activity based on this ontology
benefits the development of UAV FC systems
software and the entire system, helps to obtainproducts with fewer defects and higher quality,and enables developers to shorten thedevelopment cycle and reduce costs in a highly
competitive market. Meanwhile, because thedomain knowledge possessed by customers,
users, and domain experts plays an importantrole in high-quality SRE, this ontology can help
them accurately express requirements thatdevelopers can understand in accordance with
software development guidelines and improvesystem development quality and efficiency.
This ontology exists as a part of UAV FC
systems software and system development.
Software and system developers are responsible
for the management and maintenance of
ontologies. In addition, there is a dedicatedteam in the development team responsible forthe initial ontology development andsubsequent ontology management andmaintenance.
Contents include the GO, DO, and AO level
ontology-related
concepts,
properties,
hierarchies, and relationships. These are
specifically based on: 1) the UAV FC systems
software-related concepts and relationships, 2)
the SREP-related concepts and relationships.
This ontology is needed when developing thesame or similar UAV FC software system,
which can realize the sharing and reuse of the
ontology.
A dedicated team is responsible for the
management and maintenance of the ontology.
According to actual usage and user feedback,the ontology is continuously updated, including
adding new necessary content and deleting
outdated content. In addition, logs are used torecord the management and maintenanceprocess.

2 Ontology verification
1) Ontology taxonomy evaluation
The taxonomy evaluation method is used for checking thetaxonomy of the ontology based on main criteria mentionedin [34]. These criteria and their compatibility to UAV FC
systems SRE ontology are shown in Table IV.
TABLE IVONTOLOGY TAXONOMY EVALUATION
Criteria
Satisfaction
Inconsistency
Circularity errors
No error, reasoner shows noerrorsPartition errors
No error, reasoner shows noerrorsSemantic errors
No error, reasoner shows noerrorsIncompleteness
Incomplete
No error, all concepts of theconceptknowledge specified in theclassification
design phase are included.
Partition errors
No error, because all the
instances of the base classes
belong to the sub classes.
Redundancy
GrammaticalNo error, each class has only
redundancy
one definition.
Identical formal No error, there is no two

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definition of someclasses
Identical formal
definition of someinstancesclasses with the samedefinition.
No error, in domain ontology
there are no instances2)Improved FOCA evaluation⚫ FOCA method and its shortcomings
FOCA is a method that can be used for evaluating thequality of an ontology. FOCA includes determining the typeof ontology, a questionnaire to evaluate the components, aframework to follow, and a statistical model that calculates
the quality of the ontology. FOCA goes through threeverification steps, as shown in Fig. 8. [7]. Ontology typeverification defines two types of ontology: a domain or task
ontology and an application ontology. Questions verification
possesses questions to serve the goals. Quality verification
calculates the ontology quality.
The real world is a complex network system, and an
ontology is the result of modeling the real world. Therefore,
the ontology can also be regarded as a complex network. It isan abstract network model of the real world; however, for the
purpose of knowledge sharing and reuse, the network is
expressed as a specific network structure using semantic
standards such as RDF / OWL and other languages. Newman,a famous scholar of complex networks, argued thatmodularity should be one of the basic characteristics of(1)

Ontology Type Verification

Type 1(A Domain or Task Ontology)(2)Type 2(Application Ontology)

Questions Verification
Question (Q)Role (G)Criterion (M)Question (Q)Criterion (M)Question (Q)(3)

X5

Quality Verification
Total Quality

Partial Quality

Fig. 8. FOCA methodcomplex networks [35]. Therefore, the evaluation of
ontology quality should also consider ontology modularity.
The notion of an ontology module relates to the design ofontology composed of well-defined components that can be
managed and reused independently. An ontology module isused to reduce ontology complexity and to increase
understandability, testability, maintainability, and reliability.
The FOCA evaluation criteria do not include a quantitativeevaluation of ontology modularity. This paper adds thequantitative indicators reflecting the degree of ontology
modularization to the FOCA to evaluate the ontology quality.
⚫ Ontology module and directed acyclic graph
An ontology module is a collection of the closely related
concepts, relationships, and axioms reflecting a common
theme. The ontology module is divided or extracted from an
original ontology and is part of the original ontology (asub-ontology) [8]. The sub-ontology needs to meet certain
indicators or conditions to satisfy specific applications. The
modularization of an ontology helps reduce complexity andenhances comprehensibility, testability, maintainability, andreliability. The structure of an ontology module is consistent
with the structure of a software module in the software
engineering and should meet the principle of “high cohesion,
low coupling”.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

Ontology classes are arranged in a hierarchy from thegeneral (high in the hierarchy) to the specific (low in thehierarchy). Despite the hierarchical organisation, mostontologies are not simple trees. Rather, they are structured as
directed acyclic graphs (DAGs). This is because it is possible
for classes to have multiple parents in the classification
hierarchy, and furthermore ontologies include additional
types of relationships between entities other than hierarchicalclassification (which itself is represented by is_a relations).
All relations are directed and care must be taken by the
ontology editors to ensure that the overall structure of the
ontology does not contain cycles, as illustrated in Fig. 9. [37].

Fig. 9. (A) simple hierarchical tree, (B) DAG, (C) graph that contains a cycle,
indicated in red.

⚫ Cohesion and coupling of ontology
Cohesion and coupling are two important indicators toreflect the ontology modularity. Therefore, this paper selects
these two indicators to reflect the ontology modularity, andthen takes them as an aspect of ontology quality evaluation.
The structure of an ontology is consistent with
object-oriented structure and should also meet the principleof “high cohesion, low coupling”. The cohesion describes thedegree to which multiple concepts are combined into one
module. It reflects the closeness of concepts. The higher thecohesion of an ontology module is, the closer relationshipbetween the concepts and the more similar semantics of theconcepts in the ontology module. This benefits theunderstanding, reuse, and maintenance of an ontology.
Ontology module coupling can be regarded as the degree ofcorrelation between ontology modules. The classes in one
module are closely related to the classes in other modules,
then the first ontology module has a high coupling value. The
stronger the ontology module coupling is, the harder tounderstand, change, and correct the ontology. And it
increases the complexity of the systems using the ontology.
In short, the cohesion reflects the closeness of therelationship between the concepts in an ontology module, andthe coupling reflects the closeness of the connection between
the ontology modules. Both reflect the characteristics ofontology structure and semantics, and should also be fully
considered during the ontology quality evaluation and added
to the FOCA method to achieve its improvement.
⚫ Evaluation metrics of cohesion and coupling
In this paper, the metrics of cohesion Coh [8], and AOC[38], and the metrics of coupling NSHR [8], and NSNR [8]are used.
❖ Coh (M) is the ratio of the sum of the strength of relation to
the number of all possible relations in a module M.
sr (ci , c j )


 c
|
M
| (| M | −1)
Coh( M ) =  i M c j M
2


1

1


sr (ci , c j ) =  dis tan ce(ci , c j )
0


if

if | M | 1

(1)otherwise

relations exist between ci and c j (2)otherwise

where,
distance (ci,cj) = a minimal path length between ci and cj. For
class ci  M, cj  M, M: Module, | |: cardinality. If there is no1272

concept in M, then Coh(M)=0. If there is only one concept in
M, Coh(M)=1 because this concept does not depend on any
other concept, and it is the closest structure. The range ofCoh(M) is [0, 1] because the largest relation number in a
DAG is the number of edges of the full connected graph.
❖ AOC is the original ontology cohesion.
n Coh(M )AOC = i =1

i

n

(3)

where,
n is the number of modules partitioned by the original
ontology, and Coh(Mi) is the cohesion of the ontology
module Mi.
❖ An ontology has two types of relationships: hierarchicalrelationships and non-hierarchical relationships. The
hierarchical relationships are stronger than otherrelationships, because the classes associated through the
hierarchical relationships share and inherit more information
between classes. This paper measures the coupling based on
NSHR and NSNR. Using the proposed ontology coupling
metrics, the consistency between modules and their original
ontology should be considered. To be consistent with theoriginal ontology, modules should preserve the classes andaxioms of their original ontology. Thus, an ontology should
be modularized to lessen the number of disconnected
relations.
NSHR is the ratio of the number of hierarchical relations
that are disconnected after modularization to the total numberof relations. More disconnected hierarchical relations meanthat more information about the hierarchical relation is lost.
NSHR can be formulated as follows:(4)
NSHR ( M ) = nshr (ci , c j )
ci M

c j O − M

O is the original ontology, M is the module, and – is thedifference operation. nshr(ci, cj) is the number of hierarchicalrelations between ci and cj that is disconnected aftermodularization.
NSNR is the ratio of the number of disconnected
non-hierarchical relations to the total number of relations
after ontology modularization. NSNR can be formulated asfollows:(5)
NSNR ( M ) = 
nsnr (ci , c j )
ci M

c j O − M

O is the original ontology, M is the module, and – is thedifference operation. nsnr(ci, cj) is the number of
non-hierarchical relations between ci and cj that is
disconnected after modularization.
In the following example, an original ontology O (a UAV
FC systems SRE ontology) is partitioned into five modules,
M1, M2, M3, M4, and M5, as shown in Fig. 10. This figure
explicitly shows the disconnected relations between modules
for explanation.
❖ r N S H R (M) is the ratio of the NSHR of ontology
module M to the total number of hierarchical relationships
in the corresponding part of original ontology. The
calculation formula is as follows:
NSHR( M )(6)r(M ) =
NSHR

K

where,
K is the total number of hierarchical relationships in thecorresponding part of original ontology.
rNSNR (M) is the ratio of the NSNR of ontology module M
to the total number of non-hierarchical relationships in the

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

corresponding part of original ontology. The calculation
formula is as follows:
NSNR( M )(7)
rNSNR ( M ) =L
where,
L is the total number of non-hierarchical relationships in thecorresponding part of original ontology.
❖ Averr represents the average value of rNSHR (M i ) of
NSHR

all ontology modules after the division of original
ontology.
represents the average value of rNSNR (M i ) of
Averr

1273

The calculation formula is as follows:r(M )AverrNSHR = NSHR inrNSNR ( M i )AverrNSNR =
n

(8)
(9)

where,
n is the number of modules after the division of original
ontology.
❖ Scoupling is the comprehensive coupling value of a module.
The calculation formula is as follows:

NSNR

all ontology modules after the division of original ontology.

FunctionalError

M1
isPartOf
isPartOf
Scenario

SREP

InterfaceError

MaintenanceError

ErrorManifestation

PerformaceError

Solution

VersionControlError

describes

DocumentationError

Thing
M2

isPartOf
DynamicInteraction

EventFlow
PreConstraint

isPartOf

M3

PostConstraint

StaticEntity

DomainConstraint
ActionMonitorState

Constraint

produces

Perform

Control

SafetyError

EnvironmentalError

isPartOf

EffectState

Task

OriginalState

needs

Resource

has

M5

Objective
Peopleinteracts

triggers

Component

SystemSensorThrottleLever
Spout

ExpEn

Agent

Environment
FCMS

ElectricalSystem

FireControlSystem

M4

MeasurementControlSystem

ImpEn
StrEn
GeoEn

CliEn
EMEn

isPartOf
GeoLocation

isPartOf
SpatEn

APISoftware

BottomSoftware
AgentSensor
ApplicSoftware
isPartOf
isPartOf
isPartOf
FCMComputer isPartOf
AileronControl
FCMSSoftware
isPartOf
RudderPowerSystem

TaskEquipment

ServoSystem
Elevator

isPartOf

TerrainTaskPlanningSynthesized
TelemetryInformation
Display

HeightVertEnisPartOf

InerRefSysCd

Plateau

Basin

StraitRemoteControl

EngineThrottle
FrontRudder

HeightUnit
RefSystem

Wheel

GroundControl
DisplayTerminal

FlyingStatus
Monitor

Ocean

GeoRefSysCd

Fig. 10. Ontology modules M1~M5

Scoupling=  * Averr

NSHR+  * Averr

where,
 represents the weight of Averr

NSHR

weight of Averr

NSNR

NSNR

(10)

,  represents the,  +  = 1 . This paper argues that theimportance of hierarchical relationships is greater than thatof non-hierarchical relationships. Generally speaking,   . The values of  and  can be set according toexperience.
⚫ Calculation of values of cohesion and coupling
In this paper, the values of cohesion and coupling arecalculated based on the ontology module division shown in
Fig. 10.
❖ Calculation of cohesion value
Using (1) for M1 to M5, Coh(M1)=0.14, Coh(M2)=0.23,
Coh(M3)=0.83,
Coh(M4)=0.14,
Coh(M5)=0.14.
Substituting these values into (3), AOC=0.296. It should be
noted that the AOC value is low, indicating that therelationship between the concepts in the ontology is not very
close. The main reason for the above results is that thehierarchical structure of the conceptual classes in the
ontology is not sufficiently complete; meanwhile, there arelimited connections between the concepts other than thehierarchical relationships.
❖ Calculation of coupling value
Using (4) to (9) in turn for M1 to M5, the values of NSHR,
NSHR, rNSHR(M), rNSNR(M), Averr, and Averr
of
NSHR

NSNR

each module can be obtained as Table V. Let  =0.7,

 =0.3,

Substituting the values of

AverrNSHR and

AverrNSNR into (10), Scoupling=0.1706. According toexperience, this value is less than 0.2, indicating that the
coupling is not high, and the consistency between the
ontology modules and the original ontology is good. This
also shows that the correlation between ontology modules is
not high, and the connection is loose. The concepts of each
module focus on its own theme; therefore, it is easier tounderstand and reuse the ontology module.

No.
M1
M2
M3
M4
M5

TABLE VCOUPLING METRICS FOR M1 TO M5
NSHR
rNSHR(M)
NSNR
1
0.077
1
1
0.083
2
2
0.4
1
1
0.04
1
1
0.04
1

rNSNR(M)
0.25
0.4
0.33
0.2
0.17

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

AverrNSHR

0.128

AverrNSNR

0.27⚫ Improved FOCA evaluation method
This paper uses the improved FOCA to evaluate theontology quality.
❖ Ontology type verification
FOCA defines two types of ontology, a domain or task
ontology and an application ontology. The UAV FC systems
SRE ontology is an AO (type 2); therefore, a type 2 ontology
should answer Q4 instead of Q5 for Goal 2 shown in Table
VI.
❖ Questions verification

Goal
1.Check if the ontology
complies with Substitute.

2.Check if the ontology
complies Ontological
Commitments.
3.Check if the ontology
complies with Intelligent
Reasoning.
4.Check if the ontology
complies Efficient
Computation.

5.Check if the ontology
complies with Human
Expression.

Question

Does the document define the ontology objective?
Does the document define the ontology stakeholders?
Does the document define the use of scenarios?
Q2.Were the competency questions answered?
Q3.Did the ontology reuse other ontologies?
Q4.Did the ontology impose a minimal ontological commitment?（application ontology）
Q5.Did the ontology impose a maximum ontological commitment? （domain/task ontology）
Q6.Are the ontology properties coherent with the domain?
Q7.Was the ontology cohesion metric value acquired?
Q8.Was the ontology coupling metric value acquired?
Q9.Are there contradictory axioms?
Q1.Were the competency
questions defined?

Q10.Are there redundant axioms?
Q11.Did the reasoner bring modelling errors?
Q12.Did the reasoner perform quickly?
Are the written terms in the documentation the same as themodelling?
Does the documentation explain what each term is and does it
justify each detail of modelling?
Q14.Were the concepts well written?
Q15.Are there annotations in the ontology that show the definitions of the concepts?
Q13.Is the documentationconsistent with modelling?

TABLE VIIICOMPREHENSIVE COUPLING METRICS VALUES AND CORRESPONDINGSCORESN
N<0.250.25≤N<0.5
0.5≤N<0.75
0.75≤N<1
score
100
75
50
25


i =

When a cohesion metric and a coupling metric are added,
it needs to answer the 13 questions in Table VI (should
answer Q4 instead of Q5). These answers should then bescored by the evaluator. The set of questions corresponding
to Goal 2 is expanded by adding two questions of “Was the
ontology cohesion metric value acquired?” and “Was the
ontology coupling metric value acquired?” The scores partly
refer to the experimental data in [38]. The cohesion metricvalues and corresponding scores are shown in Table VII.
The coupling metric values and corresponding scores areshown in Table VIII. These 13 questions serve five goals.
The goal/question/metric (GQM) approach for the improved
FOCA is shown in Table VI.

TABLE VIGQM FOR IMPROVED FOCATABLE VIICOHESION METRICS VALUES AND CORRESPONDING SCORES
M
M<0.250.25≤M<0.5
0.5≤M<0.75
0.75≤M<1
score
25
50
75
100

1274

MetricCompletenessCompleteness
Adaptability
ConcisenessConcisenessConsistency
Cohesion
Coupling
Consistency
Conciseness
Computational
efficiency
Computational
efficiency
Clarity

Clarity
Clarity❖ Quality verification
Ontology quality can be calculated in two ways: total
quality and partial quality. This paper uses the total quality
verification because most goals are considered in theevaluation. Total quality verification is calculated using beta
regression models, proposed by Ferrari [39], and shown in(11).

exp{−0.44 + 0.03(CovS  Sb)i + 0.02(CovC  Co)i + 0.01(CovR  Re)i + 0.02(CovCp  Cp)i − 0.66 LExpi − 25(0.1 Nl )i } (11)
1 + exp{−0.44 + 0.03(CovS  Sb)i + 0.02(CovC  Co)i + 0.01(CovR  Re)i + 0.02(CovCp  Cp)i − 0.66 LExpi − 25(0.1 Nl )i }where,CovS is the mean of the grades from Goal 1;
CovC is the mean of the grades from Goal 2;
CovR is the mean of the grades from Goal 3;
CovCp is the mean of the grades from Goal 4;
LExp is the variable for evaluator experience, with 1 being
very experienced and 0 being not experienced at all;
Nl is 1 only if some Goal is impossible for the evaluator toanswer all the questions;
Sb=1, Co=1, Re=1, Cp=1, because the total quality
considers all the roles.
By Substituting these values into (11), the following result
is obtained,

exp{−0.44 + 0.03  (100 1) + 0.02  (87.5 1) +0.01 (100 1) + 0.02  (87.5 1) − 0.66 1 − 25  (0.11)}
i == 0.9802
1 + exp{−0.44 + 0.03  (100 1) + 0.02  (87.5 1) +0.01 (100 1) + 0.02  (87.5 1) − 0.66 1 − 25  (0.11)}
The total quality of the ontology is 0.9802, which is near
to 1. This shows the high quality of the UAV FC systems
SRE ontology.
V. RKMOF-BASED REQUIREMENTS

ELICITATION APPROACH

In addition to the basic knowledge required for the SRE,
the avionics systems RKMOF established in this paper also
contains the SREP knowledge, which greatly enriches theprior information of SRE; therefore, the knowledge isrelatively complete. The ontology quality evaluation result

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020shows that the UAV FC systems SRE ontology has highquality.
Fig. 11. shows the schematic diagram of the
ontology-based SRE method. It can be seen that each main
step of elicitation process has two branches: If there is no
existing ontology, an ontology is first established; if there isan existing ontology, it is reused and tailored / expanded
according to actual needs. By building ontologies
layer-by-layer, the corresponding concepts and relationships
are obtained. A DRM can be obtained through the domain
analysis on a DO and an ARM can be obtained through theapplication analysis on the DRM and an AO. The following
are the specific steps of the ontology-based SRE method.
No

No

No
Start
Is there a DO?
Is there a GO?

Reuse the existingGO

Construct a GO

Is there a TO?

Is there a DO?

Construct a DO

Reuse theexisting DO

Construct a TO

Reuse theexisting TO

Tailor and extend
the existing GO

Is there a TO?

No

Tailor and extend theexisting DO
Domain analysis

Is there an AO?

Construct an AO

DRM

Reuse the existing
AO

Tailor and extend theexisting AO

Is there a DO?

Tailor and extend theexisting TO

Is there an AO?
Application
analysis

ARM

End

Fig. 11. Schematic diagram of SRE method based on multi-ontology

A. Preliminary stage
The accumulation of experience is crucial to the
ontology-based SRE method. Therefore, before formal SRE,
it is necessary for testers and domain experts to jointly build
a SREP library as shown in Fig. 12.
Preliminary stage
Construct a SREP library1) Collect and summarize SREPs based on developing and testing experience;2) Use ontology language to represent the SREPs and store them in the library.

Fig. 12. Specific steps of preliminary stage

B. Implementation stage
Fig. 13 shows the specific steps of implementation stageof the ontology-based SRE method.

1275

The steps 1 to 10 in Fig. 13 are introduced earlier;
however, it is insufficient for the actual application to build
an AO only, because its granularity cannot satisfy therequirements of application software specification, and isindirect. Unified modeling language (UML) has been widelyused and has become a standard. Therefore, UML class
diagrams, use case diagrams and activity diagrams are usedin this paper to assist AO modeling and application
requirements eliciting to obtain detailed application
software specification.
1 Application of class diagrams
An AO can be constructed by eliciting and classifying
the concepts and relationships in an application domain.
Because an AO reflects the static knowledge of a system, theUML class diagrams can be used in the process of AO
construction. Fig. 14. shows a portion of concept classes(hierarchies) of the UAV FC systems SRE ontology.
2 Application of use case diagrams
An ARM can be established based on a DRM and an AO.
Because an ARM reflects the dynamic knowledge of asystem, the UML use case diagrams and activity diagrams
can be used in the process of ARM construction. The
specific steps are:1) Determine the boundaries of use cases;2) Determine
scenarios,
including:preconditions,postconditions and steps;
3) Use the use case diagrams and activity diagrams torepresent the use cases.
The use case diagrams reflect the system functions thatusers can observe; therefore, they are relatively stable. An
example of a use case diagram is shown in Fig. 15.
3 Application of activity diagrams
The activity diagrams can be used to model the workflow
between different components, and describe the conditionsof use cases and system states; therefore, they are variable.
An example of an activity diagram is shown in Fig. 16.
As shown in Fig. 16, an alternative path is represented bya diamond. The activity diagrams can represent parallel
expressions; therefore, the same actions and control flows
can be represented in the RETOs. The RETOs describe theobjectives, inference steps and control flows of requirements
elicitation activities. Therefore, the activity diagrams can betransformed into the RETOs. UAV FC systems softwarecommunication link interruption processing RETO is shownin Fig. 17.

Implementation stage

1 Identify the objectives and boundaries of a system;
2 Construct a GO;
3 Collect user needs and expectations;
4 Identify concepts and relationships by requirements analysts:
1) Familiar with a domain;2) Based on the GO, collect and classify domain concepts and relationships from domain experts and users according to mapping relationships;
3) Complement and optimize domain knowledge according to the existing documents, industry standards and software systems;
5 Create a TO by the domain experts:
1) Identify the objectives and inputs/outputs a PSM achieves;2) Describe inference steps, the control structure between the inference steps, the data flow and knowledge flow between the inference steps;
3) Establish a TO requirements library, including the static domain knowledge required by the PSM and the dynamic knowledge that constitutes the inference step s;
6 Construct a DO by the domain experts and the requirements analysts;
7 Construct a DRM based on the TO and the DO;
8 Refine initial requirements:
1) Detect requirements missing errors and feasibility errors;
2) Add the missing requirements items;
3) Remove the unrealistic contents according to the feasibility errors;
9 Create an AO:
Based on the GO and the DO, collect and classify application domain concepts and relationships according to mapping relationships. Unified modeling language (UML) class diagrams can be used in the AO
construction process;10 Evaluate the quality of the AO;
11 Based on the DRM and the AO, establish an ARM. Use cases can be used in the above process;
12 Determine the priority of the requirements items;
13 Write requirements specification;
14 Verify application requirements.

Fig. 13. Specific steps of implementation stage

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

1276

Fig. 14. Portion of concept classes (hierarchies) of UAV FC systems SRE ontology
The boundary of system
Destroying

Flying modemanagement

Parachuting

Terminating flying

UAV FC systems
software<<include>><<include>>Pre-flightswitch instruction<<include>>Fig. 15. Example of use case diagram of UAV FC systems software
Groundtelecontrol station

Wirelessteleequipment
transmits information
to UAV FC

UAV FC systems
softwarejudges ascommunication linkinterruption

UAV FC systems
softwaremeasures thebarometricheightUAV FC systems
softwareclimbs to
2000m

keeps rightcircleflies in a stackof 2000m

Fig. 16. Example of activity diagram of UAV FC systems software
Ontology name: RETO
Type: TO
{ID: (omitted)
Requirement-Elicitation Task: = <Task ID, (A UAV is in flight) && (Wireless teleequipment does notreceive ground telecontrol data in 10 mins)>
Requirement-Elicitation Task-PSM: = < Competence, Operational specification, Requirements >Competence: = <(UAV FC systems software judges the system to be communication link interruption .) && (TheUAV outputs the height and attitude information in normal condition.) && ( The UAV obtains the sequence ofactions of UAV FC system in corresponding condition.)>Operational specification: = < Inference Steps and Control Flow between the Inference Steps:{
If (Wireless teleequipment does not receive ground telecontrol data in 10 mins to cause)
UAV FC systems software judges the system to be communication link interruption,
UAV FC systems software determines the state of UAV,
If (barometric height<2000m)
UAV FC systems software determines the height of climbing,Else
UAV FC systems software determines to hold UAV flying in a stack && keep right circle,
Return this requirements item}
Requirements: = < ConceptsDomConcepts, RelationsDomRelations, RulesDomrule >Requirement-Elicitation Taskmap: {(Requirement-Elicitation Task ConceptsDomConcepts)
Concepts}}

Fig. 17. UAV FC systems software communication link interruption
processing RETO

4 Application of SREP
The use of several types of UML diagrams in normal
scenarios are introduced earlier; however, abnormal
scenarios also affect systems in practical applications.

Since an SREP library is constructed in the preliminary
stage, which contains many historical error data; therefore,
the SREP can be used to study the SRE in abnormal
scenarios.
1) Map an SREP from the GO layer to the AO layer.
Putting the SREP in the GO layer is too abstract to use;
therefore, it is necessary to map it to the AO layer. The
specific steps are based on the inverse mapping ofAppmappings in the AO definition.
2) Generate the use cases corresponding to thenon-functional requirements in abnormal scenarios basedon the SREP.
The following takes the safety requirements of
non-functional requirements as an example to illustrate thesteps of use cases development:⚫ Step a): The concerns of software requirements are
divided into general concerns and cross-cutting concerns.
This paper treats functional requirements as the general
concerns and the non-functional requirements as the
cross-cutting concerns.
⚫ Step b): All cross-cutting concerns called by eachgeneral concern are confirmed.
⚫ Step c): UML use case modeling technology is used toobtain the functional requirements according to the step 2)
in 2. This paper uses Rational Rose, a UML modeling tool,
to describe the use cases based on the scenarios, including
the preconditions, the postconditions, and the steps. Then,the scenarios are further divided into the general scenarios
corresponding to the functional requirements and thecross-cutting
scenarios
corresponding
tothenon-functional requirements (the safety requirements).
A scenario in an activity diagram is defined as:

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

Definition 12 A scenario in an activity diagram is asequence of events describing agent actions, the changes
in the state of environment, and the interactions between
the agents and the environment.
The description template of a scenario segment isshown in Fig. 18.
[Name of a secondary scenario][object1, object2,……]
Event[occurrence place]
Condition [occurrence condition]{……
Sequence of events .
……
}Fig. 18. Description template of scenario segment⚫ Step d): Rational Rose is used to build the cross-cutting
scenarios, and further generate a set of cross-cutting use
cases based on the SREP library.
⚫ Step e) The step b) is executed iteratively, and thecross-cutting use cases corresponding to the cross-cutting
concerns called by each general concern are integrated into
the general scenario corresponding to the functional
requirements in turn according to the calling relationships.
An example of use case development is shown in Fig.
19. according to the Step a) to e).

VI. CASE STUDIESIn this paper, a part of a UAV FC system is selected forontology modeling. The effectiveness of the
ontology-based SRE method is verified by a comparative
experiment. Since several continuous versions of thesoftware are developed after the ontology modeling, theresearch selects two continuous versions of the softwareand adopts a conventional method for the requirements
elicitation of a version 3.3.x; after a defined period, therequirements of a version 3.3.(x+1) are elicited based on
the ontology-based method this paper proposes. Table IX
records the detected SREP error-manifestations andnumber distributions of these two SRSs by a requirement
inspection. The same group of inspectors is used to
conduct the comparative experiment. SRS I is developed
based on the conventional method, and SRS II isdeveloped based on the ontology-based method proposedin this paper.
The results that the total number of errors in SRS I ismuch higher than in SRS II. In addition, the severity of theerrors detected in SRS I is higher, and they occur in moresignificant error types such as functional errors, interface
errors, safety errors, and environmental errors. The directcause of the above results can be initially identified as SRS
II using the ontology-based method; SRS I uses theconventional method.
Intuitively, because the ontology is a complete set of
domain knowledge, the quality information of SRS can beobtained indirectly by considering the correspondence
between the SRS and ontology element. This paper
evaluated the quality of SRS based on the metrics in [40].
The quality metric results of the SRS I and SRS II are
shown in Table X.

Error-manifestations

1277

SRS Ⅰ

SRS Ⅱ

C

I

A

Tt

C

I

A

Tt

Functional errors

4

1

1

6

0

1

1

2

Performance errors

0

0

1

1

0

0

1

1

Interface errors
3
6
0
9
1
2
0
3
Safety errors
4
1
0
5
1
0
0
1
Environmental
2
2
0
4
0
0
0
0
errors
Maintenance errors
0
1
1
2
0
0
2
2
Documentation
0
3
4
7
0
2
4
6
errors
Ts
13 14
7
2
5
8
T
34
15
Notes: C—“critical”, I—“important”, A—“average”. Tt—“total numberof errors in each type”, Ts—“total number of errors in each severity”, T—“total number of errors”.
TABLE XQUALITY METRIC RESULTS OF SRS I AND SRS II

Metrics

Correctness

SRS I

23/29=79.3%

SRS II

28/29=96.6%

From the results, the difference between SRS I and SRS
II in “Correctness” is more obvious. An ontology is asemantic basis for building a specific problem domain.
Ideally, all requirements items should be able to find thecorresponding elements in the ontology. (the number of
items that can be mapped to the ontology / the total numberof requirements items) can reflect the proportion of the
mapped elements. The higher the ratio is, the higher the
SRS quality. This ratio of SRS I to SRS II is significantly
lower. This shows that some of the requirements items of
SRS I are not included in the ontology library, implying
nonconformity with the actual application. This fact also
explains the results of the requirement inspection in Table
IX. Therefore, the requirement knowledge ontology has amajor impact on the entire requirement development
process. In general, the quality of the SRS obtained basedon the proposed ontology is higher than the quality of the
SRS obtained based on the conventional method.
Therefore, the ontology elements, i.e., the knowledge
elements, should be fully integrated in the early stage ofthe requirement development process.
VII. CONCLUSIONThe RKMOF proposed in this paper fully considered theneeds of complete knowledge basis for requirements
elicitation activities, adopted various ontologies to reflect
all aspects of requirements knowledge, divided and stored
knowledge hierarchically, and blended static knowledge
with dynamic knowledge to make them a whole. At thesame time, the empirical knowledge such as SREP was
integrated into RKMOF in a consistent form. The
framework is universal and suitable for any avionics
system. Based on the framework, the construction of
DRMs and ARMs can be further realized to guiderequirements elicitation activities to obtain SRSs withfewer defects, thereby improving the quality of SRSs andfurther improving the reliability of software systems.

TABLE IXSREP ERROR-MANIFESTATIONS AND NUMBER DISTRIBUTIONS

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 1, January 2020

Pre-destruction scenario of UAV functional requirements item 1[A UAV pre-destroys under telecontrol] [UAV FC systems software, sensors]{
A UAV is in flightIf (((INS malfunctions) && (GPS malfunctions)) || ((ADC malfunctions) && (RA malfunctions)))
Then UAV FC systems software calculates a yaw angle
executes a pre-destruction instruction // Pre-destroys under telecontrol}

UAV FC system

Telecontrol

Destruction scenario of UAV functional requirements item 2[A UAV destroys under telecontrol] [UAV FC systems software, sensors]{
A UAV is in flightIf (((INS malfunctions) && (GPS malfunctions)) || ((ADC malfunctions) && (RA malfunctions)))
Then UAV FC systems software calculates a yaw angle
executes a destruction instruction // Destroys under telecontrol}

Altimeters

Navigation systems

UAV FC system
malfunction

Telecontrol

Navigation systems

Altimeters

malfunction

malfunction
executes the predestruction
instruction

1278

malfunction

sends a predestruction
instruction
sends adestruction
instruction

executes thedestruction
instruction

Functional requirements item 2：
Destruction (a general concern)

Functional requirements item 1：
Pre-destruction (a general concern)
calls

calls

Cross-cutting concern:
Ensure that a flight safety protection switch is triggered

UAV FC system

Telecontrol

Altimeters

Navigation systems

UAV FC system

malfunction

malfunction

Altimeters

INS
GPS

Altimeters

malfunction

sends a destruction
instruction

triggers a flight safety
protection switch

Navigation systems

Navigation systems

malfunction

sends a pre-destruction
instruction
executes the predestruction
instruction

Telecontrol

executes the destruction
instruction

triggers a flight safety
protection switch{
A UAV is in flightIf (((INS malfunctions) && (GPS malfunctions)) || ((ADC malfunctions)&&(RA malfunctions)))
UAV FC systems software calculates a yaw angle
If(UAV FC systems software sends a pre-destruction instruction) ||(UAV FC systems software sends a destruction instruction)
Then triggers a flight safety protection switch
executes the pre-destruction instruction ||executes the destruction instruction // Pre-destroys or destroys under telecontrol

ADC
RA

Then

INS: Inertial navigation system
GPS: Global positioning system
ADC: Air data computer
RA: Radio altimeter}

Fig. 19. Example of use case development[11] Haruhiko Kaiya, Motoshi Saeki, “Using Domain Ontology as Domain

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Xuan Hu was born in 1983. She received the Ph.D.
degree in aerospace science and technology from
Beihang University, China, in 2010. She is currently asenior engineer of CEPREI Laboratory. Her main
research interests include ontology modeling, software
requirement engineering, and software reliability.

Jie Liu was born in 1963. He received the B.S. degree
in computer science from Xidian University, China. He
is currently the director of the Information Security
Research Center of CEPREI Laboratory. His main
research interests include information security,
software reliability and security, and software
evaluation technology.

Copyright ⓒ 2020 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

1280

Evaluation of A Mesh Network based on LoRa
Technology
Van Dai Pham*, Victor Kisel*, Ruslan Kirichek *, **, Andrey Koucheryavy*, Alexander Shestakov** Bonch-Bruevich St. Petersburg State University of Telecommunications, Saint-Petersburg, 193232, Russia** V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russia
fam.vd@spbgut.ru, kiselviktor2009@yandex.ru, kirichek@sut.ru, akouch@mail.ru, alexandr.shestakov01@yandex.ru

Abstract—In the last decade, the Internet of Things (IoT)
technologies have brought many applications in different fields.
Many communication technologies have been developed tocommunicate everything to each other and the Internet. Besides,
the long-range power-efficient wireless technologies haveemerged, such as LoRa (Long Range), which enablestransmission over long distances with low energy consumption
and can work within p2p, star, or mesh topologies. In this paper,
we analyze a model of a mesh network based on the LoRa
technology. The model considers various parameters of nodes
and communication channels in the network. Based on asimulation model on OMNET++, a series of computer
experiments are performed to analyze the delivery latency andpacket delivery ratio (PDR) in different cases. According to theanalytical results, conclusions are drawn about the possibility ofusing a LoRa mesh network to expand the network coverageand integrate with other short-range networks.
Keyword—IoT, LoRa, mesh network, routing method,
latency, packet delivery ratio

I. INTRODUCTIONNOWADAYS, info-telecommunication technology is one of

the main infrastructures of any country or company. The
advent of the Internet has helped change the approach to———————————————————————
Manuscript received on January 17, 2020. This work was supported withthe grant from the President of the Russian Federation for state support ofleading scientific schools of the Russian Federation according to theresearch project SS-2604.2020.9 and it is also supported by a follow-up of
the invited journal the accepted & presented paper entitled “Analytic Modelof a Mesh Topology based on LoRa Technology” of the 22th International
Conference on Advanced Communication Technology (ICACT2020), andGrant ID is 20200385.
V. D. Pham, he is a Ph.D student in the Department of Software
Engineering and Computer Science at Bonch-Bruevich Saint-Petersburg
State University of Telecommunications, Russia. (corresponding author,
phone: +7-953-364-5468, email: fam.vd@spbgut.ru).
V. Kisel, he is a Master’s student at the Bonch-Breuvich Saint-Petersburg
State
UniversityofTelecommunications,
Russia.
(email:kiselviktor2009@yandex.ru).
R. Kirichek, he is a professor in the Software Engineering and Computer
Science at Bonch-Bruevich Saint-Petersburg State University of
Telecommunications, Russia. (email: kirichek@sut.ru).
A. Koucheryavy, he is a professor in the Department of Communication
Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia. (email: akouch@mail.ru).
Alexander Shestakov, he is a professor in the Department of Automationof Communication Enterprises at Bonch-Bruevich Saint-Petersburg State
UniversityofTelecommunications,
Russia(email:alexandr.shestakov01@yandex.ru)

information. The Internet brings information to users,
communicates people to the other, human to machine, andmachine to machine. This term is called the Internet of Things(IoT), where every device can communicate over the Internet,
as described in [1], [2]. This leads to the expectation of therevolution of Industry 4.0.
The Internet of Things has been attracting many interests inboth scientific and commercial fields [3]. Many IoT
applications have been developed as Smart Devices, Smart
Home, Smart City, Smart Factory. Besides, communication
technologies also have contributed greatly to the developmentof IoT applications. Many technologies have been proposed
to ensure the network's quality of connection while thenumber of connected devices does not stop increasing. Eachtechnology has different characteristics such as data rate,transmission distance, power consumption. Based on thosetechnologies, network models are considered for different
requirements. Low Power Wide Area Network (LPWAN) is a
networking group, which allows long-range transmission andrequires small power consumption [4]. Among LPWANs,
LoRa (Long Range) is a promising technology for IoT
applications, which do not need high-speed data rate andrequire energy saving with long-range transmission [5], [6].
Using LoRa as the physical layer, the project LoRaWAN(Long Range Wide Area Network) has been developed
widely with a large contribution by LoRa Alliance. In the
LoRaWAN, the devices are managed by Gateway or Base
Station. In this case, the star topology is used for constructing
LoRa Networks. On the other hand, the mesh topology also is
considered in different cases. In Wireless Sensor Networks(WSN), for instance, ZigBee, Z-Wave, Bluetooth Low
Energy 5.0, devices are connected in the mesh topology, eachdevice can be a router relaying the packet of the other devices
to the destination [7], [8]. The highlight difference of LoRa
respected to these technologies is the ability of long-range
transmission. This advantage can help expand the networkmodel without needing additional base stations.
Smart City is a typical IoT application, where many devices
in the city are expected to communicate with each other andsend data to the cloud server. For instance, a bus can informthe bus station directly about the destination time. The buses
and bus stations communicate in the network and exchange
information with each other. The decentralized or meshtopology is applied to manage such applications.
Moreover, the mesh topology also is used in networks such
as VANET or FANET. In a search and rescue application, asICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

can be seen in [9], [10], the UAVs (unmanned aerial vehicles)
are connected to others in a mesh topology. So, the data aretransmitted through several UAVs to the destination.
The mesh topology for the LoRa network is a solution to
expand the number of connected nodes and the network
coverage area. We propose a LoRa-based network modelusing the mesh topology for these goals in this paper. In this
topology, the routing option is an important parameter toensure the quality of service. While considering the networkmodel, the optimal route selection is found between the nodes
based on the link quality. Moreover, we consider the delivery
latency in the influence of the load intensity and the number ofrelay nodes.
The rest of the paper is organized as follows: Section 2
gives an overview of LoRa technology and its related works.
Section 3 presents a mesh network model based on LoRa
technology and a simulation model to analyze the latency
distribution and packet delivery ratio in the network.
Obtained analytical results are presented in Section 4. And theconclusion is concluded in Section 5.

II. LORA OVERVIEW
Semtech Corporation is one of the leaders developing
wireless chipsets for IoT applications. Since when thecompany launched the first LoRa chipset, the LoRa
technology has been attracting much interest in thedevelopment of IoT applications. In 2015, LoRa Alliance wasestablished to develop and support the global network
LoRaWAN. It is observed that LoRa has become a promisingtechnology at the moment.
The key parameters used to configure the LoRa radio
module are modulation method, frequency range, bandwidth(BW), spreading factor (SF), coding rate (CR), andtransmission power [5], [6]. At the LoRa physical layer, achirp spread spectrum (CSS) modulation method is used,providing an increased channel budget and better noise
immunity. In the LoRa network, there are some options tochoose the used bandwidth, such as 125, 250, and 500 kHz.
The low bandwidths make LoRa resistant to channel noise,
long-term relative frequency, Doppler effects, and fading.
Coding rate (CR). A forward error correction technique isused to increase reception sensitivity. The coding rate isdenoted as k/n, where k represents useful information, theencoder generates n output bits, i.e. n-k will be redundant bits.
Redundancy allows the receiver to detect and correct errors inmessages, as well as reduces data rate. There are coding rates
such as 4/5, 4/6, 4/7, or 4/8 used in the LoRa network.
Spreading factor (SF). In the LoRa technology there areseveral spreading factors from 7 to 12 used to expand thetransmitted bits. The SF parameter defines two values: thenumber of source bits that can be encoded for one symbol, andeach symbol can contain 2SF chips. With a higher spreading
factor, the data rate is lower, and vice versa. Each symbol isspreading in the length of 2SF chips, i.e. 2SF chips are required
to expand one symbol. The method of replacing one symbolwith several elements of information shows the influence ofthe spreading factor on the transmission speed. Therefore, if
the value of SF increases, then the data rate decreases.

1281

Bandwidth (BW). There are different values such as 125,
250, or 500 kHz used to configure LoRa radio models. Data
are propagated at a chip frequency equal to the throughput inchips per second. Hence, 125 kHz corresponds to a chip speedof 125 kilo-chips per second.
The combination of between SF, BW, and CR determines
the data rate as (1):BW
(1)
DR = SF × SF × CR
2

SF = {7,8,9,10};
where BW = {125, 250,500}, кГц; CR = {4 5, 4 6, 4 7, 4 8}.
The high BW increases the DR, but it decreases the
decoding sensitivity. The high SF increases the decoding
sensitivity but decreases the DR. Therefore, with the low BW,
and high SF, the decoding sensitivity and transmission
distance are increased, but the DR will be decreased.
Depending on the application requirements, this combinationof parameters can be configured.
Studies on LoRa technology get much attention from thephysical layer to the application deployment. In recent years,
research and development based on LoRa have gained many
prospects. In the context of Smart Cities, there are several
LoRa-based applications, such as for waste management [11],water quality monitoring [12], or street lighting.
Since LoRa has high sensitivity, tracking, or positioning
solutions were proposed in [13], [14]. It can be seen that theapplicability of LoRa is used for many IoT applications. It isrelevant to discover the other possibility of LoRa technology.
Almost LoRa-based applications are deployed in cases
required to transmit on long-distance and save energy
consumption. According to different requirements, theparameters can be set up in the LoRa network.
Thus, to use the LoRa's advantages, a mesh topology modelis analyzed for LoRa networks in the next sections.
III. ANALYTICAL MODEL
A. Network Model
In a mesh topology model, a network can be presented as aconnected graph. For instance, as shown in Figure 1, thenetwork includes device nodes and gateways connected in agraph. The device node can be either the end-device or relay
node, via which the data are relayed. The data received by therelay node will be forwarded to the next node according to the
optimal route to the destination.
Cloud Servers

Device Node
Gateway

Figure 1. Network modelICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

As shown in Figure 1, the mesh network model can be
described by a Graph G (V , E ) with a set of Vertices – V, anda set of Edges – E. The set of vertices V = {vi }, i = 1K n ,which represents the set of device nodes in the network. The
set of edges E = {eij }, j = 1K n , which represents the set ofcommunication links between the nodes in the network. Eachnode has a maximum communication radius R; the nodes cancommunicate with the others within this distance. If thecoordinates of a node are described as ( xi , yi ) , the distance
between 2 nodes i and j follows as:

dij = ( xi − x j )2 + ( yi − y j ) 2

i, j = 1K n .
Each edge between vertices is weighted by a set of linkcosts C = {cij } with i, j = 1K n , which represents thepossibility of data transmission between two nodes. A low
link cost means that this link is more suitable to exchange
information.
B. Route selection
In this case, the routing path is calculated to find the
optimal route to the destination. The link cost is denoted by
the parameter RSSI (Received Signal Strength Indication),
which can be considered the signal strength between thetransmitter and receiver. The link cost is represented as adependent function of RSSI:

cij = f ( RSSI ij ) , RSSIij ≤ threshold and i, j = 1K n
The matrix C = {cij } with i, j = 1K n, s contains thecharacteristics of all communication links of the network(weights of edges of the graph).
 c1,1 c1,2 K c1,n c1, s cc2, s 2,1 c2,1 K c2, n
C=
 M
M O M
M 


 cn,1 cn,1 K cn,n cn, s 
There
is
a
set
of
possiblepathsΩ = Pi , s = { P1 , P2 ,K, Pk } between a node i and a sink node ingraph G.
Thus, the total link cost of the path from node i to the sinknode – s (gateway) is defined as follows:j = Pi ,s −1

wi , s = c

i , j +1

represented as a queueing system, which serves the incoming
and outgoing requests, as shown in Figure 2.
In Figure 2, the incoming traffic is denoted by mean arrival
intensity – λi and is served by the node i with a mean service
intensity – µi. There is an intensity of loading the system thatis calculated by ρi =

(3)

j =1

The optimal route from node i to the sink node is computedas:Routei , s = arg min( wi , s )(4)Pi ,s ∈Ω

C. Queueing system
In the network, each node perceives and transmits data to
the other. There are incoming packets and outgoing packets
through the nodes. To evaluate the performance of this
topology, we can assume that each node in the network is

λi( Erl ) . The load factor influencesµi

serving time in the system, i.e. the delivery time will be
influenced. By considering the queueing system, the network
performance is evaluated to see the usage possibility inapplication deployment.
i-th queueing(2)

Hence, there is a communication link between 2 nodes if
the distance between them is less than R. It can be illustrated
by the following: if dij ≤ R ; ∃eij and E = {eij } with

1282

λ*
λi+1

λi

Ti
Fig. 2. A node is represented as a queueing system

When considering the M/M/1 model for each node, we will
use the formula to calculate the time of the application in thesystem [15]:tTi = i(5)
1 − ρiwhere Ti is the mean delivery time to the node i , and ti is
the mean duration of service request.
As described above, the transmission time of a LoRa data
frame on-air depends on several parameters. This time on-air
can be considered as the average time ti . The calculation of
the time on-air is described in [6].
The number of relay nodes is denoted by m . Hence, thetotal delivery latency of a data frame through m nodes to thetarget node is computed as:
m +1

T=

m +1

ti

  1− ρ
Ti =

i =1

i =1

(6)
i

D. Propagation model
In the LoRa network, radio configuration parameters suchas operating frequency, SF, BW, CR, and transmission power
influence the communication range and reception accuracy. If
the received signal power is greater than the receiversensitivity, we can say that the transmission was successful.
Transmission power decreases as it propagates over the air tothe recipient. Thus, the received power depends on thetransmission power and losses due to signal attenuation. As
know well in wireless communication, a log-distancepropagation model has been used in indoor and outdoor
environments. Pathloss varies with logarithmic distanceaccording to the following equation:(7)
PL(d ) = PL(d 0 ) + 10γ log10 ( d d 0 ) + X σ ,where PL ( d 0 ) is the average path-loss at a reference distanced0 ; γ is the path-loss exponent; d is the distance between thereceiver and the transmitter; X σ is a random value in theGaussian distribution with zero mean and standard
deviation σ .

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

While considering the application of LoRa technology inthe urban environment and dense urban development, which
are the main obstacle to signal propagation. Based on researchin [16], the parameters of path-loss propagation are set for thesimulation
model
in
the
urban
environment
with d 0 = 40 , PL ( d 0 ) = 127.51 , γ = 2.08 , and σ = 3.57 .
E. Simulation Model
The OMNET++ and inet frameworks are used to model the
network [17], [18]. They are known well to be used innumerous domains for simulating wired and wirelessnetworks. Based on these frameworks, we have developed amodule of the LoRa node. Since the OMNET++ library andframeworks are designed based on modular andcomponent-oriented principles, the LoRa node can be
integrated with the build-in modules from the inet framework.
A LoRa node consists of modules modeling radio andupper-layer protocols. In the LoRa radio module, we canconfigure radio parameters corresponding to our hardware
model. The other parameters, such as the spreading factor andcoding rate, are configured for the LoRa node in addition to
the usual configurations. In this case, the radio parameters
taken from the datasheet of the SX127x LoRa chipset [5] areused for the simulation model.
Moreover, in the LoRa medium module, the path-loss
propagation model is chosen in considering data transmission
in the urban environment, as mentioned above. In this work,
the propagation model parameters have been received from aseries of measurements presented in [16]. The measurements
in [16] correspond to the build-up urban environment, wheredevices are partially deployed indoors.
A sink node (gateway) is located at the center of the
network. The locations of end nodes are randomly generated
in the range of field simulation size.
The payload length is generated randomly from the end
nodes in the interval from 20 bytes to 150 bytes.
According to the exponential distribution, the sent intervals
between messages are random values with a mean equal to
120 seconds.
In each experiment, we analyze the delivery latency andpacket delivery ratio to the sink node. The obtained results arepresented in the next section.
The simulation parameters and values are shown presented
in Table 1.
TABLE ISIMULATION PARAMETER
Parameter
Value
Frequency, MHz
868
Bandwidth, kHz{125, 250}
Spreading factor{7, 8, 9, 10}
Coding rate
4/5
Transmission power, dBm
14
Antenna, dBi
5Payload length, B
Random (20, 150)
Sent interval, sExponential(120s)
Number of nodes{10, 20, 40, 100}
Field size, m2
2000 x 2000
Simulation time, s
20000

1283

IV. ANALYTICAL RESULTS
A. Delivery latency
Each device node is considered as a queueing system.
Hence, the delivery latency in a communication link between
two neighbor nodes is calculated according to the formula (5).
Moreover, the delay time for transferring a payload size of 80
bytes is calculated when changing the load intensity. The
calculation results are shown in Figure 3 with configuration
parameters SF = {7, 8,9, 10} and BW = {125, 250} kHz.

Fig. 3. Influence of load intensity on the delivery latency

As shown in Figure 3, the data transmission time increases
when increasing the load intensity. It can be seen that a low
spreading factor and a wide bandwidth provide low delivery
latency. In the case of using SF = 10 and BW = 125 kHz, thedelay time increases significantly up to 2 seconds. Hence thetransmission latency via the relay nodes with such radio
modules parameters will also be high and can increase up totens of seconds as shown in Figure 4, where packets aretransmitted via several relay nodes to the sink node.
In the mesh topology model, the node receives and relays
the packets to the next destination. Therefore, the number ofrelay nodes that packet passes influences the delivery latency
to the destination. With the load factor equal to 0.4 and the
payload size of 80 bytes, as shown in Figure 4, the delivery
latency increases linearly with the number of relay nodes.

Fig. 4. Influence of the number of relay nodes on the delivery latency

According to Figure 4, when using a bandwidth of 125 kHz
and spreading factors SF = {9, 10}, the transmission delay
increases to tens of seconds while packets are relayed via 10
nodes. Depending on the requirements of IoT applications,the parameters SF and BW can be selected accordingly. On
the other hand, using a wider bandwidth (BW = 250 kHz)provides a short delivery latency through several relay nodes.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

1284

Moreover, we can see the differences in latency distributionsin the networks using the narrow and wider bandwidth in
Figure 5.
When considering the impact of the spreading factor in the
network of 100 nodes, different distributions of delivery
latency are collected from device nodes to the target node.
Figure 5 shows the latency distributions in various SF = {7, 8,
9, 10} and BW = {125, 250} kHz configured for LoRa nodes.
Data delivery time is longer with a high spreading factor and anarrow bandwidth. The SF parameter represents the numberof encoded chips, so a high SF value requires a longer
transmission time.
Fig. 6. Latency distribution in the network with different number of nodes

B. Packet delivery ratio
Considering the packet delivery ratio, the number of nodes
in the network affects the packet delivery ratio. As shown in
Figure 7, the results are received from experiments in the
networks using SF = 10 and BW = 250 kHz. As the number ofnodes increases in the network, the packets also are generated
more. Hence, the latency of data transmission and packet loss
may increase.

a)Fig. 7. Packet delivery ratio in the network with different number of nodes

V. CONCLUSIONb)
Fig. 5. Latency distribution in different SF and BW

Moreover, the latency is obtained in the networks having a
different number of nodes. Figure 6 shows the latency
distribution in the networks with 20 nodes and 100 nodes withthe same configuration parameters. As more nodes in the
network, there are more generated packets. In the relay nodes,
received packets are waited in the queue to transmit to thenext destination. Besides, the packets are relayed via several
nodes to the sink node. The delay is high in the network ofmany nodes. Thus, we can use a Long-Range mesh network toconnect other networks that cannot transmit data over a long
distance. There may be 20 nodes in the mesh network.
However, these nodes can communicate with each other andestablish communication in a large area, where theshort-range networks are used to collect data.

Nowadays, LoRa technology is used in the communication
part for many IoT applications. Taking the advantages of a
LoRa mesh network, the network coverage area can be
expanded without adding more base stations. The meshnetwork model based on LoRa technology was analyzed
while considering the delivery latency and packet loss ratiowith different configuration parameters.
Based on the analytical results, we can see the influence ofconfigured parameters of the LoRa nodes on the latency
distribution and the percentage of delivered packets to thedestination node. Besides, the number of nodes in the network
also affects the transmission latency and PDR. As using a highSF and a low BW, the transmission delay increases in the
network, but it increases the reception sensitivity. Datatransmission takes a long latency in the LoRa mesh network.
However, it opens the ability to integrate with the othershort-range networks in the mesh topology.
Thus, the mesh networks combined with LoRa technology

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 2, March 2020

can bring more advantages in the application of the wirelesssensor network in terms of increasing the coverage area andlow power consumption.
ACKNOWLEDGMENT
The publication has been prepared with the support of the
grant from the President of the Russian Federation for state
support of leading scientific schools of the Russian Federationaccording to the research project No. SS-2604.2020.9.
REFERENCES
[1]

[2]

[3]

[4]

[5]
[6]
[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]
[16]

[17]
[18]

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ubiquitous sensor networks (fusn) by using lpwan protocols,” in ser.
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2016.
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Corporation, July 2013.
I. Nurilloev, A. Paramonov, and A. Koucheryavy, “Connectivity
estimation in wireless sensor networks,” in Internet of Things, SmartSpaces, and Next Generation Networks and Systems. Springer, 2016,
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V. D. Pham, T. D. Dinh, and R. Kirichek, “Method for Organizing
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Congress on Ultra Modern Telecommunications and Control
Systems and Workshops (ICUMT), 2018, pp. 1–6.
T. D.Dinh, V. D. Pham, R. Kirichek, A. Koucheryavy, “Flying
network for emergencies,” in ser. Communications in Computer andInformation Science book series. Springer, 2018, vol. 919, pp. 58-70.
T. D. Dinh, R. Pirmagomedov, V. D. Pham, A. A. Ahmed, R.
Kirichek, R. Glushakov, A. Vladyko, “Unmanned aerial
system-assisted wilderness search and rescue mission,” International
Journal of Distributed Sensor Networks, vol. 15(6), June 2019.
T. Addabbo, A. Fort, A. Mecocci, M. Mugnaini, S. Parrino, A.
Pozzebon, and V. Vignoli, “A Lora-based IoT sensor node for waste
management based on a customized ultrasonic transceiver,” in 2019
IEEE SensorsApplications Symposium (SAS). IEEE, 2019, pp. 1–6
B. Ngom, M. Diallo, B. Gueye, and N. Marilleau, “Lora-based
measurement station for water quality monitoring: Case of botanicalgarden pool,” in 2019 IEEE Sensors Applications Symposium (SAS).
IEEE, 2019, pp. 1–4.
P. Gotthard and T. Jankech, “Low-cost car park localization using
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New York: Wiley, 1976, pp. 1–20.
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1285

Van Dai Pham was born in Viet Nam 1993 andreceived his BS and MS degree in info-communication
technologies and communication systems from theBonch-Bruevich Saint Petersburg State University of
Telecommunications in Russia, in 2017 and 2019,respectively. Currently, He is a Ph.D. student in the
Software Engineering and Computer Science at theBonch-Bruevich Saint-Petersburg State University of
Telecommunications, Russia. Since 2016, he has
published papers related to IoT and its applications in international journals
and conferences. His research interests include IoT, FANET, Smart Cities,
LoRa network, Wireless Sensor Networks.
Victor Kisel was born in Russia 1998 and received
an Bachelor’s degree in heterogeneous networks
from the Bonch-Bruevich Saint-Petersburg State
University of Telecommunications in Russia.
Currently, he is a Master’s student in the
Department of Communication Networks and Data
Transmission
attheBonch-Bruevich
Saint-Petersburg
State
UniversityofTelecommunications, Russia. His researchinterests include IoT, heterogeneous networks, software-defined radio.
Dr. Sc. Ruslan Kirichek is working at theBonch-Bruevich Saint Petersburg State University of
Telecommunications as a Professor in the Department
of Software Engineering and Computer Science. He
was born in 1982 in Tartu (Estonia). He graduatedMilitary-Space Academy A.F. Mozhaiskogo and theBonch-Bruevich St. Petersburg State University of
Telecommunications in 2004 and 2007, respectively.
He received Ph.D. at the Bonch-Bruevich St.
Petersburg State University of Telecommunications in 2012 and Dr.Sc. at
the Povolzhskiy State University of Telecommunications and Informatics in2018. From 2008 to 2013 he worked as a senior researcher at the Federal
State Unitary Enterprise "Center-Inform". Since 2012 he has been working
as the Head of the Internet of Things Laboratory at the Bonch-Bruevich SaintPetersburg State University of Telecommunications (iotlab.ru). Since 2017
he has been working as ITU-T Q12/11 Rapporteur in “Testing of Internet ofthings, its applications and identification systems”. He is a General Chair of
the International Conference “Internet of Things and Its Enablers”(inthiten.org).
Dr. Sc. Andrey Koucheryavy was born in Leningrad
02.02.1952. After graduated from the Leningrad
University of Telecommunication in 1974, he went toTelecommunication Research Institute named LONIIS,
where he was working up to October 2003 (from 1986 up
to 2003 as the First Deputy Director). He became thePh.D. and Dr.Sc. in 1982 and 1994 respectively. He is at
the Bonch-Bruevich St. Petersburg State University of
Telecommunications (SUT) a professor from 1998. He isa Chaired professor of the department “Communication Networks and Data
Transmission” from 2011. He is an honorary member of A.S.Popov’ssociety. Prof. A.Koucheryavy is the Chairman of Study Group 11 ITU-T(Study periods 2017-2020). His scientific areas of interest are the network
planning, teletraffic theory, IoT and its enablers.
Dr. Sc. Alexander Shestakov is vice-rector for
scientific
work
at
the
Bonch-Bruevich
Saint-Petersburg
State
UniversityofTelecommunications. He was born in 1960 in Bobrov,Voronezh region, 14.05.1960. He graduated from theLeningrad Military School of Communications (1982)
and the Academy of Communications (1991), as wellas the Institute of Innovation and Industrial Property(1994). For a long time he worked in St. Petersburg in research organizations
and the Rubin research institute as the first deputy general director. He
became the Ph.D. and Dr.Sc. in 1992 and 2017 respectively. His scientificareas of interest are the network planning, geographic information
technologies, teletraffic theory, mobile apps and services, information
security.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

1286

Orthogonal Defect Classification-based
Ontology Construction and Application ofSoftware-hardware Integrated Error Pattern ofSoftware-intensive Systems
Xuan Hu, Jie Liu
Information Security Research Center, CEPREI, No. 110, Dongguanzhuang Road, Guangzhou, China
Key laboratory of Ministry of Industry and Information Technology, No. 110, Dongguanzhuang Road,
Guangzhou, China
huxuan@ceprei.com, 47507346@qq.com

Abstract—Orthogonal defect classification (ODC) is amulti-dimensional measurement system with both qualitativeand quantitative characteristics. And it is currently widely usedin the software industry. However, its high level of abstraction
leads to limited semantic information. Therefore, it seems tohave a limited role in the process of software engineering ofsoftware-intensive systems (SISs). To solve this problem, thispaper first analyzes software error lifetime from the perspectiveof knowledge-based software engineering and proposes an error
generation model. Then, the paper proposes the concepts ofsoftware error pattern (SEP) and software requirements error
pattern (SREP) based on the ODC. Then, according to an error
generation mechanism, four types of software-hardware
integrated error pattern (SHIEP) in the requirement stage,
which is a sub-category of SREP, and corresponding ontology
representation are given, focusing on “scenario”, “error
manifestation” and “solution”. Finally, this paper takes acertain type of airborne radar software system as an example,
uses protégé to edit the SHIEPs and instances, and further
introduces the application of software FMEA based on theabove work. The results show that the prior information basedon the SHIEPs is helpful to discover potential failures andfailure modes that may adversely affect the function or
performance of SISs. Therefore, the proposed SHIEP is of greatsignificance for improving the quality of software development
and verification.
Keyword-orthogonal defect classification, ontology, pattern,
software error

H

I. INTRODUCTION
OW to provide developers with fast and effective
feedback in a controlled and measurable way is a———————————————————————
Manuscript received on Dec. 2, 2019. This work was supported by the
National Key Research and Development Program of China (Grant ID:
2019YFC0118800), and a follow-up of the invited journal to the accepted &presented paper entitled “The Orthogonal Defect Classification-based
Software Error Pattern Ontology Construction Researches” of the 22th
International Conference on Advanced Communication Technology(ICACT2020).
Xuan Hu, she is a senior engineer of CEPREI Laboratory, China. (E-mail:huxuan@ceperi.com)
Jie Liu, he is the director of the Information Security Research Center of
CEPREI
Laboratory,
China.
(corresponding
author,email:
47507346@qq.com)

problem that plagues the software industry [1]. There are two
common forms of software defect analysis: statistical defectmodels and root cause analysis. The first is a quantitative
method with good repeatability, but cannot provide feedback
to developers timely in the way of available process control.
The second is a qualitative method focusing on process
details and therefore cannot be applied as an engineering
method to full process control [1]. Orthogonal defectclassification (ODC) is a technique that can bridge the gap
between quantitative methods and qualitative analysis [2]. It
extracts semantic information in defects via classification,
converting what is semantically very rich into a few vital
measurements on the product and process. Thus, essentially it
is a multi-dimensional measurement system that transforms
qualitative information into quantitative measures [1].
There are some researches on ODC recently, e.g., theapplications of “defect triggers” during a testing process [3]and the researches on Bohr-Mandel errors using the “defecttriggers” [4], the researches on ODC applications of safety,
critical errors of spacecraft systems [5], [6], the researches onmodeling Y2K errors through ODC [7], the researches on adefect tracing system based on ODC [8], the researches on
automated generation of ODC [9], [10], the researches on
in-process usability problem classification, analysis andimprovement based on ODC [11]. In all, the representative
researches on ODC tend to focus on its macroscopic
characteristic as a measurement system. However, it is too
abstractive to reflect individual gaps because of theconsistency across phases and the uniformity across products[2]. Therefore, it can measure software processes only andcannot guide verification directly. In addition, it is difficult toapply ODC to the application scenario of software-intensive
systems (SISs). Nowadays, SISs have become adevelopmental direction of large-scale systems, and theapplication ranges of SISs involve the embedded systems forautomotive industry and aerospace applications, thededicated systems for wireless communications [12]. A
software product always stems from a defined requirements
set. For SISs, the formation of a requirements set is along-term and complicated process, and a main reason is: two
problems of system complexity and sudden behavior are
increasingly highlighted in the application process of SISs.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

The root cause of these two problems lies in the uncertainty
of environment and the unpredictability of operation andscenario. ODC is on a higher level and has limited semantic
information. Thus, it is difficult to describe the complicatedscenarios of SISs via ODC. And it hinders the researches on
the above problems through ODC.
Experience has shown that some abnormal events that
occur under certain extreme conditions, although they have a
low probability of occurrence, are extremely harmful. These
problems need to be collected through long-term
accumulation. In addition, there is a kind of errors depending
on both operating time of hardware and operating state ofsoftware. Thus, one important source of an SISs requirements
set is the error set obtained according to experience.
The rest of this paper is divided into the following sections:section 2 analyzes the lifetime of software errors andproposes an error generation model from the viewpoint ofknowledge-based software engineering. Section 3 andsection 4 present the definitions of software error pattern(SEP) and software requirements error pattern (SREP) basedon ODC, respectively. Section 4 also describes four types ofsoftware-hardware integrated error pattern (SHIEP)
according to an error generation mechanism, and constructs
an SHIEP ontology (SHIEPO) by an object-oriented
ontology representation method focusing on “scenario”,“error manifestation” and “solution”. In the knowledge
engineering community, an ontology is a formal and explicit
specification of a shared conceptualization [13]. This work
unifies the SHIEP concept in the ontology framework.
Section 5 presents a case study, taking an airborne radar
software system as an example to introduce the application ofabove researches on software failure mode and effects
analysis (SFMEA). Finally, section 6 concludes the study.
II. LIFETIME AND GENERATION MODEL OF SOFTWAREERRORS
Software is a sequence of computer knowledge that
describes human knowledge and the correct usage of humanknowledge [14]. And it can be regarded that software errors
are the reflection of inconsistency between the knowledge
produced in the process of creating a software product andthe original knowledge, and such reflection is regarded asnecessary adjustments [15]. Fig. 1 shows that software error
lifetime runs throughout developing, verification and fieldusage. The reliability test at the verification stage refers to atest type for hardware systems. It is a necessary means toimprove product design, and evaluate and assess productquality characteristics during product development andproduction. There are several forms of incorrect software
knowledge, (1) incorrect knowledge points or implicit
software errors induced by man-made mistakes, (2) using
correct knowledge in a wrong way [14].
produces defective documents
detects omitted errors in field usage
during developing
fixes errors
detects errors by vefification(1) incorrect knowledge points;(2)causes faults locates errors
implicit software errors induced by locates errors
man-made mistakes;(3) using correctcauses failures
knowledge in a wrong way
fixes errors
reliability
requirements design encoding inspection software
testing
test
field usage
developing
verification

Fig. 1. Software error lifetime

1287

Fig. 2 shows an error generation model. It is noted thatthese errors do not include system software errors andoperator errors. This figure shows that a software error isgenerated during the process of software developing.
Knowledge problems include domain knowledge problems
and software engineering knowledge problems. Actions andartifacts vary from one stage to another. In horizontal
direction, in-process errors may occur at various stages ofsoftware development. In vertical direction, knowledge
inconsistencies between adjacent and non-adjacent layers
may lead to inter-process errors. Software errors exist in
artifacts and are expressed in a certain form. The intra-layer
and inter-layer causes of errors are the scenarios of SEPs. The
scenarios can be generalized, such as in-layer or inter-layer
inconsistencies; they can also be relatively specific in
combination with specific patterns.
knowledge
problems

humanactions
creation,understanding
and revision
creation,understanding
and revision

domainknowledge
conception,
problems,
understanding,softwareengineering revision, realization
and reuseknowledge
problems

system requirements stage

1) requierments

requirements
collecting
developing 2) requierments generates
defining
requirements 3) domain
management analysis

design stage
generates
generalized/
detailed
designdeveloping stage
generates
encoding,
debuggingerrors might be produced during
in-process or inter-process

artifacts(with errors)
document,
thinking model
document,
thinking
model,
algorithm,
data structure
environmental
artifact,
code base,languagestructure

Fig. 2. Software error generation model

III. SOFTWARE ERROR PATTERN
The above researches show the software error lifetime
throughout the software developing, verification and fieldusage. Therefore, the related researches should consider themultiple viewpoints of developers, testers, and users.
ODC is an error classification method that helps to obtainerror information at a higher level. Its elements include“defect triggers”, “defect types”, “targets”, etc. They are
independent and orthogonal with each other. A “defecttrigger” is a condition that allows a defect to surface (What).
The “defect type” reflects the nature of errors and guides how
to amend them (How). Experience shows that, (1) the
contexts in which errors are generated are similar, and theeffect is similar to the “defect triggers” attribute; (2) errors
have similar manifestations and can be modified, i.e., there isa software implementation that satisfies the correct intent.
The effect is similar to the “defect types” and “targets”
attributes. These inspire the paper to study software errors
with the help of the concept “Pattern” [16].
Definition 1 The SEP refers to the error produced in thesoftware development stage, which occurs repeatedly in a
specific error lifetime scenario, and may cause a system(component) to fail to perform the expected function or affectthe maintainability of the system. Such errors are general andcommon in a specific scenario and can be corrected byvarious means. SEP’s components can be instantiated.
Therefore, SEP:= < Scenario, ErrorManifestation,
Solution, Severity, DetectionStage, GenerationStage,
Instance >.
This definition distinguishes the generation and survival

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020stages of errors. Furthermore, it is multi-viewpoint. Its corecomponents are: “Scenario”, “ErrorManifestation” and“Solution”. “Scenario” reflects tester and user viewpoints,
and is the basis for test cases and the catalyst that causes a
failure. “ErrorManifestation” and “Solution” reflect
developer viewpoint, express the nature of errors, and give a
way to eliminate the errors. SEP also has other components,
such as, “Severity”, “DetectionStage”, “GenerationStage”
and “Instance”. An “instance” is the result of mapping a SEP
concept at a higher level of abstraction to a specific case. The
components of SEP are shown in Fig. 3. Most error
classification systems use 5 to 15 defect attributes [1]. The
number of SEP components is in this range. The SEP
components are independent and orthogonal with each other.
Thus, SEP can be regarded as a point in Cartesian space.
Scenario(Instance)

DetectionStage

Severity

SEP

developer
viewpoint GenerationStage

tester and user
viewpoints

Solution(Instance)

ErrorManifestation

Fig. 3. SEP Components

Fig. 4 presents SEP and its components value sets in theform of classes based on experience and related references[1], [17]. Both SEP and its components can be regarded asclasses. The relationships between SEP and the components
are aggregation relationships. The “Scenario” class has 6
sub-classes. The “SEP” class has 3 operations of“measurement”, “software development”, and “software
DetectionStagerequirements inspection
design inspection
code inspection
unit test
function test
system test
reliability test
field use
1-dimension measurement()

Severity
criticalimportantaverage

FunctionTestScenario
function point coverage
test case sequence
extreme value
abnormal value
measurement data supply()
software verification()measurement data supply()
software verification()SEP

1-dimension measurement()
GenerationStagerequirements analysis
general design
detailed design
encoding

software development()

EncodingSolution
software development()

DesignSolution
software development()

measurement data supply()
software verification()

1-dimension measurement()
software verification()

software development()

RequirementsSolution
requirements incompleteness solution
requirements conflict solution

SystemTestScenario
start-up/restart
hardware/software configuration
software workload change
fault tolerance handling

Scenario

Solution

1-dimension measurement()

verification”.
The
operation
of“Scenario”,“ErrorManifestation”, “DetectionStage”, “GenerationStage”,
and “Severity” classes is “1-dimension measurement” incommon. The sub-classes of “Scenario” class have theoperation of “measure data supply” in common. The valuesof components are the attributes of the corresponding classes.
These classes can be instantiated, corresponding to the“Instance” of the components of SEP. The role of theoperation “measurement” is similar to the metric of ODC
attributes, i.e., the semantic information in errors is extracted
by classification and converted into a metric. For example,
the distribution and trends of various types of errors at eachstage can be analyzed, and the deviation from an expected
situation can indicate problems in a development process.
This provides timely feedback to developers and can guidebug fixes. The operation “software development” is given
from the developer viewpoint; while the operation “software
verification” is given from the tester and user viewpoints. In
addition, the difference between the attribute “software
workload change” and the attribute “system workloadchange” is that the “software workload” directly affects thesoftware product itself, exposing software product defects by
imposing the behaviors that may result in exceeding software
product resource limits or capacity limits, whereas the“system workload” directly affects the entire system, andgenerally has an indirect effect on software, such as thevibrations generated during an aircraft flight. The “safety
error” of “ErrorManifestation” refers to the error that may
cause failures of casualties, property damage orenvironmental damage. Such errors are very important in the
SISs.

InspectionScenario
backward compatibility
lateral compatibility
consistency
logic
concurrencyside effectabnormal scenario

measurement()
software development()
software verification()

1288

UnitTestScenario
path coveragesoftware verification()measurement data supply()
ReliabilityTestScenario
environment changesystem workload change

ErrorManifestation
functional error
safety error
interface error
logic error
timing/serialization error
initial error
algorithm error
documentation error
version control error
1-dimension measurement()
software development()

Fig. 4. Class representation of SEP and its components

measurement data supply()
software verification()
FieldUsageScenario
environment changesystem workload change
measurement data supply()
software verification()

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

IV. SREP ONTOLOGY

4.1 Ontology structure of SREP

SEP-related domains include the corresponding
domains in the stages of requirements, design, and coding
according to the “error generation stage”. And the resultsof a follow-up survey of 8,000 software projects acrossthe United States showed that the problems related torequirements processes account for 45%, and the lack of
end user participation and incomplete requirements are
two main reasons, each accounting for 13% and 12% [18].
In addition, extensive engineering experience has shownthat requirements problems are one of the main sources ofengineering problems; therefore, the research in this paper
is limited to the stage of requirements analysis, and below,
SEP (s) are replaced by SREP (s). Requirements analysis
mainly solves the problem of what a product should do[15]. The main reason for requirements problems is the
problems of knowledge sharing and reuse, and ontology isan effective means to solve these two problems. In
addition, objects (classes) do not always play a primaryrole in a domain, and sometimes it is more important to
identify relationships between objects (classes) [19].
Therefore, it is necessary to characterize otherassociations between SREP objects (classes). This paper
uses an object-based ontology method to characterize the
SREP and define its semantics.
4.1.1 Definition of SREPSREP is a summary of experience often making
mistakes in requirements engineering. Thus, this paper
proposes the definition of SREP according to thedefinition of SEP.
Definition 2: The SREP refers to the error produced in thesoftware requirements development stage, which occurs
repeatedly in a specific error lifetime scenario, spreads inthe subsequent design and implementation, and may
cause a system (component) to fail to perform theexpected function or affect the maintainability of thesystem. Such errors are general and common in a specificscenario and can be corrected by various means.
The definition shows that the core components of an
SREP are ‘‘Scenario’’, ‘‘ErrorManifestation’’ and‘‘Solution’’. In addition, “Severity” should also be
included. There is a class inheritance relationship between
SEP and SREP as shown in Fig. 5.
SEP

SREP

+Name
+Scenario+Severity+ErrorManifestation+DetectionStage+GenerationStage+Instance+Name+Scenario+Severity+ErrorManifestation+DetectionStage+GenerationStage+Instance+Solution()+Solution()Fig. 5. Class inheritance relationship between SEP and SREP

1289

4.1.2 Constituent components of SREPOThe universe of SREPO constituent components U is:
U = {Concepts, Object properties, Data properties, PR, PC,
Inherit-hierarchies, Relationships, Instances, Mappings,
Rules}. Concepts, Inherit-hierarchies, Relationships, andInstances build the basic skeleton of the SREPO. All othercomponents are attached to the basic skeleton and refine
the basic skeleton. Object properties and Data properties
are two types of properties. The Object properties connect
instances together, and the Data properties connect
instances and values together. PR indicates the restrictions
on properties, including the restrictions on the type, range,
and maximum/minimum number of property values. PC
indicates property characteristics. Mappings represent the
mappings between different levels of ontology. Rules
include axioms and custom rules. The Rules can be used
to constrain information, prove correctness, or derive new
information. The Rules can also be used to express richer
relationships between concepts.
4.1.3 Construction of SREPOThis paper builds the SREPO according to the “error
generation stage”.
Definition 3 SREPO := <Concepts, Object properties,
Data properties, PR, PC, Inherit-hierarchies, Relationships,
Rules, Instances>.
The concept classes of SREPO is shown in Fig. 6. The
concept classes marked with an “*” are non-terminal
concept classes, and the rest are terminal concept classes.
ErrorManifestation

Scenario
Solution

SREP

Severity

EventFlow
StaticEntity*

Objective*

System*Resource

DynamicInteraction*

Task*

Constraint*

Action*State*

PreConstraint

PostConstraint

DomainConstraint

OriginalState

Environment*
is_aPeopleAgent*

Component*

isPartOf
describes

Fig. 6. Concept class hierarchies of SREPODefine an inheritance relationship as:
Definition 4 An inheritance relationship is a mechanismof a sub-class automatically sharing the properties andstructure of a parent class in the SREPO concept classhierarchy.
Then, the sub-class and the parent class of non-terminal
concept classes form an inheritance relationship.
Therefore, a new class can be created based on an existing
concept class by taking the content defined by the existingclass as its own content and adding new content. A portionof the concept relationships of SREPO is obtained asshown in Table. I. The left side of arrow is a source
concept node, and the right side is a destination concept
node. Take an example of “subObjective”. Its property
characteristic is partial order. That is because it satisfies

EffectStateICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020“reflexivity”,“symmetry”
and“transitivity”
simultaneously, i.e.,(1) a  A, (a, a)  I(2) a, b  A, ((a, b)  I )  ((b, a)  I ) → (a = b)(3) a, b, c  A, ((a, b)  I )  ((b, c)  I ) → (a, c)  I
and it can be represented as ab .
TABLE IA PORTION OF CONCEPT RELATIONSHIPS OF SREPO
Relationshiptypes
subObjective
interact
has

Relationships
Objective→Sub-objective
System→Environment
System→Objective

Property
characteristics
partial ordersymmetry-

Furthermore, a portion of the concept space of SREPO
is shown as Fig. 7.
PreConstraint
PostConstraint

partialOrderpartialOrder

DomainConstraint
partialOrderexertsinteracts
has
Environment*
System*Constraint*

SubObjective*
objective1
partialOrder partialOrderhas

causesStatetransits
generates
EventFlowpartialOrder
EffectStateState
partialOrder
OriginalStatedescribes
Scenario

Fig. 7. A portion of concept space of SREPO

4.2 Ontology representation of SHIEP
4.2.1 Ontology representation framework of SHIEPFor the SISs, software requirements (SRs) can bedivided into two categories: internal requirements andexternal requirements from the perspectives of macrofunction and internal structure. The internal requirements
describe the connotation of requirements, i.e., the internal
structure of a software system; the external requirements
describe the extension of requirements, reflecting theinteraction between a software system and its
environment. The environment here includes the entitiesof people or other systems in contact with the softwaresystem. The external requirements determine the internalrequirements. The external requirements can be further
divided into explicit requirements and implicit
requirements. The explicit requirements describe thedirect interaction and constraint between a softwaresystem and its environment; the implicit requirements
describe the indirect interaction and constraint between asoftware system and its environment. Thus, SRs := <internal requirements, external requirements < explicit
requirements, implicit requirements > >. Then,
Environment Ontology := < ExplicitEnvironment,
ImplicitEnvironment>. Moreover, the set of implicit
environment can be obtained based on experience:

1290

ImplicitEnvironment=<OtherEnvironment<
StressEnvironment<TemperatureEnvironment,
VibrationEnvironment,
HumidityEnvironment,
ElectricityEnvironment, CombinedStressEnvironment>,
EMEnvironment,
GeographyEnvironment,
AtmosphereEnvironment>, OperationEnvironment>,
where
CombinedStressEnvironment= VibrationEnvironment ElectricityEnvironment  HumidityEnvironment.

TemperatureEnvironment

Requirements engineering focuses on the expected
goals and corresponding scenarios of software systems[20]; therefore, it can be considered that the SRs are theenvironmental state changes and goal realization causedby the interaction between a software system and its
environment. Then, SRs:=<System <Agent, Entity>,
Environment<ExplicitEnvironment,
ImplicitEnvironment>,
Interact<Control,
Monitor,
Perform>,
Objective<Avoid,
Cease,
Achieve,
Maintain>>, where “Agent” represents a special object,
not a person, but only a machine or other mechanism, andmust be autonomy, initiative and intelligence. “Entity”
refers to any static thing that constitutes the basic structureof a software system. “Interact” refers to the interaction
between a software system and its environment.
An event flow describes the sequence of interactive
actions, including how to start, end, and how to interact.
The semantics of SRs also describe the sequence ofinteractive actions changing the states of environment.
Therefore, state transition models can be adopted todescribe state transitions and event flows, i.e., theformation of scenario. Then, an environment framework
can be defined as follows,
State< OriginalState, EffectState >  NULL
interact→transit: T (State, State’)transit: T (State, State’)→EventFlow
Constraint<
PreConstraint,
PostConstraint,
DomainConstraint {Trigger, Pred, Synch, Conseq}>“Constraint” acts on “interact”, “transit” and“EventFlow”, respectively. “DomainConstraint” includes“Triggerdomain”,“Preddomain”,“Synchdomain”
and“Conseqdomain”. And domain knowledge can be describedby the state transition of environment.
“Environment” can be described as,
Environment=<
ExplicitEnvironment,
ImplicitEnvironment<
OperationEnvironment,
OtherEnvironment<StressEnvironment<
TemperatureEnvironment,
VibrationEnvironment,
HumidityEnvironment,
ElectricityEnvironment,
CombinedStressEnvironment>,
EMEnvironment,
GeographyEnvironment, AtmosphereEnvironment >> >,where TemperatureEnvironment = {t-Envi1,…, t-Envis1},
VibrationEnvironment = {v-Envi1,…, v-Envis2},
HumidityEnvironment = {h-Envi1,…, h-Envis3},
ElectricityEnvironment = {e-Envi1,…, e-Envis4},
CombinedStressEnvironment = {c-Envi1,…, c-Envis5},
EMEnvironment={EM-Envi1,…,
EM-Envis6},

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

GeographyEnvironment = {g-Envi1,…, g-Envis7},
AtmosphereEnvironment={a-Envi1,…,
a-Envis8}.s1,…,s8  N . Interact = {interact1,…,
interactn}, n  N . State={state1, ……, statet}, t  N .
Constraint = {constraint1, constraint2,…, constraintl},l  N . They are all obtained based on the inference ofdomain ontology.
4.2.2 Types and ontology representations of SHIEPs
Most of the current software requirements elicitation(SRE) methods do not consider prior information such as
SEPs at the beginning of SRE, resulting in incomplete
knowledge. SIS software generally has the following
characteristics: (1) SIS software is closely connected withhardware, and has strong requirements of dedicatedexternal equipments; (2) SIS software is usually strong
real-time; (3) SIS software usually operates in specificconditions, and relates to interactive environments; (4)SIS software has high safety requirements. The above
characteristics also spawn the SHIEPs (SHIEs). The
SHIEPs (SHIEs) arise from requirements analysis stage
and are a sub-class of SREPs (SREs). They have
gradually become an important part of error patterns(errors) during the field usage of SISs, and they are
extremely harmful. Therefore, it is necessary to study the
SHIEPs (SHIEs). Moreover, the collection of SHIEPs(SHIEs) mainly depends on the field usage of SISs. Thatis because the environment of field usage or field test of
SISs is relatively real, and the exposed problems canreflect actual situations. The types of SHIEP according toan error generation mechanism are shown in Fig. 8. The
core components of SHIEP are: “Scenario”,“ErrorManifestation” and “Solution”.
operation

hardwaresoftwarehardwaresystem(2) environment

hardwaresoftwarehardwaresystemhardwaresoftwarehardwaresystemsoftwarehardware(1)(3)

operation
environment(4) operation

systemFig. 8. Four types of SHIEP

1) Type 1 of SHIEPThe type 1 of SHIEP in Fig. 8 is described as, theunpredictable changes in operating conditions directly
affect hardware and eventually lead to a system failure.
The changes directly affect the hardware components of asystem, accumulate over time, and gradually penetrate theinternal units of the components, thereby affecting thecharacteristics of the components or circuits. Then, thegenerated abnormal electrical signals act as software
inputs through software-hardware interfaces to software,
resulting in abnormal software operations, and then react

1291

to the components or the system, and in turn cause global
or local failures of the system. The complete process isshown in Fig. 9. The “ units of hardware components’ ”
and “ hardware components of system’ ” in this figure
represent the state change of corresponding part (normal
to abnormal), respectively. This process is a closed loop
feedback process, and every step is accompanied by astate transition.
time(t)0

t1
software/ input abnormal generate
hardwareelectricalinterfaces
signals
input

softwaret1

characteristics ofcomponents orcircuits

units ofhardwarecomponents

penetrate hardware components
of system
act ondirectly
input
unpredictable changes in operational conditions
controlled
process
local failures or global failures
outputaffectabnormal counteract
units of hardware counteract hardware components
softwarecomponents'of system'
induce operations

t2 time(t)Fig. 9. Type 1 of SHIEPThe specific contents of the core part of type 1 of
SHIEP are as follows,
Scenario of requirements incompleteness:
Scenario 1:
constraintk(interacti)  Constraint(1  k  l, 1  i  n,k  N , l  N , i  N , n  N ),∃ (interacti  Interact) → (statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) →(statej(hardware) ∉State(hardware))ErrorManifestation: safety error
Solution: A complete set of predefined operating
conditions and a complete set of domain constraints are
given to build a complete domain knowledge ontology.
2) Type 2 of SHIEPThe type 2 of SHIEP in Fig. 8 is described as,
unpredictable environmental changes directly affecthardware and eventually lead to a system failure. Thechanges directly affect the hardware components of asystem, accumulate over time, and gradually penetrate theinternal units of the components, thereby affecting thecharacteristics of the components or circuits. Then, thegenerated abnormal electrical signals act as software
inputs through software-hardware interfaces to software,
resulting in abnormal software operations, and then react
to the components or the system, and in turn cause global
or local failures of the system. The complete process isshown in Fig. 10. The “ units of hardware components’ ”
and “ hardware components of system’ ” in this figure
represent the state change of corresponding part (normal
to abnormal), respectively. This process is a closed loop
feedback process, and every step is accompanied by astate transition.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020time(t)0
t1

software/hardware input
interfaces

penetrateabnormalcharacteristics of affect units of hardware
hardware components
electricalcomponents or circuits
components
of system
signals generate
input
act on directly
controlled
process

input

softwareinducecounteract
abnormalunits of hardware counteract hardware components
softwarecomponents'of system'
operations

t1

unpredictableenvironmental changes
outputlocal failures or global
failurest2time(t)Fig. 10. Type 2 of SHIEPThe specific contents of the core part of type 2 of
SHIEP are as follows,
Scenario of requirements incompleteness:
Scenario 2:
constraintk(interacti)  Constraint(1  k  l, 1  i  n,k  N , l  N , i  N , n  N ),Sub-scenario 2-1:
 (t-Envis  ImplicitEnvironment) → ((t-Envis
 ImplicitEnvironment)  Environment) →(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s1  s, s  N )Sub-scenario 2-2:
 (v-Envis  ImplicitEnvironment) → ((v-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software))→(statej(hardware)∉State(hardware))(s2  s, s  N )Sub-scenario 2-3:
 (h-Envis  ImplicitEnvironment) → ((h-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s3  s, s  N )Sub-scenario 2-4:
 (e-Envis  ImplicitEnvironment) → ((e-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s4  s, s  N )Sub-scenario 2-5:
 (c-Envis  ImplicitEnvironment) → ((c-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s5  s, s  N )Sub-scenario 2-6:
 (EM-Envis  ImplicitEnvironment) → ((EM-Envis
 ImplicitEnvironment)  Environment) →(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s6  s, s  N )Sub-scenario 2-7:1292

 (g-Envis  ImplicitEnvironment) → ((g-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s7  s, s  N )Sub-scenario 2-8:
 (a-Envis  ImplicitEnvironment) → ((a-Envis ImplicitEnvironment)
Environment)→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s8  s, s  N )ErrorManifestation: safety error
Solution: A complete set of predefined operating
conditions and a complete set of domain constraints are
given to build a complete domain knowledge ontology.
3) Type 3 of SHIEPThe type 3 of SHIEP in Fig. 8 is described as,
unpredictable changes in operating conditions andunpredictable environmental changes directly affecthardware and eventually lead to a system failure. Thechanges directly affect the hardware components of asystem, accumulate over time, and gradually penetrate theinternal units of the components, thereby affecting thecharacteristics of the components or circuits. Then, thegenerated abnormal electrical signals act as software
inputs through software-hardware interfaces to software,
resulting in abnormal software operations, and then react
to the components or the system, and in turn cause global
or local failures of the system. The complete process isshown in Fig. 11. The “ units of hardware components’ ”
and “ hardware components of system’ ” in this figure
represent the state change of corresponding part (normal
to abnormal), respectively. This process is a closed loop
feedback process, and every step is accompanied by astate transition.
time(t)0t1penetrateabnormal generate
software/hardware input
characteristics of affect units of hardware
hardware components
electricalinterfaces
components or circuits
components
of system
signals
input
act on directly
unpredictable changes in
input
controlled
operational conditions andprocess
environment
outputinduce abnormal counteract units of hardware counteract hardware components
local failures or global
softwaresoftwarefailurescomponents'of system'
operations
t1
t2
time(t)Fig. 11. Type 3 of SHIEPThe specific contents of the core part of type 3 of
SHIEP are as follows,
Scenario of requirements incompleteness:
Scenario 3:
constraintk(interacti)  Constraint(1  k  l, 1  i  n,k  N , l  N , i  N , n  N ),Sub-scenario 3-1:
Operation)  ((t-Envis  ((operationi ∉ImplicitEnvironment)→((t-Envis
ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s1  s, s  N )Sub-scenario 3-2:
Operation)  ((v-Envis  ((operationi ∉ImplicitEnvironment)→((v-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s2  s, s  N )Sub-scenario 3-3:
Operation)  ((h-Envis  ((operationi ∉ImplicitEnvironment)→((h-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s3  s, s  N )Sub-scenario 3-4:
Operation)  ((e-Envis  ((operationi ∉ImplicitEnvironment)→((e-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s4  s, s  N )Sub-scenario 3-5:
Operation)  ((c-Envis  ((operationi ∉ImplicitEnvironment)→((c-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s5  s, s  N )Sub-scenario 3-6: ((operationi ∉ Operation)  ((EM-Envis ImplicitEnvironment)→((EM-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s6  s, s  N )Sub-scenario 3-7:
Operation)  ((g-Envis  ((operationi ∉ImplicitEnvironment)→((g-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))(s7  s, s  N )Sub-scenario 3-8:
Operation)  ((a-Envis  ((operationi ∉ImplicitEnvironment)→((a-Envis
ImplicitEnvironment)
Environment)))→(statei(hardware) ∉State(hardware)) → (statei(software) ∉1293

State(software)) → (statej(hardware) ∉State(hardware))(s8  s, s  N )ErrorManifestation: safety error
Solution: A complete set of predefined operating
conditions and a complete set of domain constraints are
given to build a complete domain knowledge ontology.
4）Type 4 of SHIEPThe type 4 of SHIEP in Fig. 8 is described as,
unpredictable changes in operating conditions directly
affect software and eventually lead to a system failure.
The changes directly affect the software and cause
abnormal software operations generating abnormal
electrical signals through software-hardware interfaces.
Then, the abnormal electrical signals directly act on theunits of hardware components and then the hardware
components of a system, and in turn cause global or local
failures of the system. The complete process is shown in
Fig. 12. This process is a closed loop feedback process,and every step is accompanied by a state transition.
t2time(t)hardware components
of system
controlled
process

act onunits of hardware
components

input

unpredictable changes in operational conditions

outputlocal failures or global failures

directly act on
softwareinduceabnormal
softwareoperations

t1
act onabnormalelectricalsignals
generatepass through0

software/hardware interfacest1time(t)Fig. 12. Type 4 of SHIEPThe specific contents of the core part of type 4 of
SHIEP are as follows,
Scenario of requirements incompleteness:
Scenario 4:
constraintk(interacti)  Constraint(1  k  l, 1  i  n,k  N , l  N , i  N , n  N ),∃ (interacti  Interact) →(statei(software) ∉State(software)) → (statej(hardware) ∉State(hardware))ErrorManifestation: safety error
Solution: A complete set of predefined operating
conditions and a complete set of domain constraints are
given to build a complete domain knowledge ontology.
V. CASE STUDY
Various applications can be implemented based on the
SHIEP, e.g., using the instances of SHIEP to guide theapplications of failure mode and effect analysis (FMEA)
method to detect the potential failure modes that may
adversely affect system functions or performance. This isbecause the three core components of SHIEP represented
by the ontology method are conducive to FMEA analysts
familiar with the actual usage of a system and theweaknesses of a design. Then, the followings can be

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

achieved: (1) identify known or potential failure modes ina system; (2) assess the effect of the failure modes; (3)propose improvement suggestions to reduce or eliminate
these failures. The above effects are particularly
significant under the current software-intensive
developmental trend of large-scale systems. And the basic
work is to summarize test experience and collect related
SHIEP instances, and then to represent them by the
ontology and store them in an ontology library.
This paper takes a certain type of airborne radar
software system as an example, uses protégé to edit the
SHIEPs and their instances, and further introduces theapplications of software FMEA (SFMEA) based on theabove work.

5.1 Introduction of radar systems

Because SHIEP instances need to use the domain
concepts of radar systems, the functional principle ofradar systems is first introduced. Radar is a system thatuses radio waves to find the position and movement ofobjects. Its basic task is discovering the interested objects,
measuring the state parameters of distance, direction, andspeed, etc. And it is mainly composed of an antenna, atransmitter, a receiver, a signal processor, a data processor,
an indicator, and a display, as shown in Fig. 13 [21].

1294transmitter
antennareceiversignalprocessor

data
processor
indicator anddisplay

Fig. 13. Composition of radar systems

5.2 Ontology edit of SHIEP and SHIEP
instances

Protégé is an extensible, cross-platform and open
source development environment for generating andediting ontologies. It has been widely used and promoted
in many countries. Protégé is one of the most popular
ontology editing tools currently, capable of definingclasses and class hierarchies, attribute relationships andattribute-value constraints, and the relationships between
classes and attributes. Instance tags can be used to obtain
the instances of classes defined in an ontology. This paper
adopts Protégé to edit ontologies. Specifically, firstestablish an SHIEP class, and then establish its constituent
component classes. For the sake of brevity, this paper only
gives the three core component classes of SHIEPs:“Scenario” class, “ErrorManifestation” class and“Solution” class. Fig. 14 shows that an SHIEP classrelates to its constituent component classes by an object
property “isPartOf”. After the completion of ontology
editing, the description logic (DL) query in Protégé can beused for class queries or simple reasoning, which is notrepeated here.

Fig. 14. Object property “isPartOf”

Experience has shown that the number of the failures
based on SHIEs of radar systems found in reliability tests
or in field usage is limited, far less than traditional failures.
Therefore, the case study of SFMEA given below does
not distinguish specific types of radar, but analyzes a
specific type of error related to a certain type of stress,
trying to reveal the effect of such stress on the SHIEs andthe failures based on the SHIEs of radar systems.

5.3 FMEA based on SHIEP
1 System definition
1) Analyzing functions
The functions of the radar sub-system of a certain typeof mission electronic system are to detect a target andmeasure the state parameters of distance, direction, andspeed. A functional schematic diagram is shown in Fig. 15.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

The dashed box of the “servo” in the figure indicates that
this part is not available in all radar. For example, phasedarray radar (PAR) does not have a servo system.
transmits
signals

antenna 10transmitter20

radiates
signals

drivesreceiver 30

controls

controller 60

controls
filters, amplifies,
demodulates

servo 70

controls

controls

signalprocessor 40

sends thedetection
data
informationcollected at one processor 50
time
sends complete
target trajectories

indicator anddisplay 80

transmits and receives electromagnetic waves

antenna 10

provides a high-power RF signal with a modulated
carrier wave for the radar, and radiates it by 10
through a feeder and a transceiver switchtransmitter 20

separates the weak high-frequency signals received on 10
from noise and interference through appropriate filtering,and sends them to the radar back-end equipment 40 and 50
for further processing after amplification and detection

receiver 30

gives the detection information collected at one
time of the echo data from 30, including theinformation of distance, speed, azimuth, etc.

signalprocessor 40

according to the target information collected
multiple times by 40, a complete trajectory of thetarget movement over time is formed

data processor
50

implements radar resource scheduling. coordinates
the cooperative work between 20, 30, 40, and 50 in
the radar system; coordinates the cooperative work
between the radar system and other systems

controller 60

drives 10 motion by electricity

servo 70

(functional part)(structural part)detectionof target
information

Fig. 15. Functional schematic diagram of radar sub-system of a certain
type of mission electronic system

2) Drawing a block diagram• Drawing a functional block diagram: thecorrespondence diagram of function level and structurelevel of a certain type of radar is shown in Fig. 16.
2 Indenture levels
As shown in Fig. 18, an initial indenture level is“aircraft”; an indenture level is “radar system”; the lowest
indenture levels are “antenna (10)”, “transmitter(20)”, ……, “controller (60)”, etc.
3 Severity definition
The systems on an aircraft can be divided into two
categories: (1) the systems that perform basic flightfunctions, e.g., power supplies, flying control (FC)systems, etc.; (2) the systems that perform specific tasks,e.g., mission electronic systems including radar systems
and electronic countermeasures, etc. Although systems

equipment

system1295

Fig. 16. Correspondence diagram of function level and structure level ofa certain type of radar• Drawing a mission reliability block diagram: themission reliability block diagram of a certain type of radarsystem is shown in Fig. 17.
transmitter(20)antenna(10)receiver(30)signal processor(40)

initial indenturelevel

controller (60)that perform specific missions generally do not affectbasic flight functions, they have a decisive effect on
operational effectiveness and affect the reliability andsafety of aircraft systems. Therefore, the definition ofseverity is given based on combat effectiveness and basic
usage as shown in Table. II.
subsystem

devicecomponent

antennatransmitterradar system

aircraftdata processor(50)

Fig. 17. Mission reliability block diagram of a certain type of radarsystemindenturelevel

missionelectronicsystemradar
systemelectronic
countermeasure

receiveravionics
systemsignalprocessor
data
processor

othersystems
lowest indenturelevelscontroller

Fig. 18. An example of indenture level division of a certain type of mission electric systemmonolithic bipolar and
MOS analog circuit

memorycircuitICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

TABLE IITYPES AND DEFINITIONS OF SEVERITY
Types
Definitions
Mission failures cause a complete loss of combat
Type Ⅰ
effectiveness, and may endanger the safety of(hazardous)
personnel or aircraft.
The decrease in mission completion effect causes
the decline in operational effectiveness (such as
flight delay, suspension or cancellation of flight,
Type Ⅱdeclined flight quality, increased landing(moderate)difficulty, moderate environmental damage,etc.), and may cause moderate injury to personnel
or moderate damage to systems.
No effect or little effect on operational
Type Ⅲeffectiveness, but may increase unplanned(minor)maintenance or repair.

4 Failure mode analysis
The failure modes based on the SHIEPs of a certain
type of radar system are mainly obtained from relevant
information analysis. The occurrence probability levels of
failure modes based on SHIEPs are divided into fivelevels: A, B, C, D, and E. Table. III shows the specificdefinitions.
TABLE III
OCCURRENCE PROBABILITY LEVELS OF FAILURE MODES
Severity levels*
OccurrenceⅠⅡprobability levels
Ⅲ(slight)(catastrophic)(moderate)A(frequently)
1
5
13
B(sometimes)
2
7
16
C(occasionally)4818
D(rarely)81219
E(nearly impossible)
121620
Note: the shadow part of table should be paid special attention to. The
smaller the value is, the greater the risk.

5 FMEA table filling
The software/hardware integrated FMEA method is anextension of traditional FMEA methods. A risk index
table and a risk level table are shown in Table. IV and
Table. V, respectively.
TABLE IV
DEFINITIONS OF RISK INDEX
LevelsDefinitions

A
B
C
D

frequently
sometimes
occasionallyrarelynearlyimpossible

E

Occurrence
probability
characteristics
very high probability
medium probability
relatively low
lowvery low

Ocurrenceprobability P(during productusage time)≥ 20%
10% ≤ P < 20%
1% ≤ P < 10%0.1% ≤ P < 1%
≤ 0.1%TABLE VTYPES OF RISK LEVEL
Risk evaluation indexes
Risk levels
Evaluation criteria
1-5very highunacceptable
6-9
high
unexpected (generally notaccepted)

10-17
18-20

moderate
low

1296

acceptable (need to review)acceptable

According to the specific analysis of this case, thesoftware/hardware integrated FMEA table of a certain
type of radar system is shown in Table. VI.
Traditional software testing methods have limitations
in detecting the SHIEPs. For type 1, type 2 and type 3 of
SHIEPs given in section 4, software testing usuallyassumes that a hardware platform on which it operates has
always been in an ideal state, without considering theactual environmental conditions or workload of the
product, which may change the hardware characteristics
or performance of the product, and eventually affectsoftware operations. Software problems caused indirectly
by environmental stresses, e.g., vibration stresses ortemperature stresses, need to be detected by the hardware
environment on which reliability tests operate. This also
shows the limitations of software as a downstream
product that must be attached to hardware.
Take a failure instance detected in domain usage as an
example: this failure is caused by the error that the delay
error of a delay device increases at a low temperature. The
delay error causes the timing signals unmatched, resulting
in the wrong signal value received by software, andfurther causes the operation error of the software, whichacts on system hardware and eventually causes the system
to malfunction. Although the direct cause is a design
problem, the root cause is that the changes in component
characteristics under extreme temperature conditions are
not considered in the requirements analysis stage. Afterthe detailed analysis of the problem, the problem-related
knowledge can be incorporated into the ontology libraryas new knowledge. The scenario where this error occurs is:“Environment→
AtmosphereEnvironment→
ExtremeTemperature” + “interaction between softwareand hardware”. Similarly, there are errors that occur under
extreme vibration conditions or external interference
represented by EMI, which are not listed here.
In short, based on the collected SHIEPs and theirinstances, we can elicit the corresponding scenarios anderror manifestations, analyze solutions, further guide theimplementation of FMEA method, identify all possible
SHIEPs, analyze the causes and possible effects of
SHIEPs, determine the severity and risk of SHIEPs,
eliminate or control the dangerous failures ofsafety-critical products, and formulate effective
improvement measures to improve product reliability andquality.
VI. CONCLUSION
This paper first analyzed software error lifetime fromthe perspective of knowledge-based software engineeringand proposed an error generation model. Then, it
proposed the concepts of SEP and SREP based on the
ODC. According to an error generation mechanism, four
typ es o f SHIEP in th e requ ir em en t stage, an dICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020

1297

TABLE VI
SHIEP-BASED FMEA TABLE OF A CERTAIN TYPE OF RADAR SYSTEM
Initial indenture level: aircraft

Analyze: XXX

Indenture level: radar system

Check: XXX

Approve: XXX
Date: XX, XX, XXXX

Codes

Identifiers of
product(function)

Functions

SHIEPs

Local

Higher level

Final

10

antennatransmits/receives electromagnetic
waves

type
1

the chip of control antenna
enters a power saving mode
under low temperature
conditionsa chip enters thepower-saving mode under
low temperature conditions
and does not communicate
with a servo system

no pitchdisplay on thescreen20transmitter

provides a high-power RF signal
with a modulated carrier wave forthe radarthe screen
does notshow thebuilt-in test(BIT) processof antenna

-

-

-

-

causes mismatch between
related timing pulses,
resulting in timing waveform
distortion

using thetiming signal
to control thedatatransmissionof receiving
channelcausestransmission
dislocation

the failure of an external
component causes the othercomponent to dissipate
insufficient heat, and thetemperature rise changes thedelay time of the delay device

causes a data
conflict

Mechanisms

30

40

50

60

receiver

Separates the weak high-frequency
signals from noise and interference
through appropriate filtering, andsends them to radar back-end
equipment after amplification anddetection

type
1

the error of a delay
component (a linear
integrated circuit) increases
under low temperature
conditions

type1

the delay time of a delay
component (a linear
integrated circuit) changes
with temperature rise.

--signal processor

gives the detection informationcollected at one time of the echodata from the receiver, including
the information of distance, speed,
azimuth, etc.

data processor

according to the target informationcollected multiple times by thesignal processor, a complete
trajectory of the target movement
over time is formed.

type1

controller

implements radar resourcescheduling. coordinates thecooperative work between thetransmitter, the receiver, the signal
processor, and the data processor
in the radar system; coordinates
the cooperative work between theradar system and other systems.

-

Missionphases andwork modes

Failure causes

Stressestemperaturestressdetection oftargetinformation

Severitytypes

OP levels of
SHIEPsREI

Failuredetection
methods

Improvement
measurestype Ⅲ

C

18

visionadd a pull-up
resistor to the
chip's read-write
end

-

-

-

-

-

-

causes theradar cannot
track the target
under lowtemperature
conditions

typeⅠ

C

2

visionuses a software
delay to replacea hardwaredelay

causes thesoftware to failand further
causes themission to fail

typeⅠ

C

4

visionstrengthens
component
screening andimproves
component
reliability

-

-

-

-

Failure effects

-

-

-

data processing bus
arbitration timing is closer tothe critical value under low
temperature conditions

reports a flight failure, amaintenance failure, no echo
signal on the picture under animaging state

reports a dataprocessor
failure, atransmitter
failure, areceiverfailurecannotintercept atarget during
an aerial
combat

typeⅠ

B

2

vision

-

-

-

-

-

-

-

-

improves thealgorithm
design of dataprocessing bus
arbitration
timing-Notes:
REI: Risk evaluation indexes; OP levels of SHIEPs: Occurrence probability levels of SHIEPs

corresponding ontology representation were given. The
results showed that the prior information based on the
SHIEPs was helpful to discover potential failures andfailure modes that may adversely affect the function or
performance of SISs. Therefore, the proposed SHIEP wasof great significance for improving the quality of softwaredevelopment and verification. However, the current
application scope of this method is relatively limited, andthe experimental results obtained are still lacking inquantity. This is because the construction of ontology is atime-consuming and laborious task, requiring theparticipation of domain experts, software engineers, etc.
On the other hand, the collection of error data is also a
long-term task, especially for today’s increasingly
complex software systems, there are many reasons forsoftware errors. Moreover, there are constantly new errors
and their causes with the development of newtechnologies. However, once the ontology is built, it willcontain rich experience and domain knowledge, which
provides the possibility for the reuse of data resources, aswell as the possibility of resource conservation andefficiency improvement. In addition, an increasingly
complete SEP set also lays the foundation for acomprehensive understanding of software systems toensure their quality.
The defect analysis method of ODC is of greatsignificance and has a profound impact on the software
industry. How to use the ODC method more reasonably
and effectively under the development trend of large,
open, dynamic and non-deterministic software systems
will be a continuous research topic.

REFERENCES[1]

M. R. Lyu, “Handbook of software reliability engineering,” Los
Alamitos, California: McGraw-Hill Companies, 1996.
[2] R. Chillarege, Bhandari, I.S., Chaar, J.K., Halliday, M.J., Moebus,
D.S., Ray, B.K., Wong, M.-Y., “Orthogonal defect classification: aconcept for inprocess measurements,” IEEE Transactions on
Software Engineering, 18(11), pp. 943-956, 1992.
[3] R. Chillarege, “Test and development process retrospective: a casestudy using ODC triggers,” in Proc. International Conference on
DSN’02. Washington, DC, USA, 2002, pp. 669-678.
[4] R. Chillarege, “Understanding Bohr-Mandel bugs through ODCTriggers and a case study with empirical estimations of their field
proportion,” in Proc. 3th International WoSAR’11. Washington,
DC, USA, 2011, pp. 7-13.
[5] Lutz, R.R., Mikulski, I.C., “Empirical analysis of safety-criticalanomalies during operations,” IEEE Transactions on Software
Engineering, 30(3), pp. 172-180, 2004.
[6] Nuno Silva etc., “Experience report: orthogonal classification ofsafety critical issues,” in Proc. IEEE 25th International
Symposium on Software Reliability Engineering, Naples, Italy,
2014, pp. 156-166.
[7] Raymond A. Paul, et al., “Defect-based reliability analysis formission-critical software,” in Proc. 24th Annual International
Computer Software and Applications Conference, Taipei, Taiwan,
2000, pp. 439-444.
[8] Pan Tiejun etc., “Defect tracing system based on orthogonal defectclassification,” in Proc. 2008 International Conference on
Computer Science and Software Engineering, Wuhan, China, 2008,
pp. 574-577.
[9] LiGuo Huang, et al., “AutoODC: automated generation oforthogonal defect classifications,” Automated Software
Engineering, Springer, 22(1), pp. 3-46, 2015.
[10] Ferdian Thung, et al., “Automatic defect categorization,” in Proc.
19th Working Conference on Reverse Engineering, Kingston,Ontario, Canada, 2012, pp. 205-214.
[11] Ruili Geng, et al., “In-process usability problem classification,
analysis and improvement,” in Proc. 14th International
Conference on Quality Software, Dallas, TX, USA, 2014, pp.
240-245.
[12] Martin Wirsing, “Beyond the horizon-final report, thematic group
6 on software intensive systems,” June 2006, pp: 1-39.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 3, May 2020[13] T. R. Gruber, ‘‘Toward principles for the design of ontologies used
for knowledge sharing?’’ Int. J. Hum.-Comput. Stud., vol. 43, no.
5-6, pp. 907-928, Nov. 1995.
[14] Renzuo Xu, “The testing method based on software knowledge,” J.
Wuhan Univ. (Nat. Sci. Ed.), 46(1), pp. 61-62, 2000.
[15] Xuan Hu, Bin Liu, Yichen Wang and Chongwu Jiang,“Application of software requirement error pattern based on
ontology,” Journal of Beijing University of Aeronautics andAstronautics, vol. 35, no. 6, pp. 723-727, 2009.
[16] C. Alexander, “The timeless way of building,” UK: Oxford
University Press, 1979, pp. 5-20.
[17] Xizi Huang, “Software reliability, safety and quality assurance,”
Beijing: Publishing House of Electronics Industry, 2002.
[18] Zhijin, Linliu, Yingjin, “Software requirements engineering
method,” Beijing: Science Press, 2008.
[19] Ruqian Lu, et al., “Ontology-oriented requirements analysis,”
Journal of Software, 11(8), pp. 1009-1017, 2000.
[20] Jon G. Hall, Ivan Mistrík, Bashar Nuseibeh, Dr Andrés Silva,“Editorial relating software requirements and architectures,” IEE
Proc.-Softw., 152(4), pp. 141-142, 2005.
[21] Lufei Ding, Fulu Geng, Jianchun Chen, “Radar Principles,”Beijing: Publishing House of Electronics Industry, 2009.

Xuan Hu was born in 1983. She received the Ph.D.
degree in aerospace science and technology from
Beihang University, China, in 2010. She is currently asenior engineer of CEPREI Laboratory. Her main
research interests include ontology modeling, software
requirement engineering, and software reliability.

Jie Liu was born in 1963. He received the B.S. degree incomputer science from Xidian University, China. He iscurrently the director of the Information Security ResearchCenter of CEPREI Laboratory. His main research interests
include information security, software reliability andsecurity, and software evaluation technology.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020

1299

Estimation of Network Type Based on The
Response Delay Property
Rui Teng, Kazuto Yano, and Yoshinori Suzuki
Wave Engineering Laboratories, Advanced Telecommunications Research Institute International
Kyoto 619-0288, Japan{teng; kzyano; yoshinori.suzuki}@atr.jp
Abstract—The large number of Internet of things (IoT)
terminals increases the cost and difficulty of IoT network
management such as network scan. Some wireless IoT networks
that have low transmission data rate especially require low
overhead for network management to avoid interruption.
Hence, it is important to make automatic identification of
network type for effective network management. In this study,
we at first carry out laboratory experiments of network scan towireless terminals in wireless LAN (WLAN), Long Term
Evolution (LTE) and Low Power, Wide Area (LPWA) networks
that include Wi-SUN and LoRa network. From the result of
response delay obtained from each network in the experiments,
we then examine the basic property of the response delay,
especially about the minimum response delay. Finally, we
propose a scheme of network-type estimation based on theproperty of the minimum delay in different networks. The
performance of the proposed scheme of network-type
estimation is evaluated regarding the network objects of WLAN,
LTE and LPWA networks. The evaluation results show that thescan response delay is a useful factor for successfully identifying
different types of IoT networks.
Keyword—Internet of things, response delay, network
estimation

W

I. INTRODUCTION

ith the advances in Internet of things (IoT)
technologies, the number of IoT terminals is rapidly
increasing. In addition to conventional communication
networks, a number of IoT networks emerge in recent years
to meet requirements of IoT use on energy efficiency,
long-time operation, and large coverage. For instance, Low
Power, Wide Area networks (LPWANs) provide wireless———————————————————————
Manuscript received on Jan. 20, 2020. This work is a result of the project
entitled “Research and Development of Technologies of Efficient Network
Scanning for Wide-Area IoT Wireless Networks,” which is supported by the
Ministry of Internal Affairs and Communications as part of the research
program “R&D for Expansion of Radio Wave Resources (JPJ000254).” This
paper is a follow-up of the invited journal to the presented paper entitled“Identification of IoT Network Type Based on The Response Delay
Property” of the 22th International Conference on Advanced
Communication Technology (ICACT2020).
Rui Teng is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (corresponding
author, email: teng@atr.jp)
Kazuto Yano is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (email:kzyano@atr.jp)
Yoshinori Suzuki is with the Wave Engineering Laboratories, Advanced
Telecommunications Research Institute International, Japan. (email:yoshinori.suzuki@atr.jp)

communication with large coverage with low power
consumption. In LPWANs, there are diverse network types,
includes Wi-SUN and LoRa, each of which has its
specification in communication [1], [2]. In this paper, theterm of IoT networks refers to the broad scope networks thatinvolve conventional networks and LPWANs because both
networks are employed for operating IoT applications.
For the large number of networked IoT terminals, efficient
network management is important for reliable and safe
communication. Network scan, including TCP port scan andping-based scan using Internet Control Message Protocol(ICMP), is an important way to detect the network
connection status and vulnerable terminals [3–6]. In a scan
event, a scanner probes the target network terminals toacquire information such as the port usage state as well as the
response delay. Those kinds of information is helpful for
network administrator to monitor network congestion status
and to detect the vulnerable IoT terminals.
Due to the large population of IoT terminals, the network
management employing network scan may incur large traffic
overhead that causes the interruption on IoT networks andoperation of IoT terminals. The impact of network scantraffic on IoT networks differs according to the types of IoT
networks that have different amounts of available
communication resources, transmission data rates, andallowable energy consumption.
Hence, automatic identification of IoT network type is a
key technology to realize the successful network scan.
Identification of the network type assists the scheduling of
scan and to alleviate interruption to the IoT networks. It also
helps the estimation of the underlying causes of network
malfunctions and failure of scan events [7].
The studies of identification of IoT devices have been
introduced in reference in [8–9]. The methods include
classifying the IoT devices based on TCP port status or
network traffic from the devices. On the other hand, thesestudies have not addressed how to identify the network typeof IoT devices. In reference [5], we introduced a preliminary
study about the estimation of network types. However, thepaper focuses on examining only the object networks of
LPWAN and wired LAN. Moreover, the network estimation
method is limited to use the information of the average
response delay of the known IoT networks.
This paper is an extended version of the reference [6].
From the reference [6], we extend the network objects for
examination by including Long Term Evolution (LTE)
network in addition to WLAN and LPWA networks. First,

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020

the properties of the network LTE network are measured andadded to the set of delay properties regarding minimum delayof each network type. Furthermore, due to the increase of thenumber of objects to estimate, the evaluation of network
estimation is re-conducted according to the new
configuration of the set of minimum delay at object networks.
Consequently, in the evaluation results, we show theperformance of network-type estimation from theresponse-delay sample for WLAN, LTE, Wi-SUN and LoRa
network.
In this paper, to enable network type estimation, we at firstexamine the response delay property of IoT networks that
include wireless LAN (WLAN), LTE, and LPWAN withlaboratory experiment. We propose a network estimation
method utilizing the minimum response delay of each IoT
network. We also carry out the comparison with the average
delay based method [5] regarding the correctness of network
type judgement. The evaluation results show that the
proposed method is able to successfully identify the network
type from the response delay of scan events.
The rest of this paper is organized as follows. Section II
introduces the system model. Section III describes theproperty of response delay in IoT networks. Section IV
describes the proposed method of network-type estimationand evaluation results. Section V concludes this article.
II. SYSTEM OVERVIEW
The network scan system utilizes a network scanner tomeasure the status of various IoT terminals. An example ofthe scan system is illustrated in Fig. 1. We consider several
target wireless networks such as WLAN, LTE and LPWANs
including Wi-SUN and LoRa network. To assess the status of
IoT terminals, the scanner probes the IoT network by issuinga series of scan events to the IoT terminals. (In this paper, theterms of device and terminal have the same meaning.) A scan
event in this paper refers to an ICMP ping operation. That is,
in a scan event, the scanner issues a ping packet that traverses
between the scanner and a target IoT terminal.
The network scan system measures the round trip time(RTT) spent in the target access network (ARTT) [5]. ARTT
may have different properties according to the network type.
Hence, the ARTT information will be used for network typeestimation. In the following parts of this paper, the term of
response delay refers to the ARTT in a scan event. The
network estimation can be expressed as follows. Suppose that
there are K network types to which the scanner monitors andperforms network scan. The scan system tries to estimate the
network type from the response delay data in terms of the
ARTT.
The study of network type estimation introduced in thispaper consists of two parts. (1) Preliminary investigation of
the ARTT property with experimental measurement,
especially with a focus on the minimum response delay ofeach network type. Measurement of the response delay inscan events is carried out by experiment on WLAN, LTE, and
LPWA networks including Wi-SUN and LoRa network; wethen investigate the characteristics of the response delay,
especially the minimum response delay, from the results
obtained through the experiments. (2) Estimation of the
network type according to the response delay property of theminimum response delay in each network.

1300

Fig. 1. An example of network scan system.

III. THE MEASUREMENT OF RESPONSE DELAY IN
IOT NETWORKS
A. Measurement of Response Delay Property
We examine the properties of response delay of network
scan in WLAN, LTE, Wi-SUN and LoRa network. These
properties include the minimum, average and maximum of
ARTTs, as well as the empirical probability distributionfunction (PDF) of ARTT result in each network type.
Although the laboratory experiments on measurement of
ARTT in different networks have constraints regarding to thescale of networks and diversity of environment, such basic
experiments provide the basic properties of ARTT in eachobject networks.
The scenario of WLAN experiment setup is shown in Fig.
2. The network consists of a scanner, a WLAN access point(AP) and the terminal. The connection between the AP and
the terminal is IEEE 802.11b/g/n WLAN in 2.4 GHz band.
The connection between the scanner and the AP is by
Ethernet.
The scenario of measurement in LTE simulator testbed isshown by Fig. 3. The experiment involves a scanner, an LTE
BS simulator MD8475B and a LTE-handset terminal. The
link between the scanner and the BS simulator is Ethernet.
The link between the BS simulator and the terminal is LTE
based wireless connection using Band 21 (1.5 GHz).
The scenario of experiments in Wi-SUN or LoRa network
is shown by Fig. 4. The experiment involves a scanner, an
LPWA access point (AP) and terminals of Wi-SUN and
LoRa. The link between the scanner and the AP is Ethernet.
The link between the AP and the terminal is wirelessconnection of Wi-SUN and LoRa network in a channel of
920 MHz band.
In the experiment, a series of scan events is issued to each
network to measure the response delay. The interval between
scan events in WLAN, LTE, and Wi-SUN is set to one
second. While the interval between scan event in LoRa is set
to 5 seconds due to the low data rate of LoRa. We measure
response delay RTT, which is used as ARTT because thetransmission delay in the wired network is much smaller thanthat in the wireless networks. Then, we construct the property
database of each network type from the collected informationof 500 samples of response delay in each network.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020

Fig. 2. Experimental scenario of WLAN.

The response delay in LTE is distributed between 19.28 ms
and 137.95 ms. The average delay of scan evens in LTE is
24.48 ms. The response delay in Wi-SUN is distributed
between 180 and 738 ms. The average delay in Wi-SUN is460.4 ms, which is much larger than those in other networks.
The response delay in the LoRa network is distributed
between 3808 and 9047 ms. The average delay in the LoRa
network is 5250 ms, which is much larger than those in
WLAN, LTE and Wi-SUN.

Fig. 3. Experimental scenario of LTE.

Fig. 6. The empirical PDF of response delay in WLAN.

Fig. 4. Experimental scenario of LPWA network: Wi-SUN and LoRa
network.
Fig. 7. The empirical PDF of response delay in LTE.

Fig. 5. Response delay in WLAN, LTE, Wi-SUN, and LoRa network.

B. Property of the Response Delay in Different Networks
Fig. 5 illustrates measured samples of response delay in theexperiment of WLAN, LTE, Wi-SUN and LoRa network.
The response delay in WLAN is distributed between 0 and 84
ms. The average delay of scan evens in WLAN is 2.16 ms.

1301

Fig. 8. The empirical PDF of response delay in Wi-SUN.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020

1302

TABLE IESTIMATION RESULT FROM THE RESPONSE DELAY DATA OF WLAN, LTE, WI-SUN AND LORA ACCORDING TO THE MINIMUM
DELAY PROPERTY OF EACH ETWORK TYPE. (FROM 200 OBSERVED SAMPLES FROM EACH NETWORK TYPES.)TABLE IIESTIMATION RESULT FROM THE RESPONSE DELAY DATA OF WLAN, LTE, WI-SUN AND LORA ACCORDING TO THE AVERAGE
DELAY PROPERTY OF EACH ETWORK TYPE. (FROM 200 OBSERVED SAMPLES FROM EACH NETWORK TYPES.)

Fig. 9. The empirical PDF of response delay in LoRa network.

information is obtained by the preliminary experimental
measurement in each object IoT network. Let DistMin[x][i]stand for the distance between a result of response delay D[i]to DMin[x], as illustrated in Fig. 10 that assumes K is 5.
As shown in Algorithm 1, the proposed method estimates
network type according to DistMin[x][i]. For each result of
response delay regarding the i-th scan event, the scan systemcomputes the DistMin[x][i] corresponding to all network
types in NA by DistMin[x][i] = |D[i]-DMin[x]|, as shown in
Fig. 10.
As a consequence, the scan system collects a set ofDistMin[x][i], x is a member in NA. Let Z be the network
type that gives the least DistMin[x][i]; x is a member in NA.
Then, Z is the result of network estimation. (For multiple
networks that has the same least DistMin[x][i], the estimation
result can be set to unknown or either of the network types
that gives the least DistMin[X][i] for X in NA).
B. Evaluation of network estimation
We examine the effectiveness of the proposed method byutilizing the measurement results obtained from theexperiment scenarios introduced in Section III. The
minimum response delay of each object network is obtained

Fig. 10. Distance to the minimum response delay (K = 5).

IV. METHOD OF NETWORK-TYPE ESTIMATIONBASED ON THE PROPERTY OF THE MINIMUMRESPONSE DELAY
A. Network Type Estimation Scheme
Suppose that there are K types of the object networks, towhich the scan events are issued. We denote the set of object
networks to be examined by NA = {N1, N2, …, NK}. For an
object network x in NA, the minimum response delay isdenoted by DMin[x], where x is a member in NA. A set ofminimum delays corresponding to all network types isdenoted by {DMin[N1],…, DMin[NK]}. This minimum delay

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020

from 500-sample data of scan events. Network typeestimation is carried out from response delay results in 200
scan events in each object networks. These result data belong
to the 500-sample data of scan events from which theminimum response delay is derived, while each of which are
independent to each other. The results of network typeestimation from the response delay information in WLAN,
LTE, Wi-SUN and LoRa network are shown in Table I. The
proposed method is able to successfully identify WLAN,
LTE, Wi-SUN and LoRa in all judgements. We also carry out
the network estimation based on the property of average
response delay to make a comparison with the proposed
method [5]. The result of network type estimation based on
average delay property is shown in Table II.
We can find the proposed method have a higher successrate than that the method based on average delay property.
The largest number of failed judgements in the proposed
method is 3 in 200 judgements for LTE. While the method
utilizing the average delay property fails 7 times in 200
estimation tests for Wi-SUN. We consider the reason why the
proposed method shows better performance is the minimum
response delay reflects the distinct network property much
more than that of average response delay with respect to the
distribution of response delay.

1303

Fig. 11, Fig. 12, Fig. 13, and Fig. 14 show the distances
between the response delay (D[i]) of each scan event and theminimum response delay of each network type obtained in
the learning (DMin[x]). Fig. 11 illustrates that the responsedelay in WLAN is closest to the minimum response delay of
WLAN, with the distance ranging from 0.075 ms to 82.66 ms.
Fig. 12 illustrates that the response delay in LTE is closest to
the minimum response delay of LTE, with the distance
ranging from 0.3 ms to 118.67 ms. Fig. 13 illustrates that the
response delay in Wi-SUN is closest to the minimum
response delay of Wi-SUN, with the distance ranging from
10 ms to 554 ms. Fig. 14 illustrates that the response delay in
LoRa network is closest to the minimum response delay of
LoRa network, with the distance ranging from 34 ms to 5239
ms.

Fig. 13. Wi-SUN case: distances between the response delay (D[i]) of eachscan event in Wi-SUN and the minimum response delay of each network
type (DMin[x]).

Fig. 11. WLAN case: distances between the response delay (D[i]) of eachscan event in WLAN and the minimum response delay of each network type
(DMin[x]).

Fig. 14. LoRa network case: distances between the response delay (D[i]) of
each scan event in LoRa network and the minimum response delay of each
network type (DMin[x]).

V. CONCLUSION

Fig. 12. LTE case: distances between the response delay (D[i]) of each scan
event in LTE and the minimum response delay of each network type
(DMin[x]).

In this paper, we investigated the basic properties of theminimum response delay in the network scan in WLAN, LTE,
Wi-SUN and LoRa network. By laboratory experiments, wefound that each network has quit different distribution of
response delay. We then proposed a method to estimate
network type from response delay. The proposed method
determines the network type whose minimum response delay

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 4, July 2020is nearest to the instantaneous one of the target scan events.
The effectiveness of proposed method was verified by using
experimental results of response delay and the network
property of the minimum response delay. The evaluation
results of network estimation showed that the proposed
method successfully identifies the three object networks of
WLAN, LTE, Wi-SUN and LoRa network in scan events.
ACKNOWLEDGMENT
The authors would like to thank Mr. Naoto Egashira, Mr.
Shirayuki Araki, and Mr. Susumu Ano for their support tothis work.
REFERENCES
[1]

[2]
[3]

[4]

[5]

[6]

[7]

[8]
[9]

H. Harada, K. Mizutani, J. Fujikawa, K. Mochizuki, K. Obata, and R.
Okumura, “IEEE 802.15.4g Based Wi-SUN Communication
Systems,” IEICE Trans. Commun., vol.E-100B, no 7, pp.1032–1043,
July 2017.
F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. MeliaSegui and T. Watteyne, “Understanding the Limits of LoRaWAN,”
IEEE Commun. Mag., vol.55, Issue 9, pp.34–40, Sept. 2017.
K. Yano, N. Egashira, T. Kurihara, S. Shimizu, and Y. Suzuki,“Research and Development on Wide Area Network Scan Techniques
for IoT Wireless Equipment,” IEICE Tech. Rep., NS2019-85,
pp.203–208, July 2019.
R. Das, N. Baranasuriya, V. Padmanabhan, C. Rodbro, and S. Gilbert“Informed Bandwidth Adaptation in Wi-Fi Networks using Ping-Pair,”in Proc. CoNEXT 2017, pp.376–388, Dec. 2017.
R. Teng, S. Araki, K. Yano, and Y. Suzuki, “Statistical Study on
Response Delay Characteristics of Network Scan to IoT Devices – The
Base for Estimation of IoT Device Attributes,” IEICE Tech. Rep.,
NS2019-142, pp.45–49, Dec. 2019.
R. Teng, K. Yano, and Y. Suzuki, “Identification of IoT Network Type
Based on The Response Delay Property,” in Proc. IEEE 22ndInternational Conference on Advanced CommunicationsTechnology(ICACT2020), Pyeongchang, Korea, pp. 319–324, Feb. 2020.
B. Ojetunde, N. Egashira, T. Kurihara, K. Yano and Y. Suzuki, “Factor
Discrimination of Scanning Failure and Delay for Efficient Network
Scanning of IoT Wireless Equipment,” in Proc. CCNC 2020, pp. 1-2,
Jan. 2020.
A. Sivanathan, H. Gharakheili, and V. Sivaraman, “Can we classify an
iot device using tcp port scan?” in Proc. IEEE ICIAFS, pp.1–4, Dec.
2018.
Y. Meidan, M. Bohadana, A. Shabtai, J. D. Guarnizo, M. Ochoa, N. O.
Tippenhauer, and Y. Elovici, “Profiliot: a machine learning approach
for iot device identification based on network traffic analysis,” in Proc.
the Symposium on Applied Computing, pp. 506–509, April 2017.

Rui Teng received the Ph.D. degree in Information and Communication
Engineering from the University of Tokyo. He was a senior researcher in
National Institute of Information and Communications Technology, Japan.
He is now with the Advanced Telecommunications Research InstituteInternational (ATR). His research interests include wireless LAN, wirelesssensor networks, and Internet of things. He received Bionetic2010 Best
Paper Award, IEEE WF-IoT 2018 Best Paper Award - First Runner-Up, andIEEE ICACT2020 Outstanding Paper Award. He is a member of IEEE and
IEICE.
Kazuto Yano received the B.E. degree in electrical and electronicengineering, and the M.S. and Ph.D. degrees in communications andcomputer engineering from Kyoto University in 2000, 2002, and 2005,
respectively. He was a research fellow at the Japan Society for the Promotionof Science (JSPS) from 2004 to 2006. In 2006, he joined the Advanced
Telecommunications Research Institute International (ATR). Currently, he
is a principal researcher at Wave Engineering Laboratories, ATR. His
research interests include space-time signal processing for interference
suppression, MIMO transmission, and PHY/MAC cross-layer design ofwireless communication systems for ISM bands. He received the IEEE VTSJapan 2001 Researcher's Encouragement Award, the IEICE Young
Researcher's Award in 2007, the Ericsson Young Scientist Award 2007 andthe IEICE 2007 Active Research Award in Radio Communication Systems.
He also received 2010 Young Investigator Award in Software Radio fromIEICE Technical Committee on Software Radio. In addition, he received

1304

IEICE Communications Society Best Tutorial Paper Award in 2017, andICAIIC 2019 Excellent Paper Award in 2019. He is a member of IEEE and
IEICE.
Yoshinori Suzuki received the B.E., M.E. and Ph.D. degrees from Tohoku
University, Sendai, in 1993, 1995 and 2005 respectively. He joined NTT
Wireless Systems Laboratories in 1995. Since then, he engaged inresearching microwave signal processing techniques for satellite onboard
applications and onboard multiple beam antenna feed techniques. He worked
as a part-time lecturer at Niigata University in 2012 and 2014. From 2013 to2014, he was in charge of sales engineering of satellite communication
services in NTT Software Corporation (currently NTT TechnoCross
Corporation). Since then, he was a research engineer in NTT AccessNetwork Service Systems Laboratories working on future mobile satellite
communication systems. From June 2018, he has been engaged in the
research of innovative radio communication systems at ATR Wave
Engineering Laboratories, Kyoto, Japan. He received the Younger Engineer
Award from the IEICE Japan in 2003.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

1309

Determination of Subscribers Coordinates using
Flying Network for Emergencies
Truong Duy Dinh*, Vladimir Vishnevsky**, Van Dai Pham*, Duc Tran Le***, Ruslan Kirichek*,**,
Andrey Koucheryavy**Department of Software Engineering and Computer Technology, Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, 22 Prospekt Bolshevikov, St. Petersburg, Russia.
**V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Street,
Moscow, Russia***Information Technology Faculty, The University of Danang - University of Science and Technology, 54
Nguyen Luong Bang, Danang, Vietnam.
duydt.sut@gmail.com, vishn@inbox.ru, fam.vd@spbgut.ru, letranduc@dut.udn.vn,
kirichek@sut.ru, akouch@mail.ru

Abstract— In the last decade, unmanned aerial systems haveemerged in different fields, such as entertainment, military,
Internet of Things, emergencies. In these systems, unmanned
aerial vehicles (UAVs) communicate with each other based on aflying network to provide services to customers such as live
streaming, high-speed access point, etc. The mobility of UAVs
can bring numerous advantages in emergency cases, for example,
rescuing and searching victims in areas where the network
infrastructure is not available. However, the crucial task needed
to be done first is to locate the victims’ coordinates. In this paper,
we propose a method for detecting the coordinates ofsubscribers with the Wi-Fi signals generated from victims’
phones in the absence of network infrastructure ofcommunication operators using a flying network for
emergencies based on UAV-swarms. We develop a new protocol
for communication between UAVs and UAV-swarms. A
structure of UAV-swarms is considered to optimize thesearching time. The results of the proposed method are achievedby simulation.
Keyword — SAR, UAV, Flying Network, positioning, Wi-Fi,
IEEE 802.11p———————————————————————
Manuscript received on Jan. 17, 2020. This work was supported by
RFBR, project number 20-37-70059 and it is also supported by a follow-up
of the invited journal the accepted & presented paper entitled “Positioning
Methods Based on Flying Network for Emergencies” of the 22th
International Conference on Advanced Communication Technology(ICACT2020), and Grant ID is 20200380.
T. D. Dinh, he is a Ph.D student in the Department of Communication
Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia. (email: duydt.sut@gmail.com).
V. Vishnevsky, he is a professor at V.A. Trapeznikov Institute of Control
Sciences of Russian Academy of Sciences, Russia. (email: vishn@inbox.ru).
V. D. Pham, he is a Ph.D student in the Department of Communication
Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia. (email: fam.vd@spbgut.ru).
D. T. Le, he is a professor in Information Technology Faculty, The
University of Danang - University of Science and Technology, Danang,
Vietnam (email: letranduc@dut.udn.vn).
R. Kirichek, he is a professor in the Department of Communication
Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia. (corresponding author, phone:+7-921-970-0160, email: kirichek@sut.ru).
A. Koucheryavy, he is a professor in the Department of Communication
Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia. (email: akouch@mail.ru).

I. INTRODUCTIONE

VERY year, a significant number of people around theworld die as a result of untimely assistance. Examples
include earthquakes, tsunamis, and other natural disastersof natural and artificial origin. In addition, a considerable
number of individuals disappear in forests due to the inability
to contact emergency services. The effectiveness of searching
and rescuing operations can be improved by using new
approaches to detect victims, which are based on using a
UAV group with attachments - a flying network [1], [2]. Sucha network can reduce the time required to locate victims aswell as reduce the number of people involved in theoperations [3], [4].
When a disaster occurs, infrastructure and thecommunication systems in this area are destroyed, and theycannot be able to operate. The rescue team comes to the areaand deploys UAVs to operate missions. UAVs will fly, do thesearching and rescuing operations and establish a quick
deployment flying network to make it possible for rescue
teams on the side to contact each other, as well as victims who
can contact outside for help. The first and most important
thing is to identify the victim's location in the search area, andthanks to the UAVs, searching operations become
time-saving and highly accurate. After a disaster, it's also
challenging to find people, who were buried under debruises,or they were unconscious. There are many ways to determine
the victim's location, but searching through beacon signals
from devices attached to the victims, such as smartphones,
laptops, and tables, which have a Wi-Fi connection, becomes
useful and easy to deploy. To identify the victim's location
through the location of the phone that the victim carries withhim, at least UAV-swarms need to receive three signals [5],[6].
Nowadays, smartphones and smartwatches have become
indispensable things for humans, and almost everyone carries
these devices with them at any time. This factor also is an
essential part of smart cities, smart countries when everythingneeds to be connected seamlessly. In smart cities, a public
Wi-Fi network is always designed for the people in order for
everyone to connect to this network and use the services of theICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

smart city [7]-[9]. Thanks to that, the process of searching andrescuing through finding signals from the victim’s phone also
becomes easier. When UAV-swarms, which have functioned
as an Access Point (AP), use this public Wi-Fi network to
send beacon signals to the victim's phone, the phone will
confirm and respond to the signals that is the basis for
detecting the location of the victim.
Related papers using UAVs to find signals from thevictim’s phone can be found in some studies, such as in [10],[11]. The authors of [10] proposed a system named Krypto,
which addressed the challenges of maximizing the size of thesearching area and minimizing the location errors withdifferent approaches. In this paper, however, the authors did
not consider the UAV-swarm Fixed-Wing type, as well as theinfluence of the distance measurement error and the numberof received signals, and the considered area was small incomparison with our targeted area. In [11], the authors used
the UAV to track indoor smartphones or watches owned by
the victim by sending to the UAV the received signal’s
strength and the UAV location with a shallow Q-learningalgorithm. However, the paper studied only one single UAVwith an indoor search and rescue mission.
In this paper, we propose a method for detecting
subscribers' coordinates with the Wi-Fi signals generated
from victims’ phones in the absence of network infrastructureof communication operators using a flying network for
emergencies based on UAV-swarms. On the basis of thiswork, a draft Recommendation ITU-T Q.ETN-DS“Signalling architecture of the fast deployment emergency
telecommunication network to be used in a natural disaster” isbeing developed in the 11th research commission of thestandardization sector of the international telecommunication
union “Signalling requirements, protocols, test specifications
and combating counterfeit products” (SG11 ITU-T). The
protocol of inter-communication between UAVs in flying
network for emergency systems is present in Section II. The
flying network and deployment models are described in
Section III. The positioning methods and the assessment of
positioning errors are considered in Section IV. The
conclusion of the article is shown in Section V.

II. PROTOCOL OF INTER-COMMUNICATIONBETWEEN UAVS IN FLYING NETWORK FOR
EMERGENCY SYSTEMS
A. Basic statements
In recent years, a group of UAVs working together as a
network has proven to be effective, useful, and flexible. This
network often is called Flying Ad-hoc Networks (FANET),
which is considered a subset of Vehicular Ad-hoc Network(VANET) and Mobile Adhoc Network (MANET). The
development of an autonomous FANET depends on thequality of inter-communication between UAVs. Currently,
however, there is no technology for inter-communication
between UAVs and UAV groups. Since FANET is seen as asubnet of VANET, inter-communication technology in
VANET is one of the great candidates.
The current inter-communication technology in VANET
mainly uses IEEE 802.11p. In the process of developing to
5G, there are new technologies for VANET such as C-V2x,
IEEE 802.11bd, or 5G NR (5G and new radio). However, the1310

C-V2x technology is still in the process of being completed, it
has only been simulated on models, and it works well thanks
to the existing foundation of cellular network, therefore, in thecase of emergency rescue when the network infrastructure is
not working, it cannot be applied. IEEE 802.11bd is a newtechnology that is under development based on IEEE
802.11n/ac, promising to be able to replace and be compatible
with IEEE 802.11p, but there is still a long way to go.
Similarly, C-V2x, 5G NR are future technologies forinter-communication of vehicles, but it is now just in the early
development process [12]-[15].
IEEE 802.11p [16] protocol is a modified version of thefamiliar IEEE 802.11 (Wi-Fi) standard, which was originallydeveloped for VANET. The IEEE 802.11p standard was
adopted as Medium Access Control (MAC) and PhysicalLayer (PHY) specifications for the lower-layer Dedicated
Short-Range Communication standard (DSRC), which has
characteristics such as frequency range - 5.9 GHz (5.85-5.925
GHz), wide coverage (up to 1000 m), fast transmission rate
(up to 27 Mbps), self-organization and fast convergence. Thisapproach is commonly referred to as vehicle-to-any (V2X)
communication. Compared to cellular networks, Wi-Fi-based
V2X communication does not offer a pre-installed
infrastructure and (almost) full spatial coverage. However, its
direct communication capabilities among nodes with shortcommunication latency make it a strong candidate for vehicle
safety and traffic efficiency applications [17].
B. Proposed work
In a flying network for emergencies, which focuses on
searching for victims of natural disasters, UAV-swarms movewith a certain flight mission and have a geographicallimitation. In such a network, UAVs interact with each otherto receive a flight task from the mobile base station (MBS), or
send the collected data to MBS.
In [18], a modified MAC sublayer was proposed based on
the IEEE 802.11p and IEEE 1609.4 protocols forcommunication between UAVs and UAV-swarms. IEEE
1609.4 protocol [19] allowed adding a seven-channel MAC
scheme to the MAC layer of IEEE 802.11p. Each channel has
a total bandwidth of 10 MHz. These channels consist of one
control channel (CCH - Control Channel 178) for managing
the network and transmitting safety messages and six service
channels (SCH - Service Channels 172, 174, 176, 180, 182,
184) for other traffics. All vehicles must monitor the control
channel with safety/control messages during the CCH period
and will be able to switch to the service channel to exchange
only non-safety applications.
The proposed protocol (Fig. 1) includes three different
sub-protocols:• Cluster management protocol (CMP);• Intra-cluster communication protocol (IntraCP);•Intercluster communication protocol (InterCP).
We denote the combination of these protocols asсluster-based Multichannel MAC IEEE 802.11p protocol(CMMpP). In this case, the UAV-swarm is considered as acluster and the UAV as a node. The protocol is focused ondata transmission between mesh network elements, in whichrouting should be performed at the data link layer, or, in otherwords, based on MAC addresses. This is the difference frommobile Ad-hoc networks, in which the focus is on
standardizing the functions of the network layer [26].

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

UAV 2-1
UAV (CH-1)

InterCPUAV 1-1

IntraCPUAV 1-2

UAV (CH-2)

IntraCPUAV 2-3

Fig. 1. UAV-swarms using protocol CMMpP

In the first step of the searching operation, the MBS moves
to the searching zone. Next, UAV-swarms (clusters) are
deployed to collect information, establish communications
and exchange data. These clusters use the CMP protocol toselect a cluster head (CH). The CH node can be reassigned
during the task’s execution depending on the location, and the
speed of nodes in the cluster. The CH node is responsible forcollecting information from cluster members (CM - cluster
member) and subsequent transmission to the MBS or CH
nodes of other clusters. The CH nodes also act as anintermediate for transmission, scheduling, channel
assignment through message management.
Nodes will use IntraCP and InterCP for data transmissions.
These protocols use the IEEE 1609.4 and IEEE 802.11p
multi-channel MAC sublayer in conjunction with TDMA foreach communication session. In particular, the IntraCP
protocol performs the following main tasks:• Collecting/Delivering control messages from/to CM on
CCH channel;• Allocating the available SCH channels to CM for data
traffic;• Contending to transmit data on the SCH in the cluster.
Meanwhile, the InterCP protocol is responsible forestablishing communication between different clusters(transmission of both control messages via CCH and data via
SCH). In the CMMpP protocol, each node is equipped with
two transceivers, designated respectively “Trans_1” and“Trans_2”. They can work simultaneously on different
channels. “Trans_1” is always set to CCH for monitoring andtransmitting control messages on the CCH channel, and“Transmit_2” is set to any SCH channel (channels 174, 176,
180, 182) to perform data transmission. Therefore, instead of
the CCH (CCHI) and SCH (SCHI) intervals of 50 ms for eachtransceiver, the entire synchronization interval of 100 ms canbe used. In addition to using the CCH channel 178 for control
messages, the SCH channel 182 is proposed exclusively forInterCP. Thus, SCH channels 174, 176, 180 will be used toassign to CM nodes for IntraCP transmission data. It should
be noted that only SCH channels 174, 176, 180 can be used
for data transmission in a cluster (IntraCP). However,inter-channel communication (InterCP) will be carried out on
SCH channel 182. These operations occur in non-overlapping
channels, so they do not affect each other.

6000

5000

UAV (CH-1)
4000

UAV 1-1
UAV 1-2

3000

UAV 1-3
UAV 1-4

2000

UAV 1-5
UAV 1-6

1000

UAV 1-7
UAV 1-8

0
0
108
216
324
432
540
648
756
864
972
1080
1188
1296
1404
1512
1620
1728
1836
1944
2052
2160
2268
2376
2484
2592
2700
2808
2916
3024
3132
3240
3348
3456
3564

UAV 1-3

were selected. The data transfer rate was set to 12 Mbit/s, andCH nodes are used as the access points. As for the CCH
simulation, all vehicles must operate at the center frequency
with the channel 178 in the range 5885-5895 MHz. Therefore,
the minimum frequency should be 5885 MHz. Other
parameters are set by default or as in [21].
Four scenarios are evaluated with one central BS, one
MBS, and five clusters, each of which has 1 CH node, and thenumbers of its member nodes (CM) for each scenario are 4, 8,
16, and 32, respectively. The mobility of nodes withtrajectories is determined by the Mobility Config andTrajectory modules. Application and profile configurations
are used to create messages and configure scripts. The
duration of each simulation scenario is 60 minutes, and the
EDCA parameters are set by default. We analyze thetransmission of service messages and voice traffic insimulation.
As an example of these four scenarios, we study fiveclusters, each of which has 8 CM and 1 CH. Fig. 2 shows theaverage throughput for each member of the nodes in one
cluster (Cluster_1) from those five. The average throughputof the CH-1 node is proven to be the highest since it is thehead cluster.
Average Throughput (bits/second)

UAV 2-2

Time (second)Fig. 2. The average throughput for each UAV in one cluster

The number of retransmission attempts for different
scenarios is shown in Fig. 3. The result shows that an increasein the network size leads to the increase of the number ofnodes using the same channels SCH. This also results inconflicts during transmission, and therefore, the number ofretransmission attempts increases with time. It should be
noted that the vertical axis of the graph shows the ratio of thenumber of packets to be retransmitted to the total number ofpackets.
0.05

Average number ofretransmission attempts

Cluster_2
Cluster_1

1311

0.045
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005

C. Simulation and results
The simulation was performed in the Riverbed Modeler,
and the performance of the proposed CMMpP protocol was
evaluated based on the results. As shown in [20, 21], theRiverbed Modeler package does not have an IEEE 802.11p
module designed for operation in flying networks. Whensetting the initial data, all models with a bandwidth of 10 MHz

0
4

8

16

32

Number of member UAVs in a cluster

Fig. 3. The average number of retransmission attempts for different numbersof CM nodes in one cluster

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

Since each of the initial network configurations willproduce results with different absolute values, it is importantto focus on comparing the scenarios. Fig. 4 shows thenormalized graphs (normalized average delay) for
representing the average delay when transmitting packets in
the CMMpP protocol in a scenario with one cluster, but with adifferent number of CM nodes. This figure indicates that themore the number of CM nodes in the cluster, the higher thetransmitting delay in the network becomes.

1312

UAV (CH)IEEE 802.11p*

FlyingSegment

UAV (CH)

Mobile Base
StationIEEE 802.11p
IEEE 802.11p
UAV
UAV
UAV

UAV

UAV

IEEE 802.11n
Phone is foundPhone has
not been foundTerrestrial
Segment

Normalized average delay (%)

26

25.5

25

Fig. 5. An architecture of flying network for emergencies
24.5

Start

24

23.5
4

8

16

32

Number of member UAVs in a cluster (scenario with one cluster)UAV-Swarms fly
and scan the area

Fig. 4. Normalized average delay for a scenario with one cluster

III. FLYING NETWORK FOR EMERGENCY SYSTEMS
A. System ModelIn the proposed flying network for emergencies, theUAV-swarms move with specific purposes and are subject togeographical restriction. In a mission-oriented flying network
environment, UAV-swarms cooperatively communicate witheach other to receive their missions from the mobile basestation (MBS) or forward the collected data to the MBS.
In particular, MBSs will send UAV-swarms to scan areas
around MBS and gather information, which will bedisseminated among UAV-swarms and transmitted to theMBS. Besides, the UAV-swarms also act as intermediaries tomake connections and transfer data between the MBS.
Together, they create a timely, efficient, and effective rescue
system and increase the coverage area of the flying networks.
In general, there are two types of UAVs: Multi-Rotor and
Fixed-Wing. For searching operations, fixed-wing UAVs are
more suitable than the others because they have a highvelocity and long flying time, up to several hours. An
architecture of a flying network for emergencies is presented
in Fig. 5.
A flying network for emergencies consists of two parts: theflying segment and the terrestrial segment. In the flying
segment, UAV-swarms are interconnected by the IEEE
802.11p, using CMMpP, which is a mesh network deployedon UAV-swarms so that they can locate and communicate
with each other and retransmit data to the MBS. Furthermore,
UAV-swarms are equipped with IEEE 802.11n technologies
to capture signals as well as communicate with devices on theterrestrial segment [18].
To begin searching and rescuing operations in a flying
network for emergencies, the MBSs will move to the rescue
area. There they will deploy UAV-swarms by clusters, which
include a head cluster UAV (UAV (CH)) and members
cluster UAV, to collect information, make connections, andexchange data. Member clusters UAV scan the area, collectinformation, and send this information to MBS through headclusters UAV (Fig. 5).

Are signals
detected?

No

Yes
Calculate and send
coordinates tobase station

Keep flying andscanning the areaNo

Is the entire
area scanned?
Yes

End
Fig. 6. Scheme of the searching process of UAV-swarms

As mentioned above, the first task of the operation is
finding the location of victims. Assuming that the victims
always have their smartphone and an automatically accessible
Wi-Fi as well as UAV-swarms has enough power to work in
the target area. Firstly, UAV-swarms receive information
about the scanned area and start flying and scanning. When
UAV-swarms get more than two signals from the victim’s
phone, they start to calculate the phone’s coordinator. After
getting one more new signal, UAV-swarms need to repeat thecalculation of the phone’s coordinator. These signals containthe phone’s MAC address, which helps us distinguish amongphones on the ground. Then, UAV-swarms send the latest
calculation of the phone’s coordinator back to MBS when no
more signal of that phone is sent to them. UAV-swarms
continue scanning the entire area to find all signals fromsmartphones of victims. The scheme of the searching processof UAV-swarms is presented in Fig. 6.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

B. Deployment Models
When UAV-swarms fly for the searching, they
continuously send beacon signals about the public Wi-Fi at afrequency of about 100ms. When the phone on the groundcaptures these beacon signals and knows about the Wi-Fi
availability, it will start to feedback to UAV-swarms. Figure 7
describes the geometry approach to defining the coordinate.
Assuming that the phone lay on the ground ( z0 = 0 ) , therefore
the coordinate of the phone’s location needed to be foundis ( x0 , y0 , 0) , and the i-th position of UAV when receiving thephone signal is ( xi , yi , zi ) . If the UAVs receive m signals
from the phone, the coordinates of the searching point are
required to satisfy the equations:( x1 − x0 )2 + ( y1 − y0 )2 + ( z1 )2 = (d1 ) 2

...

2
2
2
2
(1)
,
( xi − x0 ) + ( yi − y0 ) + ( zi ) = (di )...
( xm − x0 )2 + ( ym − y0 ) 2 + ( zm ) 2 = (d m ) 2
with di is the distance between the UAV and phone location.

UAV(xm,ym,zm)

UAV(xi,yi,zi)

UAV(x1,y1,z1)

v

1313

TABLE I
TIME TO SCAN AN AREA OF 5 KM × 5 KM FOR THREE CASESTime to scan (s)Horizontal line

Triangle

Vertical line

4400

7224

13056

Assuming that there is a phone located in a certain zone andin a certain flying time t (time of receiving-signals) throughthis zone, the UAV group always is able to receive at least one
signal from the phone. Figures 8a, 8b, 8c, the time t isillustrated according to three considered cases. With different
UAV structures, this time is different and is calculated
according to the following formulas:thorizontal line =

2⋅ R
,
v

(2)

3
2 ,

2⋅ R + R⋅
ttriangle =(3)v
3⋅ 2 ⋅ R
tvertical line =
,
v

(4)

The experiments were performed to measure the number ofsignals sent from the phone to UAV-swarms and the time of
the receiving-signals process, which can be calculated fromequations (2), (3), (4). The results in Table II show that the
Vertical line shape has the possibility of getting a maximum
number of signals, which means that the Vertical line shape iscapable of finding the victims with the highest probability.
TABLE IIMAXIMUM NUMBERS OF RECEIVED SIGNALS

Mobile Phone(x0,y0,0)Time of receiving
signals (s)Horizontal line

4

Maximum number ofreceived signals
31

Triangle

6.45

42

Vertical line

1253

Fig. 7. The geometry approach to defining the coordinate

From a practical perspective, the UAV-swarm can consistof 3-10 UAVs [22]. In this paper, for the aim of searching
victims, we assume that members cluster UAV have three
fixed-wing UAVs, which always move forward with the same
constant velocity v = 25 m/s at altitude h = 50m, as well as
being able to capture signals with a radius of coverage circles
in the ground of about R = 50m by using the technology IEEE
802.11n.
There are three cases for the structure of the UAV-swarm
in the searching process (Fig. 8): Horizontal line (Fig. 8a),
Triangle (Fig. 8b), and Vertical line (Fig. 8c). By comparing
different structures of the swarm, we can select the best
structure for finding victims due to the time and accuracy
during the searching process as UAVs have speed and energy
limits.
With the parameters given above, there is time given for
UAVs to be able to scan an area of 5 km × 5 km, as shown in
the Table I. This table shows that horizontal line gives us theshortest scan time, but the main purpose of the article is to findout victims, so our top priority is to receive as many signals as
possible because the more signals we get, the higher accuracyof the searching process for the victim's location we will have.

t
UAV 1

v

R

v

UAV 2

t
h

v
v

UAV 3

UAV 1
UAV 2
v

UAV 3
v

h

R

b) Trianglea) Horizontal line

t
v

UAV 1

v

UAV 2

v

UAV 3

h
R
c) Vertical line

Fig. 8. Three cases of the structure of UAV-swarm for searching

IV. POSITIONING METHOD
A. Distance Calculation
In wireless communication, the radio propagation models
are considered to evaluate the path loss when a signal
encounters barriers over a distance. The power of the received

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

signal is less than the transmitted signal. A measurement of
the power in each received radio signal is presented byReceived Signal Strength Indicator (RSSI) to predict thedifference between the transmitted and received signals in atransmission range [23], [24]. RSSI distance measurement
generally is calculated in the logarithmic distance path-loss
model as:
 d 
RSSI = A − 10 ⋅ n ⋅ log  + Xσ ,(5)d0
where d is the distance between the transmitter and thereceiver;A is the path loss at the reference distance d0;
n is the path loss exponent related to a specific wirelesstechnology;
X σ is a Gaussian-distribution random variable with zero1314

The experiments were performed to measure RSSI anddistance from a phone to an AP. The received measurements
were used to estimate the parameters (A, n) in the distancecalculation model (8). The comparison of the received dataand estimated data is shown in Fig. 9.
B. Coordinate Calculation
After the UAVs measure the distance to the signal source,
the next task is the coordinate calculation. Assuming that thephone position is p0 ( x0 , y0 , 0) on the ground and the UAV
receives a signal from the phone at the position pi ( xi , yi , zi ) .
The distance between these two points is calculated with themeasurement error ξ µ ,σ , set during the experiment. With each
received signal from the mobile phone, the distance from
UAV to it will be:(9)d ( pi , p0 ) = ( xi − x0 ) 2 + ( yi − y0 )2 + ( zi ) 2 + ξ µ ,σ ,mean and variance σ 2 .
In the calculation, d0 usually is chosen to equal 1 meter.
Hence, A is the RSSI at a distance of 1 meter. Since the X σ has
zero mean, RSSI can be expressed as follow:(6)
RSSI = A − 10 ⋅ n ⋅ log ( d ) ,Therefore, the number of received signals and themeasurement error influence the accuracy of the positioning
process. The position pc ( xc , yc ) is found by minimizing theexpression:

According to equation (6), the distance between
transceivers can be calculated by:arg minA− RSSId = 10 10⋅n ,(7)In our proposed system, UAVs act as Wi-Fi Access Points(AP), which are always ready to capture any signals fromclients. The AP can evaluate the received signal’s strength
generated from the phone when it is connected to the AP.
Hence, the distance from the AP to the phone can be
determined by equation (7).
Since the path loss depends on the environment, in order
to increase the positioning accuracy in our system, there is aneed to estimate the parameters (A, n) before deploying thesearching mission. Therefore in each specific searchingenvironment, the UAV-swarm is set up to calibration mode,i.e., the RSSI, the distance between the UAVs and the starting
point are measured several times. Then, the non-linear leastsquare method is used to find the optimal parameters (A, n).
With the m measurements, these parameters are founds as:
2

A− RSSI i m
( A, n) = arg min  10 10⋅n − di  ,A, n  i =1




(8)

m

xc , yc2

( ( x − x ) + ( y − y ) + ( z ) − d ( p , p )) , (10)
2

i

c

2

i

c

2

i

i

c

i =1

where m is the number of received signals from the phone.
As the result of minimization, the coordinates and theirestimation errors are found.
C. Result Assessment
In order to evaluate the positioning capability based on the
Wi-Fi signal, the model is simulated in consideration of thenumber of received signals and the measurement error.
According to Fig. 8 and Tables II, III, the group of UAVs cancover a certain zone in the t seconds. In that interval of time,
the UAVs can receive at least one signal or more.
A comparison of distances between the given point and thefound point is considered as a positioning error. The
difference between them is calculated as:(11)
d ( p0 , pc ) = ( x0 − xc ) 2 + ( y0 − yc )2 ,The simulation gives the density of positioning error whenchanging the number of detected signals. Figures 10 and 11
show the probability density of positioning error when 30 and3 signals are received, respectively, with a relative error of
distance measurement of 20%.

Fig. 9. Distance determination based on RSSI
Fig. 10. Density and histogram of positioning error when 30 signals arereceived

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

The dependence of the positioning error on the number ofreceived signals is shown in Figure 12.
According to Figure 12, the coordinates are determinedwith less error when more signals are detected.
On the other hand, distance measurement accuracy is an
important factor that affects the positioning error. Figure 13
shows the dependence of the positioning error on the relative
accuracy of distance measurement, which is generated in thesimulation. The number of received signals is assumed with30.

Fig. 11. Density and histogram of positioning error when three signals arereceived

1315

The simulation results show that the positioning error
increases when the error of distance measurement increases.
Hence, the positioning accuracy can be achieved byincreasing distance measurement quality and ensuring moresignals are detected. The distance measurement mostly
depends on a specific technology and the environment.
Therefore, some models of distance measurement can be usedin different cases. In addition, the UAV-swarm’s ability toreceive signals also depends on communication technology
and the environment, as well as on the number of used UAVs
and their speed.
IV. SIMULATION FOR POSITIONING METHOD
In order to evaluate the effectiveness of the search andrecuse operation using the three cases of the UAV-swarm as
mentioned above, a simulated scenario of UAV-swarm’s
mobility was developed by network simulator Omnet++ [25].
Assuming that the victim is in the subarea 500m x 500m, theability to receive signals from the UAV-swarm is consideredwhen this entire subarea is being scanned. The UAV system isdesigned to receive signals from the victim's phone on the
2.4 GHz frequency band with the average rate is
1 signal/second (1 signal generated per second) and with thetransmission power is 10 mW. The victim location is
randomly chosen 50 times (black points) in the subarea, as
shown in Fig. 14. The search mission is simulated 50 times
corresponding to the victim position, which also is randomly
generated in each simulation time. As shown in Fig. 14, there
are 50 victim positions that the UAV system needs to find.
After each scanning mission, the coordinates of the victim arecalculated and compared with initial random coordinates. We
can conclude which structure provides the most accurate
results for UAV-swarm in search and rescue operation.

Fig. 12. The dependence of the positioning error on the number of received
signalsFig. 14. Random positions for the victim in the subarea

Fig. 13. The dependence of positioning error on the relative accuracy of
distance measurement

The results shown in Fig. 15-17 indicate the locations of
the UAV receiving a signal from the victim’s phone in threecases (gray points) where the location of the victim is atcoordinates (300,200) (black point). Fig. 15 shows thenumber of received signals of UAV-swarm for the Horizontal
line shape is 9, whereas that for the Triangle shape in Fig. 16
is 14. Lastly, for the Vertical line shape in Fig. 17, there are 37
received signals. It is clear that the collecting-signals position
within a radius of around 150m from the victim and the
Vertical line shape gives us the most significant number ofreceived signals, which makes it the most effective approachin terms of positioning accuracy for the searching operation.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

Fig. 15. Coordinates of UAVs, when receiving signals in search used the
Horizontal line shape for UAV-swarm

1316• Horizontal line (10 signals with the standard
deviation of 3),• Triangle (13 signals with the standard deviation of 4),• Vertical line (20 signals with the standard
deviation of 5).
Approximately, the ratio of received signals for three cases
is as follows (Horizontal line, Triangle, Vertical line) =(1: 1,5: 2). According to Fig. 21, Vertical line and Triangle
provide small distance errors (at around 2.2 m with thestandard deviation of 0.9 m and 2.1 m with the standard
deviation of 1.2 m, respectively). A conclusion that can beextracted from this result is that the positioning method ismore accurate when there are more received signals. This isaligned with the statement mentioned above.

Fig. 16. Coordinates of UAVs, when receiving signals in search used the
Triangle shape for UAV-swarm

Fig. 18. The number of received signals for UAVs in three cases

Fig. 17. Coordinates of UAVs, when receiving signals in search used the
Vertical line shape for UAV-swarm

Fig. 19. Density and histogram of the number of received signals for threecases

Through 50 simulations run for each case, the number ofreceived signals in each case is presented in Fig. 18. This
figure shows again that using the Vertical line shape for
UAV-swarm, we might receive more signals from the victim’s
phone. The probability density of the number of received
signals for three cases is shown in Fig. 19. There is a higher
probability of receiving 20-30 signals using the Vertical line
shape, whereas the probability of receiving 5-15 signals ishighest by using the Horizontal line shape, and for the
Triangle shape, the highest probability of receiving signals isin an interval of 10-17 signals.
The number of received signals is averaged after the whole
50 simulation times. The mean and standard deviation values
are shown in Fig. 20. With the results from Fig. 20, theaverage numbers of received signals in each case are as
follows:

Fig. 20. The average number of received signals for different UAV-swarm
shapesICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

Fig. 21. The distance errors for different UAV-swarm shapes

V. CONCLUSION
In the paper, the method for detecting the coordinates ofsubscribers by catching the Wi-Fi signals generated from theirphones in the absence of infrastructure of networks ofcommunication operators using a flying network for
emergencies based on UAV-swarms was proposed. In the
flying network for emergencies, UAV-swarms use a newprotocol, known as Cluster-based Multichannel MAC IEEE
802.11p protocol, for communication between UAVs and use
technology IEEE 802.11n to capture signals. We propose ascheme of searching process of UAV-swarms and study threecases of UAV-swarm structure for searching. The results ofexperiments show that the Horizontal line shape gives us theshortest scan time while the Vertical line shape is capable offinding the victims with the highest probability. The
simulation’s results for detecting the coordinates ofsubscribers show that the probability density of positioning
error is much better when there are more signals, which
UAV-swarm received from the victim’s phone. Besides, thephones’ coordinates are determined with less error when moresignals are detected, and the positioning error increases whenthe error of distance measurement increases. In considered
three cases, the Vertical line shape provided the best result of
positioning methods and continuously received the highest
signals.
ACKNOWLEDGMENT
The reported study was funded by RFBR, project number
20-37-70059.
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A. Yastrebova, R. Kirichek, Y. Koucheryavy, A. Borodin, and A.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

Truong Duy Dinh received his BS and MS degreefrom the Bonch-Bruevich Saint-Petersburg State
University of Telecommunications in Russia, in 2014
and 2016, respectively. He is currently a Ph.D. student
in the Department of Communication Networks andData
Transmission
attheBonch-Bruevich
Saint-Petersburg
State
UniversityofTelecommunications, Russia. His research interests
include Internet of Things, Smart Cities, wirelesssensor networks, flying networks.

Dr. Sc. Vladimir M. Vishnevsky received an
Engineering degree in applied mathematics from the
Moscow Institute of Electronics and Mathematics,
Russia, in 1971, the Ph.D. degree in queuing theory
and telecommunication networks and the D.Sc. degree
in telecommunication networks from the V. A.
Trapeznikov Institute of Control Sciences of Russian
Academy of Sciences (ICS RAS), in 1974 and 1988,
respectively. He became a Full Professor with ICS RAS
in 1989 and the Moscow Institute of Physics and Technology in 1990.
He was an Assistant Head of the Institute of Information Transmission
Problems of RAS from 1990 to 2010 and an Assistant Head of a laboratory
with ICS RAS from 1971 to 1990. He is currently the Head of
Telecommunication Networks Laboratory, ICS RAS. He is a member ofExpert Councils of Russian High Certifying Commission and RussianFoundation for Basic Research, member of IEEE Communication Society,
International Telecommunications Academy, and New York Academy of
Science.
Van Dai Pham received his BS and MS degree in
infocommunication
technologies
andcommunication systems from the Bonch-Bruevich
SaintPetersburg
State
UniversityofTelecommunications in Russia, in 2017 and 2019,respectively. He is currently a Ph.D. student in the
Department of Software Engineering and Computer
Science at the Bonch-Bruevich Saint-Petersburg
State University of Telecommunications, Russia.
Since 2016, he has published papers related to IoT
and its applications in international journals and conferences. His researchinterests include IoT, FANET, Smart Cities, LoRa network, Wireless Sensor
Networks.

Dr. Duc Tran Le acquired his degree of Ph.D. at
Admiral Makarov State University of Maritime andInland Shipping, Russia in 2018. He works in
Information Technology Faculty, The University of
Danang - University of Science and Technology,
Danang, Vietnam from 2019. His research areas
include Internet of Things, wireless network, network
security, QoS, WLAN, Software-defined networking.

Dr. Sc. Ruslan Kirichek working in the
Bonch-Bruevich Saint-Petersburg State University of
Telecommunications as Professor in Department“Communication Networks and Data Transmission”.
He was born in 1982 in Tartu (Estonia). He graduatedMilitary-Space Academy A.F. Mozhaiskogo and theBonch-Bruevich Saint-Petersburg State University of
Telecommunications in 2004 and 2007 respectively. R.
Kirichek received Ph.D. at the Bonch-Bruevich Saint-Petersburg State
University of Telecommunications in 2012 and Dr. Sc. at the Povolzhskiy
State University of Telecommunications and Informatics in 2018. From2008 to 2013 worked as a senior researcher at the Federal State UnitaryEnterprise "Center-Inform". Since 2012, he has been working as the Head of
the Internet of Things Laboratory at the Bonch-Bruevich Saint-Petersburg
State University of Telecommunications (iotlab.ru). Since 2017, he has beena ITU-T Q12/11 Rapporteur “Testing of Internet of things, its applications
and identification systems” and an expert in international standardization at
the National standardization body “Rosstandart”. He is a General Chair of
the International Conference “Internet of Things and It’s Enablers”(inthiten.org). Since 2020 he has been the Head of department “Software

1318

Engineering and Computer Technology” at the Bonch-Bruevich
Saint-Petersburg State University of Telecommunication.
Dr. Sc. Andrey Koucheryavy was born in Leningrad
02.02.1952. After graduated from Leningrad Universityof Telecommunication in 1974 he worked atTelecommunication Research Institute named LONIIS,
where A. Koucheryavy working up to October 2003 (from1986 up to 2003 as the First Deputy Director). He became
the Ph.D. and Dr.Sc. in 1982 and 1994 respectively. A.
Koucheryavy has been professor in the Bonch-Bruevich
Saint-Petersburg State University of Telecommunications
since 1998. He has been the Head of department “Communication Networks
and Data Transmission” since 2011. He is honorary member of A.S.Popov’ssociety. Prof. A. Koucheryavy was the Chairman Study Group 11 ITU-T(Study periods 2017-2020). His scientific areas of interest are the network
planning, teletraffic theory, IoT and its enablers.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

1315

Role of Teachers in Developing Learning Pedagogy
Deepanjali Mishra*and Mangal Sain*****

Department School of Humanities KIT University Bhubaneswar, Orissa, India
Division of Information and Computer Engineering, Dongseo University, South Korea
deepanjalimishra2008@gmail.com and mangalsain1@gmail.com

Abstract—The rapid development of science andtechnology such as multimedia technology has offered abetter tool to explore the new teaching method. In fact,
multimedia technology has played an important role in
English language teaching, especially, in the non-native
speaking of English situations. It also aims to make nonnative speakers of English as language teachers aware of
the strategies to use it in an effective manner. This paper
aims to analyze the use of technology to English language
teaching in the non-native speaking countries and to bringout the problems faced by both teachers and learners ofEnglish. Multimedia teaching method has been used incollege English teaching wider and wider. It hascontributed a lot to higher teaching quality. Chalk and
Talk teaching method is not enough to teach English
effectively. There should change in the methodology ofteaching and free from all kind of biasism. Not only that ateacher must have the quality of adapting himself/herself
to the new technological revolution by incorporating it into
teaching practices. This paper gives an in depth analysis of
the new methodology that could be adopted in teachingpractices inorder to make teaching effective and student
centric.
Keywords—Multimedia, learningtechnology, Web based learning

strategies,

personality,———————————————————————
Manuscript received Jan. 10, 2019. This work is a follow-up the invitedjournal to the accepted & presented paper of the 20th International Conference
on Advanced Communication Technology (ICACT2018),"This work was
supported by Dongseo University, "Dongseo Cluster Project" Research Fundof (20200007).
Deepanjali Mishra is with the Department School of Humanities KIT
University
Bhubaneswar,
Orissa,India(e-mail:deepanjalimishra2008@gmail.com)
Mangal Sain is with the Division of Information and Computer
Engineering, Dongseo University, South Korea. (Corresponding author phone:+8251-320-2009; e-mail: mangalsain1@gmail.com).

I.

INTRODUCTION

The learners Carrier, Damerow and Bailey’s book offers awide-ranging perspective on digital learning, highlighting
cutting-edge research and recent developments in the use oflearning technologies in language education both in terms oftheory and practice. In fact, one of the particular strengths ofthe book is how well the pedagogy and technology areorchestrated to reflect the essence of digital learning. The
significance of ‘learning to learn’ has stimulated considerable
debates in both theoretical and practical fields of education
being a very strident and accountable process to develop
reading, comprehension, expression, curriculum, instruction
and assessment.Relatively, the principle inherent incomprehending various processes of learning styles andstrategies on the basis of learners’ autonomy has become
equally important for the teachers and the learners as well.
Research shows that the learning strategies of our learners
today display puny extension in some essential skills like notetaking, note making, idea organizing etc resulting abysmal
performance.
However, last decades surge in the study of language
pedagogy lending for cognitive strategies has retained
enormous discretionary power that eventually led to a ray of
positive hope in the fairness of the education system. The
ultimate intention of making aware of the learning styles andstrategies is to enable students encounter the problems they
face both academically and non-academically. Focusing thesignificance of the strategies required for learning, Deshler &Schumaker in ‘Multipass: a learning strategy for improving
reading comprehension’, [1] state that through the strategies we
not only train the students for the fulfillment of immediate
requirements but also help them to generalize those in othersituations over time.Hence the exposition of the special
individual potentiality of a child is the key factor demanding aserious, substantive, contextual and conducive atmosphere. The
pedagogic perception of language as skill, as a matter ofsocialization, is no more a matter of fact memorizing or
information collecting from various sources, rather anunderstanding of the learners’ characteristics congruously.
A system of burdened and impulsive education devoid of
individual learning styles doesn’t awaken the mind and thedormant intelligence in it but stuffs it mechanically creatingsome chaos and conflictions hemorrhaging the talent. It is acommon acceptance that different learners have different
attitude to learning a language for their individual differences.
Taking these important faculties into consideration somesuccessful pedagogic efforts and innovative ideas are

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

contemplated as learning styles and strategies of the learners.
Moreover, these differences determine their response todifferent teaching styles and techniques and those shouldn’t beunheeded. These differences include a learner’s motivation,
personality, language level, learning style, learning strategies,
and age and past language experiences. [2]The structure of the paper is organized as follows: Section
II presents the past work related to this field. Section 3
describes about the methodologies used for this research work.
Section 4 describes about teaching folklore through web basedlearning. Section 5 describes about the conclusion and future
work.
II.

TEACHING ENGLISH AS SECOND LANGUAGE

The concept of second language holds its position in
between a first or native language and a foreign language,
having profuse interactive scope outside and lacking the scope
inside the home. Concept of second language acquisition is
inclusive of applied linguistics with effectiveness of two
prominent disciplines; psychology and education. The
combined endeavor of the two important faculties takes care ofmaking learners active processors of information, creating
comprehension of materials, establishing relationships between
new knowledge and previous knowledge. Language learning is
not the acquisition of a conscious set of rules or facts but aprocess of understanding, speaking, writing and expressing thethoughts and emotions.
The philosophy of active learning can be best done when
the surrounding environment is contextual and learners get
optimum opportunity to exploit it. Teaching is a sub-social
process which technically sits on the triangle of teacher,
student and subject matter having the interior area filled withgeneral learning styles and specific learning strategies. The
effectiveness of the teaching process amounts high when thevertex of student’s corner becomes more significant. The scopeof high degree of interaction involves facilitating, motivating,enabling, engaging the students according to their own stylerather than plain presentation of facts and figures didactically
before them. Here the improvement and desirable changes arebrought about in the behavior of learners are perpetually under
the scanner of experiences and responses of their own. Our
main focus is on learning of a second language, which
encompasses a setting where it has abundant inputs in thesurrounding with wider scope of daily communication. We
need to remove veil after veil until we reach the inmost depths
and touch the pure white individuality and specialty in order tonaturalize the target language.
III.

CONCEPT OF LEARNING STYLE

Learning style is predisposition to behave in a particular
way when engaged in the learning process. Learning strategies
are the methods employed by the learner to achieve learning.
Research is being done taking various factors to investigate
links between learning style and strategy and multimedia. Suchknowledge could prove invaluable when attempting to design
multimedia learning materials for the widest possible
educational use. The viewpoint to learning is relatively sturdy
and it gives general direction to learning behavior being a key

1316

component in effective teaching. Style is a motif of a person to
execute a specific assignment. Learning styles are the ways inwhich a learner naturally prefers to take in, process and output
the information and acquire the skill. This is the way ofinterpreting, organizing and representing information. It reveals
the way one learns the best thus having a great impact onacademic life and career as well. The theory of individual
learning style took effort in the 1970s and since then it has
been exerting a greater impact on the realm of pedagogy. The
classroom methods are suitably affected by the individual
learner preferences. As the interest towards a rhyme, TPR(Total Physical Response), physical game, story, picture
displayed in a classroom is not always the same.
The focus and intensity for different styles differ dependingon their grades, personality, academic competence etc. David
A. Kolb in his experiential learning model spells out “fiveparticular levels of behavior: personality types, earlyeducational specialization, professional career, current job role,
and adaptive competencies.” The analyses show that learning
styles have a significant influence on learners’ learning strategy
choices. There is evidence that the Judging scale correlates
positively with seven sets of learning strategies. Thus it turns
out to be the most influential learning style variable affecting
learners’ learning strategy choices. Compared with low
achievers, high achievers are more capable of exercisingstrategies that are associated with their non-preferred styles.
The statement of Dunn and Griggs can be most suitable
here; “learning style is the biologically and developmentally
imposed set of characteristics that make the same teaching
method wonderful for some and terrible for others” (Dunn &Griggs, 1988, p.3). Pertaining to this the theory of multiple
intelligence proposed by American Howard Gardner such asmusical-rhythmic, visual-spatial, verbal linguistic, logicalmathematical, bodily- kinesthetic, interpersonal, intrapersonal,
naturalistic, existential and moralgeneral abilities contributes a
lot in the field of multiple learning and teaching styles. The
Experts give a significant emphasis on the individual styles andthus classify the learning styles distinctly as below.
A. Visual style of learning:
Out of all the three methods of learning strategies, Visual
Learning is one of most important strategy that was
popularized by Neil D. Fleming in his VAK model of learning.
The visual learning style, often referred to as the spatial
learning style, is a way of learning in which information isassociated with images. This learning style requires thatlearners first see what they are expected to know. People with avisual learning style are often referred to as visual-spatial
learners. Basically, the visual learning style means that people
need to see information to learn it, and this "seeing" takes
many forms from spatial awareness, photographic memory,color/tone, brightness/contrast, and other visual information.
Naturally, a classroom is a very good place for a visual learnerto learn. Teachers use overheads, the chalkboard, pictures,graphs, maps, and many other visual items to entice a visual
learner into knowledge. This is great news for you if this is theway you typically learn.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

Advantagesof Visual Learning
Visual learners typically do really well in a modern
classroom setting. After all, there are just so many visuals inclassrooms - whiteboards, handouts, photos and more! Thesestudents have much strength that can boost their performancesin school. Here are just a few of the strengths of this learning
type:•

Instinctively follows directions•

Easily visualizes objects•

Has a great sense of balance and alignment•

Is an excellent organizer•

Has a strong sense of color, and is very color-oriented•

Can see the passage from a page in a book in his or
her mind•

Notices minute similarities and differences between
objects and people easily•

Can envision imagery easily

B. Auditory style of learning:
The Auditory style is learning by hearing. This type oflearning is helpful in the classroom environment. During a
lecture, an auditory learner is able to easily comprehend
process and retain information. Auditory learning is not only
the ability to listen to spoken word and interpret tone, but todo so with a high level of accuracy and efficiency. Learners
with strong auditory ability are able to hear and comprehend
without missing much.Some people prefer to learn by hearing
what they want to learn. Theirs is the auditory learning style.
To learn, such people would prefer listening to discussions,talking matters over, reading out of texts or making use of ecourses containing audio recordings. One special advantage ofthis style is that you assimilate and retain information without
having to see it in texts or pictures. However, the difficulty oflearning among silently reading learners – in a library, forexample – is one disadvantage.
C. Kinesthetic style of learning:
These learners experience learning by actions; doing or
being physically associated with the process. They prefer toconnect their learning with real-life experiences. Hands on
experiment are more conducive for them in an active lab
setting demanding more physical involvements. They prefer tothe concreteness of the learning such as class demonstrations
and field work outside the class room to understand andremember things. They remain focused on the topic, comeprepared prior to class teaching, and take note actively
throughout the teaching. Studies in short intervals of time
having gaps of short breaks retain the memory of thesechildren. The stimulations of almost all the senses like sight,
touch, smell, hearing and speaking through energetic activities
like TPR (Total Physical Response), music, rhyme, video,
power point presentations, field trips, real life examples,collections, project work, role play, exhibitions, group1317

presentations etc are mostly preferred by the learners. They
often love to work on trial and error method. Discussions withother kinesthetic learners are found to be useful for them.
Application of the knowledge, comparison and analysis ofideas, taking help of references, scrutinizing samples, study
through screenshots and photographs are their interest area.
D. The Reading and Writing learning style:
This form of learning includes both talks and texts.
Children of this kind love to play with the words such as
rhymes, guessing words, tongue twisters, limericks andactivities like public speaking, debating, politics, writing andjournalism. They often record their speaking, speak before
mirrors, and talk themselves through simulators. They are
adept at expressing themselves verbally. [7] Collaborative
teaching- learning, oral lectures, use of mnemonics andacronyms for remembering things and other verbal tools are
preferred by such learners. They normally have good memory
power. Scripting of memorable words and sentences, reading
things dramatically and in a varied manner, setting some key
points to familiar song, jingle and rhyme, working with others,
doing verbal exchanges are found to be useful for suchlearners. They have fascination for performing arts, old
writings, music and lyrics, novels and fictions in order tosatisfy their linguistic needs.[8]If you learn best by reading
texts or writing down notes from what you read, see or hear;then you are defined as a read & write (R&W) learner. R&W
learners need writing materials to take down points they think
are important from what they read, hear or see. The R&W
style has the advantage of making students more independent.
With note-taking, they can learn and retain more of theconcepts on their own with learning style that works for them.
However, they also face the disadvantage of not being able tolearn easily with only visual or audio instruction or where they
do not have access to writing materials.
E. The social-interpersonal learning style:Learners having such learning style interact well withpeople both by face to face conversation and by non verbal
expressions like proper posture, gesture or body movements.
They are sensitive to the feelings, emotions and sentiments ofother persons and listen sincerely to other peoples’ style ofexpressions. They are typically fond of working through vivid
discussions in a group. The preference of social activities
rather than individual activities is high in case of them. Social
learners often create a study group or any specific club, may
be of same or different age and work taking feedbacks fromthem trying some techniques of memory development. They
stuff their learning through varied responses, imaginative
associations, role-playing techniques etc in a social setting andcreative suggestions of the group members, analyzing theerrors of others. As class room is a risk-free environment tomake mistakes than the real life situations, this kind oflearners experiment more in groups to learn. [10]Women studies plays an important role in imbibing asocio-interpersonal learning style as it helps n every class fromEnglish to history, math to science, parenting to auto
mechanics, there is room for feminism. It could be something

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

as grand as teaching about women’s roles in history tosomething as simple as asserting that girls can work on cars,
too, but it should be a part of every class, every day.
Historically, women — and other oppressed groups — havebeen marginalized. We have been relegated to boxes in themargins of textbooks as if to say, “This is what the women
were doing back at home while the men were off at war.It fits
into this little box which must mean that it wasn’t thatimportant and it won’t be on the test.” Imagine what that doesto the self-esteem of the girls seated in the classroom.
Incorporating feminism in the classroom is so much morethan doing a lesson about the Suffragettes. It’s about showing
girls that they matter, too, by giving them books written by
women. It’s about teaching boys to respect women. It’s about
encouraging girls to take math and science classes. It’s about
privileging the voices and fostering the interests of all of thestudents in the classroom, regardless of gender.
The study of feminism is also intersectional with othertopics. It isn’t just about women, but about all oppressedgroups. Teaching feminism in the classroom is not only about
women; it’s about civil rights, LGBTQ rights, and rights forpeople with disabilities just to name a few. Teaching respect
and acceptance of all people is teaching feminism. If we canincorporate feminism in the classroom, the benefits would beendless. Men would be taught to respect women, making
sexual assault and sexual harassment in schools a thing of thepast. Women would follow their passions rather than whatsociety wants of them. Bullying would virtually disappear
because respect and acceptance would triumph. [15].
Various cognitive styles of learning and activities followed
for the respective ones have been depicted below.
IV.

ROLE OF TEACHERS IN DEVELOPING LEARNING STYLES

Teachers, however have to call to mind that learners may
not always stick exactly to one stipulated type but they may
alter or develop their learning styles in course of time.The first
four styles of learning; visual, auditory, kinesthetic and tactilerefer to the physical, perceptual learning mediums and can be
categorized under ‘sensory preferences’. Visual students enjoy
a lot all activities of visual stimulations. They make theirlearning by reading, observing pictures, chat models, written
work on the black board, movies, slide show, flash card,
projector etc moreover, oral discussions and lectures without
any visual backup make them bored and confusing. In contrast,
auditory students are very much comfortable with dry,undistorted lectures or plain oral conversations. [11] Their
excitement lies in role plays, mock interview, debate,elocution, quiz, interactions among peer and with the teacher
etc. kinesthetic and tactile students however prefer to learn byfrequent movements, physical responses, tangible objects,
realia, collages, models, experiments, action works etc. They
can’t endure constant sitting for a period of time and need short
breaks in times.
The most significant factor associated with learning style is‘the personality type’. The diversities in personalities
determine the learning styles of a learner. They may be
extrovert or introvert, may be intuitive-random or sensing
sequential, may be thinking or feeling type or closure vs. open

1318

perceiving category. The extroverted types of students are
more expressive, friendly, and interactive. On the other hand
introverted students are having their own internal world, scanty
friend circle and often derive bliss from solitude. Intuitive
random students better understand theoretical phenomenon andnew abstract interpretations, often regulating self learning, on
the contrary sensing sequential students show more interest forfacts rather than theories, depend on specific instructions andguidance of the teachers. Thinking learners are morestraightforward and hardly care for others concern while
expressing their own views, whereas feeling students are
empathetic and compassionate in their words valuing others
ideas. While closure oriented students are serious, hardworking, need written information and like tasks withdeadlines. On the contrary, the open learners are very muchperceiving, light, fun loving and enjoy language as a gameinstead of taking it seriously of task completion activity. The
teacher being a keen observer and style instructor makes thelearners aware about their specific preferences and designs thebest strategies for the purpose.
V.

CONCEPT OF LEARNING STRATEGIESThe word ‘strategy’ has a significant place in the world
chart being associated with many ups and downs of manynations and people. Though it has a military origin, it stillconnects with wisdom, science and craft and application ofskills. It first sprung from the need of the people making plans
to defeat their enemy. This is derived from the Greek word
‘strategos’ which means ‘general’ or ‘stratagems’ which means‘tricks of war’. Alfred Chandler wrote in 1962 that: “strategy isthe determination of the basic long term goals of an enterprise,
and the adoption of the courses of action and the allocation of
the resources necessary for carrying out those goals”. Bruce
Henderson defines “strategy depends upon the ability toforesee future consequences of present initiatives. Strategy
mainly involves two significant processes: formulation andimplementation. The former deals with analyzing anddiagnosing the situation developing guiding policies, and thelatter includes the action plans to attain the destination.”
According to Chamot “learning strategies are techniques,
approaches, or deliberate actions that students take in order tofacilitate the learning and recall of both linguistics and content
area information. Studies indicate that “learning strategies is atechnical phrase that means any specific conscious action or
behavior student takes to improve his or her own learning”. It
is considered that “any specific action taken by the learner tomake learning easier, faster, more enjoyable, more selfdirected, more effective, and more transferable to newsituations” is a language learning strategy (LLS). Strategies are
the conscious steps of behavior used by language learners toenhance the acquisition, storage, retention, recall and one of
new information .The concept of learning strategy is still afuzzy one and not easy to have a final definition, though it has
been over thirty years since researchers began the study oflearning strategies.
Strategy is a very important phenomenon in any field as theresources available for any specific purpose is limited. The
adversary’s capability and intentions are uncertain. The
resources are irreversible. Strategies improve student

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

performance in spite of lacking a good cognitive skill earlier.
These also help in increasing student independence andengagement with learning. Through various strategies learners
realize the best one and find out the weakness in ineffective
strategies in order to augment the performances and thus
ultimately take new challenges. Thus their opportunities topractice skills become wider.
As students step up from one class to another, they confront
greater challenges of imbibing much information, working
independently, comprehending the extended and complicatedwritten text, expressing themselves properly, presenting well intests etcin their academic classes. To induce the concentrationof the mind and to increase its potentiality they have to apply
some learning strategies to accomplish any task more
effectively and efficiently despite their knowledge andversatility deficits. Various studies in this field expose threemain rationales related to learning strategies; first, according toArmbruster older students show better proficiency in use ofsuch behavior than the younger ones as these are related to age.
Secondly Deshler &Schumaker opine that students who learn
how to learn are more expert in learning new information andadapting to changing conditions of future. Thirdly this requires
the learners to welcome the accountability for their learning.
[11] Thus the age, learning how to learn and acceptance ofmajor responsibility are three significant factors paramount tomake a learner independent.
Learning strategies are the specific conducts and mentation
detected by the learners to learn language. These refer tolearners’ self created thoughts, actions and ways which are
directed to the achievement of the learning goals in an
organized manner. The learning tricks or the academic skills
followed by the learners to excel their performances are named
as learning strategies. Weinstein and Mayer (1986) opined
about learning strategies that “they are behaviors and thoughtsthat a learner engages in during learning, which are intended toinfluence the learners’ encoding process”(p.315). According toJasminaHasanbegovic “learning strategies refer to students’
self generated thoughts, feelings and actions, which aresystematically oriented towards attainment of theirgoal”.Scarcella defines learning strategies as “specific actions,behaviors, steps or techniques; such as seeking out
conversation partners, or giving oneself encouragement totackle a difficult language task used by students to enhancetheir own learning”(scarcella& Oxford).
Considering the vitality of learning strategies Allwrightstates, “learning strategies can also enable students to become
more independent, autonomous, lifelong learners”(Allwright,
1990). Students apply a broader range of learning strategies
with their consciousness sometimes and acquire the skills of
language and sometimes the skilled teachers make them adopt
the appropriate ones. Schumaker and Deshler (2006) have theopinion on learning strategies as “an individual’s approach to atask. It includes how a person thinks and acts when planning,
executing and evaluating performance on a task and its
outcomes.” Thus it is a deliberate individual practice toorganize and remember things in learning process. “Learning
strategies are intentionally used and consciously controlled by
the learner” [12] let’s say for example a teacher is going toconduct a brainstorming activity in a class; she has to make a1319

plan first about the way she is proceeding. Taking
consideration to the number of the students in the class, shesets the strategies. Oxford (1990) compiles the learning
strategies briefly as “any specific action taken by the learner tolearning easier, faster, more enjoyable, more self directed,
more effective and more transferable to new situations, is alanguage learning strategy.” Rubin (1987) opines “learning
strategies are strategies which contribute to the development of
the language system which the learner constructs and affects
learning directly”. Cohen gives his consent “second language
learner strategies constitute those processes which areconsciously selected by learners and which may result inactions taken to enhance the learning or use of a second or
foreign language, through storage, recall application of
information about the language.” [4] Seliger (1984) defines“strategies as basic abstract categories of processing by which
information perceived in the outside world is organized andcategorized into cognitive structures as part of conceptual
network.”
The different definitions and opinions on language
strategies can be presented in brief as follows. Learning
strategies are learners’ actions or behavior for improvement of
their own apprehensions and internalization. These may be
behavioral or cognitive. They may have direct or indirect effecton language development. They are flexible and often
conscious. They are problem oriented and solution intended.
These may be conscious or subconscious steps towards goal.
Use of right strategies at right time helps the learners learn thelanguage in a better way i.e. in an autonomous and independent
manner. The learners can be trained to use strategies. The rightchoice of strategies that fit the learning style of the learnerpresents a meaningful, active and successful self regulative
learning. Some learning strategies adopted by different learners
are; repeating new words in mind thereby memorizing it, trying
to use the language that they have already learnt during
conversations, making predictions of new word meaning by
requestingopinions from others about their use of language,
throwing themselves into critical situations of using thelanguage, recording, judging and correcting by self for fluency
and accuracy, asking a learner to revise what they have learnt,
discovering ways of remembering new vocabulary, exploringown area of weakness and deciding the ways of improvement
etc.
VI.

TYPES OF LEARNING STRATEGIES

According to the two psychological scientists Dr. Yana
Weinstein and Dr. Megan Smith the six research-basedlearning strategies are spaced practice (learning to be done insmaller chunks over regular interval of time), retrieval practice(recalling learnt information putting all the learning materialsaway), elaboration (asking students open ended questionsto
elaborate their understanding as much detail as possible),interleaving (practicing different skills one after anotherinterrupting the repetitive monotony of the same thing),
concrete examples (showing concrete examples for the abstract
phenomena), dual coding (combining words with visuals fordurable learning). The combined effort of all the strategies
instead of isolated try outs makes the learning more permanent.
[13]

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020VII. TEACHER’S ROLE IN DEVELOPING LEARNINGSTRATEGIESE
The Learners are expected to be individualistic executantsby controlling the learning situation with required focus andintention. In order to teach learning strategies teachers as
strategy instructors have to learn about students’ interests,
motivations and learning styles and strategies. So the basic
thing that a teacher always needs to carry in his mind is theways of acquiring information for a good impact on overall
scholastic accomplishment and life adaptation process of thelearners. Teacher as an ardent strategy instructor first has to
identify the type of curriculum demand the child fails to meet,so that he can determine the task specific strategy as a required
remediation for the student. Secondly the new strategy isintroduced to the learners by breaking down it into simpler
components. Thirdly the strategy is modeled in detail before
the students demanding their full involvement and at the fourth
step students use the verbal rehearsal to learn the steps
systematically. In the fifth step the students practice the up todate strategy to a desired level of performance in controlled(simple, short, easy) materials. The next step is to practice thestrategy to master over that with proper reinforcement andcorrective feedback from the teacher. The final step isdetermined by a post test to mark the satisfactory progress inthe target strategy. For instance, the teacher puts an eye at
introducing the students with one strategy at a time, presents
approaches age and grade appropriately, makes explicit
instruction about strategies.
The instructions should include elements that help students
generalize a strategy and apply it to other tasks. A goodstrategy instructor should introduce the strategy by modeling it
and describing it, sell the strategy by telling how it works,
generalize the strategy by telling where else it is useful andhelp students perfect the strategy by providing practice
opportunities.
VIII. LEARNING STYLES VS. LEARNING STRATEGIESThe Learning styles are the general approaches –forexample, global or analytic, auditory or visual –that students
use in acquiring a new language or in learning any othersubject. These styles are“the overall pattern that give general
direction to learning behavior Of greatest relevance to thismethodology book is this statement: Learning style is the
biologically and developmentally imposed set of characteristics
that make the same teaching method wonderful for some andterrible for others.This chapter explores the following aspectsof learning style: sensory preferences, personality types,
desired degree of generality, and biological differences.
Learning strategies are defined as “specificactions, behaviors,
steps, or techniques-- such as seeking out conversation
partners, or giving oneself encouragement to tackle a difficult
language task -- used by students to enhance their own
learning” When the learner consciously chooses strategies that
fit his or her learning style and the L2 task at hand, thesestrategies become a useful toolkit for active, conscious, andpurposeful self-regulation of learning. Learning strategies can
be classified into six groups: cognitive, meta-cognitive,
memory-related, compensatory, affective, and social.

1320

Stylesandstrategieshelpdetermineaparticularlearner’sability
andwillingnessto work within the framework of various
instructional methodologies. It is foolhardy to think that asingle L2 methodology could possibly fit an entire class filledwith students who have a range of stylistic and strategic
preferences. Instead of choosing a specific instructional
methodology, L2 teachers would do better to employ a broad
instructional approach, notably the best version of thecommunicative approach that contains a combined focus onform and fluency. Such an approach allows for deliberate,
creative variety to meet the needs of all students in the class.
While learning style refers to the relationship of anindividual with his method of learning, learning strategies
mention the attitude and actions related to the learning
objectives. While learning style deals with the cognitive level
and learners’ preferences, learning strategies deal with metacognitive level and learners’ competence. Moreover, learning
style refers to approach whereas learning strategy refers to theactions taken for learning. According to Scarcella and Oxford
in “The Role of Styles and Strategies in Second Language
Learning”, while learning styles reveal the preferred system ofbrain response and biological reciprocation to certain sensory
stimuli, learning strategies explicate distinct actions,
techniques, strides and stances adopted by the learners toamplify their learning. Learning strategies are dependent oflearning styles. The former deal with specific approach and thelatter refer to general approach of learning. [14]Direct strategies refer to subconscious tasks, which are
inherently learnt while indirect strategies refer to conscious
strategies. These two classes are then subdivided into six subgroups of memory, cognitive, compensation, social, affective
and meta-cognitive. These subsets are interwoven with eachother, creating an occasional overlap in the strategy groups.
According to Oxford direct and indirect strategies and these six
strategy categories function as a mutual support network within
which various types of strategies enhance second language
learning. Oxford’s inventory is designed in a way to suit not
only students learning English as a second/foreign language(ESL/EFL) in America but also students of any country. The
inventory has already been translated into many languages andused as an effective tool for measuring strategy preferences and
10 developmental stages in strategy usage. [15]IX.

CONCLUSION

Summing up the significant factors of the general approachof learning style or the specific action of learning strategy on
the considerations of the cognitive, affective or the behavioral
aspects of the learners the teacher has to deliberately develop
the appropriate plan, material and course of action in order to
get the maximum learning outcomes for the growing academic
challenges. There is a need of a harmonic relationship between
the two in order to achieve the best result among the learners.
The more the balance between style and strategies is found, theless the anxiety and the more the confidence among the learneris to be paramount. Increasingly the intensity is about learningby doing. So the issue of building capacity for learning with arange of key constructs including motivation, propensity, andapplication of strategies to produce an effective language
learner with a base of full fledge rich and extensive language

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 5, September 2020

proficiency is solicited. In a word, multimedia teaching method
belongs to CAI (computer-aided instruction) in substance, andwe can heighten efficiency substantially, which is a kind oftrend. We should make best use of its advantages and bypass
its disadvantages. Of course it can’t replace all other teaching
methods and we can apply several methods together in one
class. Only under the background of quality education can weuse advanced educational theory and we can fulfill the target ofcollege English teaching by utilizing modern education
technology reasonably. [16]REFERENCES
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[3] Cornett, C., “What You Should Know about Teaching and Learning
Styles.” Bloomington, IN (1983): Phi Delta Kappa.
[4] Cohen, A.D., “Strategies in Learning and Using a Second
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[5] Chandler, A., “Strategy and Structure: Chapters in the history ofindustrial enterprise”,(1962)Doubleday, New York.
[6] Cohen, A.D. & Scott, K., “A Synthesis of Approaches to Assessing
Language Learning Strategies. In R. Oxford (Ed.), Language Learning
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[7] Dunn, R. & Griggs, S.,“Learning Styles: Quiet Revolution in American
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Principals (1988)[8] Alley, G.R., & Deshler, D. D., “Teaching the learning disabledadolescent: strategies and methods.” Denver: Love Publishing.(1979)[9] Brown, A. L. “Metacognitive development and reading.” In R. J. Sipro,
B. B. Bruce, & W.F. Brewer(Eds.), Theoretical issues in reading
comprehension. NJ: Lawrence Erlbaum Associates.(1980).
[10] Mishra, Deepanjali. “Impact of Sociolinguistics in Technical
Education”Research Chronicler, 3(1) 95-101.(2015)1321[11] Schumaker, J.B., Denton, P.H.,& Deshler, D.D. “Learning Strategies
Curriculum: The Paraphrasing Strategy.” Lawrence :University Of
Cansas.(1984)[12] Schumaker, J.B., Deshler, D.D. Alley, G. R., & Denton, P.
H.,“Multipass: a learning strategy for improving reading
comprehension.”Learning disability quarterly, 5(3), 295-304.(1982)[13] Kolb, D.,“Experiential Learning Experience as the Source of Learningand Development.” New Jersey: prentice hall, inc. (1984)[14] Spratt, M., Pulverness, A., Williams, M., “The Teaching Knowledge”
Test Course, Cambridge University Press.(2009)[15] Samsa, Ashley Lauren. Teaching Feminism., “Why Should We?”//http.
care2.com/causes/teaching-feminism(2009)[16] Oxford, R.L., “Language Learning Strategies: What Every Teacher
Should Know.” Boston: Heinle & Heinle1990[1]

Deepanjali Mishra has completed her PhD in English from Utkal
University, Bhubaneswar. Area of interest includes Feminism andGender Studies, Sociology, Linguistics, Management. Has over 15 yearsof teaching and research experience "Published a book titled I am A
Woman". Organized a National Workshop and International Conferencein December 2017.

Mangal Sain received the M.Sc. degree in computer
application from India in 2003 and the Ph.D. degree in computerscience in 2011. Since 2012, he has been an Assistant Professor with
the Department of Computer Engineering, Dongseo University, South
Korea. His research interest includes wireless sensor network, cloudcomputing, Internet of Things, embedded systems, and middleware. He
has authored over 70 international publications including journals andinternational conferences. He is a member of TIIS and a TPC member
of
more
than
ten
international
conference.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

1322

A Survey on the Security in Cyber Physical Systemwith Multi-Factor Authentication
Mangal Sain*, Oloviddin Normurodov*, Chen Hong**and Kueh Lee Hui****

Division of Information and Computer Engineering, Dongseo University, South Korea
Department of Linton School of Global Business, Hannam University, Daejeon, Republic of Korea***
Dept. of Electrical Engineering, Dong-A University, South Korea**

mangalsain1@gmail.com, oloviddinnormurodov52@gmail.com, prof.chenhong@hnulinton.org and leehkueh@dau.ac.kr

Abstract—Cyber-physical Systems can be defined as acomplex networked control system, which normallydevelop by combining several physical components withthe cyber space. Cyber Physical System are already a partof our daily life. As its already being a part of everyone
life, CPS also have great potential security threats and canbe vulnerable to various cyber-attacks without showing
any sign directly to component failure. To protect user
security and privacy is a fundamental concern of any kindof system; either it’s a simple web application or
supplicated professional system. Digital Multifactor
authentication is one of the best ways to make secureauthentication. It covers many different areas of a Cyberconnectedworld,
including
onlinepayments,
communications, access right management, etc. Most of
the time, Multifactor authentication is little complex as it
requires extra step from users. This paper will discuss theevolution from single authentication to Multi-Factor
Authentication (MFA) starting from Single-Factor
Authentication
(SFA)
and
through
Two-Factor
Authentication (2FA). This paper seeks to analyze andevaluate the most prominent authentication techniques
based on accuracy, cost, and feasibility of implementation.
We also suggest several authentication schemes which
incorporate with Multifactor authentication for CPS.

I.

INTRODUCTION

We live in a world that is changing at a much faster pacethan ever before. More than any other time in our history, there
is a pressing need for a new way of looking at science,technology, and social phenomena to help us understand ourplanet, ourselves, and how we can take control of our collective
destiny. Without a doubt, our knowledge in the areas ofscience, technology, and social sciences has played a key rolein shaping the modern world. In simple words Cyber-Physical
Systems (CPS) is an engineered system in which a physicalsystem or a process is augmented with cyber components ssuch as computational hardware and communication network.
All these components are tightly integrated with each otherthat’s the reason functionality of one component is dependent
on the functionality of other component. each other. CPS aresystems of collaborating computational entities which are inintensive connection with the surrounding physical world andits on-going processes, providing and using, at the same time,
data-accessing and data-processing services available on theinternet. With other words, CPS can be generally characterized
as “physical and engineered systems whose operations aremonitored, controlled, coordinated, and integrated by acomputing and communicating core” [1].

Physical System orProcess

Keywords—Authentication, Multifactor Authentication, Cyber
Physical System, Sensors, Cryptography, Face recognition

Sensing

Control———————————————————————
Manuscript received January 10, 2019. This work is a follow-up the invitedjournal to the accepted & presented paper of the 20th International Conference
on Advanced Communication Technology (ICACT2018),” This work was
supported by Hannam University, Daejeon and Dongseo University,"Dongseo Cluster Project" Research Fund of (20200007).
Mangal Sain is with the Division of Information and Computer
Engineering,Dongseo
University,
South
Korea.
(;
e-mail:mangalsain1@gmail.com)
Oloviddin Normurodov is with the Division of Information and Computer
Engineering, Dongseo University, South Korea. (Corresponding author phone:+8251-320-2009, e-mail: oloviddinnormurodov52@gmail.com)
Chen Hong is with the Department of Linton School of Global Business,
HannamUniversity,
Daejeon,Republicof
Korea.
(e-mail:prof.chenhong@hnulinton.org)
Kueh Lee Hui is with the Dept. of Electrical Engineering, Dong-A
University, South Korea (e-mail: leehkueh@dau.ac.kr)

Computational
HardwareCommunication
Network

Cyber Component

Fig. 1. Cyber Physical System Architecture
The interaction between the physical and cyber elements isof key importance: “CPS is about the intersection, not theunion, of the physical and the cyber. It is not sufficient toICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

separately understand the physical components and thecomputational components. We must understand theirinteraction”. [2]Population and our consumption of the Earth’s resources
are soaring. Estimates put the world’s population beyond 7
billion, and annual per capita energy consumption at about 20
MWh/year or 1,800 kg oil equivalent. Fortunately, with thedevelopment and availability of powerful communications
infrastructure, our awareness of the state of the world and ourability to influence it are also improving: It is remarkable, forexample, that the number of smartphones on the planet has
already topped one billion. Some of the most exciting
innovation these days are from cyber physical system. Some ofthe examples of CPS include smart grid, autonomous
automobile systems, medical monitoring, process control
systems, robotics systems, and automatic pilot avionics.
With the recent development and exponential growth of
CPS, new security challenges have emerged. New type of
security threats, attack and controls have been introduced forthe new generation of CPS. However, there lack a systematic
study of CPS security issues. In particular, the heterogeneity of
CPS components and the diversity of CPS systems have madeit very difficult to study the problem with one generalized
model. [3]Authentication is the case of verifying the claimed identityof an entity. The process involves the verification of one ormore of some knowledge (like a parole), something inpossession (like a RFID access card) or inherence (like thefingerprint) of the entity. Main authentication process verifies
anyone of the above information. In theory, authentication is
simple process where a user provides some sort of credentials
like a password, smartcard, finger print, digital certificatewhich identifies that user as the entity which it claims to be.
There can be many ways and protocol that can be used toachieve this. Regardless of the method, the basic authentication
process remains the same. Authentication is an absolutely
essential element of any typical security model. Multi-Factor
Authentication (MFA) is a form of strong authentication whichuses more than one information among the above. Multi-factor
authentication techniques operate multiple authentication
programs to confirm the identity of the user and eliminate therisk of compromise of secret or token in basic authentication
methods. Each authentication program is a basic authentication
process. Thus, the overhead involved in a multi-factor
authentication process is multiple times that of a basic
authentication process. This makes the process fewer flexible.
In cyber physical system digital multifactor authenticationis one of the best ways to make secure authentication. It covers
many different areas of a Cyber-connected world, including
online payments, communications, access right management,
etc. Most of the time, Multifactor authentication is little
complex as it requires extra step from users. With two-factor
authentication, user also needs to enter a special code or key
number which can be generated by mobile, Internet, or somespecial device or some special code which was generated at thetime of registration. In some case these steps can be more than
2 where security level can be increase by adding special codeor Token. By introducing additional authentication factors, it1323

greatly increases the level of security, but it could also become
frustrating for end users. This paper surveys the alreadyavailable and emerging sensors (factor providers) that allow forauthenticating a user with the system directly or by involving
the cloud. The corresponding challenges from the user as wellas the service provider perspective are also reviewed.
Rest of the paper is organized as follow. In the first section
we have discuss about the evolution from single authentication
to Multi-Factor Authentication (MFA). In section III several
Multi –Factor Authentication scheme are discussed. In section
4 we have done some comparative analysis and finally insection 5 describes about the conclusion and future work.
II.

CONCEPTOF SINGLE AUTHENTICATION TO MULTIFACTOR AUTHENTICATIONIn the opening scene of Hamlet, Bernardo is asked to
identify himself by reciting a password before he can take over
his castle post from Francisco: “Long live the king!” [4] This
exchange, from Shakespeare’s prolific tragedy, is one of themost famous examples of passwords in literature. This ancientcollection of Arabic stories was officially published in English
in 1706. In one of the most famous stories, a woodcutter named
Ali Baba overhears a secret phrase used by a group of thieves
to protect their treasure. While the thieves aren’t watching, Ali
Baba utters the phrase “Open sesame” and takes the treasure.
With the development digital technology devices are
getting cheaper, smaller, more portable, and much more
powerful. Due to advancements in network, connectivity isfaster, easier, and wireless is available almost everywhere.
However, one thing stills the same, the way we authenticate
our device. We still using the same style of user names andpasswords that was devised almost 50 years ago at MIT tocontrol the amount of time students and professors could spend
on the university’s timeshare computer. In the beginning single
password-bases approach was enough to access to digital
information and resources. Fig 2 shows Initially, only one
factor was utilized to authenticate the subject. By that time,Single-Factor Authentication (SFA) was mostly adopted by thecommunity due to its simplicity and user friendliness [5, 6].

Fig.2. Single Factor Authentication
But once we start sharing much more sensitive personal andfinancial information it was necessary to come up withalternative authentication solutions. In addition, with thedevelopment of high-speed computing device it was easy touse several algorithms like brute force to generate millions ofpasswords per second. To counter those attacks, Multifactor
authentication (MFA) security systems come in to solve these
problems. Fig 3 shows In MFA mechanism the user must
present more than one credential in order to be authenticated.
These credentials or authentication factors varies according to
the security needs of the application. The user should submit

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

several authentication factors to the security systemconsecutively. These factors must be independent to avoid
breaking the whole security system in case of compromising
one of these credentials. [7]Biometric technology is among these new approaches
validates identity. The move from passwords to fingerprint
identification is already implemented in most of smart phones.
For the first time PayPal and Samsung worked together tomake the Galaxy S5 enables people to shop and pay in a store
or on their mobile device using just a fingerprint forauthentication. But it wasn’t just a simple new authentication
step, it open new window how digital commerce be safer andmore secure. Up to now, using cryptic and hard to remember
passwords were normal and it was always recommended to use
maximum available symbols such as upper, lower case, special
character, numbers etc. Now that we can begin to move frompasswords to biometric information like fingerprints, we’rereaching the point where we won’t have to make this kind ofcompromise any more.

1324

developed. As we know staff who operated these systems needbetter and need more accurate situational awareness, as they
have to be ready to intervene in the case of any cyber threat. In
As can be seen in Fig. 3, the virtual environment, which hosts
the digital twins, lies at the heart of the proposed framework.
Data (e.g., system logs, network traffic, sensor measurements)
is passively collected from the physical environment to mirror
the program states of real devices to digital twins [6].

Fig.4. the architecture of the proposed digital-twin cybersituational awareness framework
Fig.3. Multi Factor Authentication
Fingerprints are not the only form of biometric information
used for authentication today. There are many devices that use
technologies such as voice impressions, iris scans, and personal
physical signals such as unique heart rate information. Other
types of advanced authentication technologies are also used.
Organizations are already experimenting with various forms of"multifactor authentication" that combine different types of
information, location, audio and gestures to verify that you are
the one who claims to be you. With technological advances, we
can have multi-level authentication that works in the
background, so that the user does not even know what's goingon.
To do that, you have to know what is needed in the future.
Leading the way for these efforts is called Fast Identity Online
or FIDO for short. Founded in 2012, FIDO What's new andwhat does it mean? PayPal is one of the founders of FIDO and
now includes a large part of leading technology companies
such as Google, MasterCard, Microsoft, RSA, Netflix anddozens of other companies. As it is so urgent to improvesecurity and simplification, there are many companies working
hard to offer new solutions. And while it's still too early to saywhich approach will ultimately replace the current system, I'msure things are much safer and easier to use than the passwordbased approach.
III.

MULTI-FACTOR AUTHENTICATION METHODOLOGIES

3.1 A Cyber Situational Awareness Framework Based on
Digital Twins
Proposed Solution
To improve the cyber defense capabilities of CPS, A cybersituational awareness framework based on Digital twins were

From the Fig.4’s architecture, the behavior of digital twins
can be monitored on the program and network layer andpotential intrusions can be detected. In this case, digital twins
should show the correct, benign behavior of other physicalcounterparts, because they are generated from the CPS’s
specification. It also possible that they not show actual state ofreal devices, but they will still able to spot the deviations that
could indicate malicious activity. If there are somevulnerability or if there are some errors, digital twins will show
malicious behavior similar to their physical counterparts.
Above proposed framework provides enough veracity toadvance cyber situational awareness. As a result, thefunctionality of devices (i.e., programs, network
communication) can be emulated or simulated.
3.2 Multi-Factor Authentication based on multimodal
Biometric (MFA-MB) for Cloud Computing
Proposed Solution
The A. Mansour et al. [8] present a Multi-Factor
Authentication based on Multimodal Biometrics called MFAMB solution aiming to enhance the security level at theauthentication and access control process for the tree cloudcomputing service models (SaaS, PaaS and IaaS). An
authentication server is introduced in the authentication andaccess control process. The server provides an authentication
response (Auth Response) to the user authentication request(Auth Request) for cloud PaaS or SaaS; and allow access tocloud resources for user’s access demand for cloud IaaS.
3.2.1 FaMSL-MBS
Based on Ross et al. [9] observations and recommendations,that the fusion at the match score level is usually preferred as
it is relatively easy to access and combine the scores presented
by the different modalities. In this work, the authors suggest toICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

use a MBS method integrating multiple biometric traits(Fusion) at the Match Score Level. The proposed system iscalled FaMSL-MBS, see Fig. 5 for detailed architecture.
Depending on the number of traits, sensors, and feature sets
used, a variety of scenarios are possible in a MBS. Hereafter,
the authors have chosen to integrate multiple sensors andmultiple biometric traits.

6.4 MFA-MB for Cloud IaaS
In order to control access of authorized or unauthorized users
to the cloud infrastructure, it depicts in Fig. 7 solution toprovide privacy and security for cloud consumers dataowners. Also, in the present case it requires that the usercis
already registered in the database. Thus, Fig. 7 presents thesequence diagram to allow access based on MFA-MB to cloud
resources.

Fig. 5. FaMSL-MBS scheme

3.2.2 Security of biometric templates
Having established the need for MFA and challenges involved
in secure storage of authentication data; Khan et al. [10] haveproposed a Keyed Random Projections and Arithmetic
Hashing (KRP-AH) scheme for generating secure, efficientand renewable authentication templates. This scheme involves
random projections of biometric data using a random key
derived from a user’s password, and arithmetic hashing of theresulted projections.
3.2.3 MFA-MB for Cloud SaaS or PaaSFor End-Users authentication in cloud SaaS or PaaS, thescheme in Fig.6 is proposed as a solution and privacypreservation for cloud environments. Now, it is assumed thatthe user has already registered in the database. Moreover, it isimportant to notice that the relationship between End-Users(cloud consumers) and cloud SaaS/PaaS providers takes place
in a trusted environment introducing a Trusted Third Party(TTP); This is why they adopt the Secure Sockets Layer (SSL)
security technology.

Fig.6. MFA-MB for authentication in cloud SaaS or Paas

1325

Fig. 7. MFA-MB to allow access to cloud IaaS

3.3Prioritized access control enabling weighted, finegrained protection in CPS
The public report from the National Electric Sector
Cybersecurity Organization Resource (NESCOR) project
highlighted the importance of access control. [11]This report
identifies possible cybersecurity failure scenarios in six
domains of smart grid applications; they are Advanced
Metering Infrastructure (AMI), Distributed Energy Resources(DER), Wide Area Monitoring, Protection, and Control(WAMPAC), Electric Transportation (ET), Demand Response(DR), and Distribution Grid Management (DGM). As shown,
the top four action groups relate to access control as follows:
1.

Control access: block unauthorized access;2.

Authenticate only qualified users;

3.

Detect abnormal or unauthorized activity;

4.

Verify that systems operate as they should (audit).

A security policy addressing the prioritization is todistinguish and prioritize access operations and strengthen
protection level for highly prioritized ones. However, MFA
increase the security level with most of the application but it
does not work well in CPS environment, specially when it
comes to smart grid. An access control mechanism must beable to adjust protection level accordingly that MFA cannot
do.
Proposed Solution
To overcome the security weakness in smart grid, Lee et el.
introduces a priority-based access control, find grained
protection in cyber physical system. [12]a) Attribute-based identification

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

In CPS, there are number of devices are connected which
generate a large amount of data and that data makes it moredifficult to manage communication a non-trivial task. With thehelp of attribute-based identification, each user does notremember the ID of partners. Instead, it describes data ofinterest using keywords and/or attributes and communicateswith any users who store contents matching the description.
b) Attribute-based encryption
In attribute-based encryption (ABE), a user encrypts datausing a set of descriptive attributes, not using a specific user’s
public key.[13,14] It also creates an access policy tree,
representing a Boolean formula defining the combination of
attributes. Any user who can present credentials that
correspond to the attributes, that is, satisfying the tree, candecrypt the ciphertext.
c) Decentralized access control entry
In Access Control Entry (ACE), each file (object)
maintains an ACE that predefines three user classes (owner,group, and others) and determines which privileges (read,
write, and execute) are assigned to each class. The privilege
for each class is represented by a three-digit code so that auser is authorized to read from, write to, or execute the file.
DeACE decentralizes ACE by exploiting ABE and capabilitybased security. That is, a user is granted a capability“object_1 = 101” by an authority. When the user accesses theobject_1, he or she is authorized to read and execute, but not
to write. The capability is realized by number-assigned
attributes using ABE.
Access Control Decision
In a MFAC system, there exist three types of entities—
authorities CA ∈, users ∈ , and objects ˜ ∈ ˜. An
authority
maintains its own set of attributes, , that canbe in various forms. Fig.8 illustrates a protocol for access
control in MFAC.

1326“Prioritization in access control,” people perceive the access
operations of “reading” and “controlling” with different
priorities. The use of additional channel strengthens the level ofverification independence. In MFA, a factor represents a very
low computation complexity; numbers of 4–6 bits are
generated and matched. This simplicity is mainly attributed tohuman being’s involvement in the verification process. This
article adopts PBC, LSSS matrix, and DeACE, which represent
expensive computation costs. Unlike in MFA, qualification offactors and independence between factors are defined andverified via more complicated mathematical computation
processes in MF authorization.
3.4Anonymous and Efficient Message Authentication
Scheme for Smart Grid
In smart grid, network communication forms the core of theelectric system automation applications. The deployment of
one-way information flow communication networks is similar
to that in the traditional smart grid. But when it comes to twoway information flow communication network, it involves aneighborhood gateway which collects the electricity
consumption records from corresponding consumers via
wireless network connections [15].In next step, neighborhood
gateway sends its collected data to the control center for
detailed consumption analysis via a wired network connection.
Finally, the control center sends the electric control
information to relieve the burden of electricity demand peak or
responds with real-time pricing information to the smart gridconsumers.
It is necessary to select the internet Protocol based
communication to enable network communications in thesmart grid. Unfortunately, such networks are prone to anumber of external attacks, such as impersonation attack,tracking attack, and denied of service (DoS) attack [16]. Most
recently, Li et al. [17] proposed a provably secure message
authentication scheme with high efficiency for smart grids.
However, this scheme fails to provide mutual authenticationand cannot mitigate the impersonation attacks and DoS attacks.
[18]To overcome the weakness of Li et al.’s scheme, they
have proposed an improved anonymous message
authentication scheme for the smart grid. Similar to Li et al.’sscheme, proposed scheme also has three phases: theinitialization phase, the authentication and key agreement
phase, and the message transmission phase.
3.4.1 Security Initialization Phase
In this phase, the registration center (RC) generates the systemparameters and private-public key pair for each registered)entity user ( . . ,Fig.8. Authentication and authorization protocol. A protocol for an access
control decision in MFAC

The proposed MFAC can be applied to various situations inmany CPS applications as prioritization occurs everywhere.
This article considers a fine-grained access control in a smart
building context as a sample scenario. As introduced in section

Step 1. The RC chooses a multiplication cyclic group G withorder q, and the generator is g. Next, the RC selects a secure
one-way hash function ℎ ∙ : {0,1}∗ → {0,1}∗#$%&'(|*+| and asecure hash function ℎ ∙ : {0,1}∗ → ,'∗ .
Step 2. For each registered entity, RC generates a distinct
random numberxas private key and compute gx as the

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

corresponding public key. Here, let-. ∈ ,'∗ .be the i-th HANGW’s private key, and Pi=gxi be i-th HAN-GW’s public key.
Step 3. The RC sends the key pair (xi, Pi) to(i.e., thei-th HAN-GW) via a secure channel, wherePi can be revealed
to others.
Similarly, the j-th NANGW can obtain its own key pair as (yj,Qj= gYj) from the RC as described in Step 3.

1327

Cloud application systems provide service and need secure
identity authentication for users[25]. A third-party
authentication center can provide authentication and log
service for different users. Encryption cards or encryption
machine are adopted to provide encryption and decryptionfunctions. Cloud application servers convey the strong
authentication tasks to authentication servers, and deal withthe users’ access control according to the authentication result
returned from authentication server. The proposed cloud-based
authentication infrastructure is shown in Fig. 10 as follow:

Fig. 10. The cloud-based authentication infrastructure

Fig.9. Authentication phase of the proposed scheme

3.4.2 Authentication Phase
In this phase,andauthenticate with eachother. In addition, a temporary session key is created andused for encrypting subsequent transmitted messages, as
depicted in Fig. 8.
3.4.3 Message transmission phase
In this phase, a secure symmetric encryption algorithm (i.e.,
Advanced Encryption Standard, AES) and a secure one-way
hash function are used to guarantee the message’s
confidentiality and integrity.
This paper also demonstrates how their improved scheme
achieves the mutual authentication, session key agreement,user anonymity, perfect forward secrecy, and resistance toseveral attacks [19-23].
3.5Cloud-based Authentication Infrastructure
Users need to perform authentication whenever try to login
from different application systems, for example input
username and password to authenticate, one-time token
authentication [24], or smart card authentication, etc.
Cloud terminals include personal computer, mobile phone,
etc. Each terminal user is assigned a smart card with unique
identity in this solution, including USB key, SD key, etc. thesesmart cards provide encryption and decryption functions.
Users perform smart card authentication and password
authentication while login application systems.

7.1 Initialization
While initializing, authentication server update user’s smart
key information, set up the distribution system identity, user
identity, key seeds and transmission key. Meanwhile set up therelated parameters of encryption cards or encryption machineat server, including transmission key, storage key, etc.
7.2 Registration
If a user needsto access cloud resources, a user should register
firstly. When asking for registration request, users enterusername and password, and select whether use the proposed
multi-factor authentication based on smart key or not, and thensent them to cloud server.
After receiving the information, cloud server checks theusername. If it has been registered, the user need enter anotherusername, if not, complete the registration process. For thosemulti-factor authentication users, cloud server label andstoretheir user identity, and carry out the multi-factor
authentication protocol when authenticating.
7.4 Authentication Protocol
The proposed asynchronous challenge-response solution
presents time factor as a challenge to the user when it tries tologin cloud server. Each time cloud server generates a newchallenge for user when it wants to login. This challenge is
obtained and injected by the client software. Then the client
software uses the smart card to generate a unique random
number factor, and then use the cryptography technique togenerate a unique password according to challenge andrandom number factor. The cloud server checks username andpassword, and the authentication server checks the generated
one-time password. Only the authenticated legal user canaccess cloud resources at server. Consequently, the solution

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

TABLE 1.

✓

✓

✓

✓

✓

✓

✓

MFA based on multimodal
biometrics (MFA-MV) for
Cloud Computing✓✓

Anonymous and Efficient
Message Authentication
Scheme for Smart Grid

✓

✓

✓

✓

✓

✓

A Cyber Situational
Awareness Framework Basedon Digital Twins
Prioritized access control
enabling weighted, finegrained protection in CPS

Cloud based authentication

V.

User
Anonymity

MULTIFACTOR AUTHENTICATION COMPARISION
Resistance to
SeveralAttacks

In the following section we will discuss and compare someof popular and improved Multifactor authentication schemes
which we discussed in this paper. in Cyber Situational
Awareness Framework Based on Digital Twins provide anadvanced monitoring, inspection, and testing capabilities.
They also introduced technical use cases illustrate how thesefeatures support operations staff in gaining situation
perception, comprehension, and projection. But it still needs
lots of improvement as author suggested that for further
development effort is required to improve the visualization ofdigital twins and to bring the record-and-replay feature tocompletion.
In Multi-Factor Authentication based on multimodal
Biometric (MFA-MB) for Cloud Computing authors first
discuss about security issues (authentication, identity andaccess management) of cloud computing as well as an
overview of biometrics-based solutions were presented. Next,
they propose FaMSL-MBS which is a multimodal biometricfusion system at the Match Score Level. Later, they present a
multimodal biometric authentication scheme for CC systems
aiming to enhance and to increase the security level in termsof authentication and access control. The proposed approach ismore efficient as it overcome all the drawbacks related toTokens and Passwords authentication technologies based on
single factors and the limitations imposed by UBS.
In Prioritized access control enabling weighted, finegrained protection in CPS author presented a novel access
control mechanism, MFAC, that employs an MF technique tosolve the prioritization problem. In addition, it is implemented
using an ABAC model working well in a large-scale CPS
network. implemented and applied MFAC to a fine-grained
access control scenario in our smart building testbed. The
experiments measured its computation cost and showedreasonable performance. They also develop an automated,prioritized smart building control in which protection levels
changed along with real-time power price.
In Anonymous and Efficient Message Authentication
Scheme for Smart Gridauthor first address the weaknesses in
Li et al.’s scheme, and later proposed an improved message
authentication scheme which does not incur additional
computation and communication costs. A security analysis
demonstrates that our proposed scheme can satisfy various
security requirements for the smart grid. demonstrate how
their improved scheme achieves the mutual authentication,
session key agreement, user anonymity, perfect forward
secrecy, and resistance to several attacks.
After going through with all above example, we conclude
that Multi-factor is getting popular but still it’s not that
popular and as it needs extra step for authentication people
often avoid that. Although some defensemechanisms havebeen proposed/deployed, new and systemspecificsolutions arestill expected in response to the newlyidentified threats andvulnerabilities [26-28].Wang and et al [29] stated that it is noteven possible to build an ideal two-factor authentication

cryptographic
technique

SECURITY ANALYSIS AND COMPARISONS

Session Key
AgreementIV.

system and therefore research into this critical issue willcontinue. Designing a perfectly balanced authentication
solution that fits all is probably infeasible. However, designing
an authenticationsolution that is well suited for a particularapplication is certainly doable.Kumar et al. [30] proposed anefficient two-factor userauthentication framework for wirelesssensor networks, whichis based on password and smart cardand it uses one-way hashfunction. This scheme provides
mutual authentication andgives user facility to change
password at need. But theproblem here is, it does not provide
privileged insider attackas the password is sent to the basestation in plain text. It alsosuffers from the synchronization
problem as it uses the timestamp for avoiding replay attack.

MutualAuthentication

offers good security.

1328

✓

✓

✓

✓

CONCLUSION

With the exponential growth of cyber-physical systems(CPSs), new security challenges have emerged. Various
vulnerabilities, threats, attacks, and controls have been
introduced for the new generation of CPS.In this paper we
discuss the evolution of security from single authentication toMulti-Factor Authentication (MFA) starting from Single-Factor
Authentication (SFA) and through Two-Factor Authentication(2FA). In addition, this paper also presents some of the latest
multifactor authentication scheme available for different part ofCyber Physical System which demonstrate several features offeatures of user authentication in the gadget-free world
including security, privacy, and usability aspects. These areadapted and extended from earlier research on web
authentication methods. In conclusion, we compare all thesemethodologies available and try to compare with some key
attributes such as mutual authentication, Session Key
Agreement, cryptographic technique, Resistance to Several
Attacks and User Anonymity. this paper gives future research
directions and open problems that stem from our observations.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

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[15] H.Li,R.Lu,L.Zhou,B.Yang,andX.Shen,“AnefficientMerkletreebasedauthenticationschemeforsmartgrid,”IEEESystems
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authentication in distributed systems: certain goals are beyond
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Mangal Sain received the M.Sc. degree in computer
application from India in 2003 and the Ph.D. degree incomputer science in 2011. Since 2012, he has been an
Assistant Professor with the Department of Computer
Engineering, Dongseo University, South Korea. His
research interest includes wireless sensor network,
cloud computing, Internet of Things, embedded
systems, and middleware. He has authored over 50
international publications including journals and international
conferences. He is a member of TIIS and a TPC member of more thanten international conferences.
Normurodov Oloviddin received the bachelor
degree from Tashkent University of Information
Technologies named after Muhammad Al-Khwarizmi
in 2018 (Uzbekistan). At the moment he is doing MS
at Dongseo university, South Korea. His area ofinterest is cyber security, Computer vision, Computer
graphics and Network security.
Chen Hong has been an Assistant Professor with the
Department of Linton School of Global Business,
Hannam University, Daejeon, South Korea from2015, She received her MA in International Trade,
GSIS, Pusan National University, South Korea andBA E-Business in Management, Xiang Tan
University, China
Kueh Lee Hui is working as an assistant professor
at the department of Electrical Engineering, Dong-A
University since 2012. She completed her PhD
Degrees from Department of Electrical Engineering,Dong-A University, Korea. In 2009 she completed
her BS degree in Electronic and Communication,
Department of Electronic Engineering, University
Malaysia of Sarawak, Malaysia. She also did MS in2007 from Malaysia.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

1329

Web based Learning: A Methodology to Teach
Literature in a Classroom
Deepanjali Mishra*and Mangal Sain*****

Department School of Humanities KIT University Bhubaneswar, Orissa, India
Division of Information and Computer Engineering, Dongseo University, South Korea
deepanjalimishra2008@gmail.com,mangalsain1@gmail.com

Abstract—The internet has succeeded in emerging as amajor component in our daily lives, and its presence ineducation sector is outstanding. Web-based syllabuses
have been enjoying an upper hand and have succeeded ingetting attention of learners and the instructors. If
presentations at international conferences are any
indication, the interest in this new instructional medium isindeed commanding. Web-based learning involves the useof appropriate technology with the corresponding
pedagogical approaches managed in the context of today's
educational setting. Web-based learning encompasses all
educational interventions that make use of the internet.
There are currently three broad classifications or
configurations within WBL: tutorials, online, discussion
groups, and virtual patients. The distinctions between
these configurations are often blurred, and in fact a given
WBL intervention might use a combination of two or
three, but the implications for teaching warrant a
conceptual, albeit at times arbitrary, separation. Therefore
the objective of this paper is to understand the concept ofWeb based learning and its advantages along with its use
in literature, culture and folklore studies, and the various
methodologies to implement it in a class of Folklore
studies.
Keywords—Web based learning, literature, techniques, folklore,
culture——————————————————————————————
Manuscript received Jan. 10, 2019. This work is a follow-up the invitedjournal to the accepted & presented paper of the 20th International Conference
on Advanced Communication Technology (ICACT2018),"This work was
supported by Dongseo University, "Dongseo Cluster Project" Research Fundof (20200007).
Deepanjali Mishra is with the Department School of Humanities KIT
University Bhubaneswar, Orissa, India (e-mail: deepanjalimishra2008@gmail.com)
Mangal Sain is with the Division of Information and Computer
Engineering, Dongseo University, South Korea. He is the corresponding
author of this paper. (Corresponding author phone: +8251-320-2009; e-mail:mangalsain1@gmail.com).

I.

INTRODUCTION

The learners understand the concept of cultural differences,and get acquainted with the concept of the term review of peers
and enhance their own knowledge. Jakes (2006) confirmed
digital storytelling helps students explore the meaning of theirown experience, give value to it, and communicate theexperience on multiple levels to others. Sadik indicated that
including storytelling in the social studies curriculum develops
students’ understanding of democratic ideals, other culturesand citizenship; improves their communication skills;
motivates them to connect the past and the present; and sharedexperiences.[1]Web-based learning is a type of learning process which
utilises the system of Internet as a mode of delivery method of
instruction for accomplishing different types of activities oflearning. It is an online learning process where the learning
methodology is carried out and conducted without face to faceinteraction between the instructor and the learners, Or it could
also be put in this way where the instructor interacts with thestudents either by being physically present in the classroom or
imparts education through virtual medium depending on theneed of the syllabus. Web-based learning can be integrated intoa curriculum that turns into a full-blown course or as asupplement to traditional courses. It is a form of education
where the resources are imparted between student faculty andstudent-student interactions which take place on the World
Wide Web. There are various online courses which are taken as
primary sources of learning and have successfully caught
attention of faculties as well as learners. Sometimes when it
becomes difficult to be physically present in various
conferences and present their paper, then a scholar can present
his or her paper through video conferencing or sending therecorded video of the presentation by the presenter. At thesame time a scholar can directly watch the proceeding of theconference through various social networking sites. Web-basedlearning includes every intervention related to education whichuses digital mode which is also known as local intranet. Web
Based learning is broadly classified and configured into three
types namely tutorials, online discussion groups, and virtual
patients. This difference between the aforesaid configurations
may not be very much clear and it may be that Web basedlearning could utilise not one or two but multiple combinationof Medias and its implications in teaching generate aconceptualand arbitrary outcome.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

"Web teaching is nothing but building network. It helps tobuild strong bonding with one's students and help them identify
and utilize the resources all over the world irrespective ofdistance. combining different materials – music, motion, text,
narration – into one presentation; collecting related informationfrom multiple sources... enable students to make their own
connections by offering materials for download and use in theirscholarship or by having them construct web documents as partof their coursework. And this process of making meaningfulconnections is at the core of all learning." [2]Web based learning is conceptualized as a mode of online
learning process or e-learning because all the online course
content are included in the database. Discussion forums can becreated online through formation of various groups, pages etc
on various social networking .Web based courses also has
facility of accessing various printed course materials in pdf
form and they also provide http link which can direct a learnerto the course. After clicking on the link, the learner visits thepage in the web and the reader can easily access the course
materials and then again the reader may find hyperlink which
can easily be opened thereby leading to various other linked
documents. There are two types of learning environment which
are VLE, virtual learning environment and MLE, managed
learning environment. The VLE is a combination of functions
operated by discussion boards, chat rooms, online assessment,
tracking of students' use of the web, and course administration.
VLEs are like a general learning environment which provides
information to learners. VLEs have the ability to share
information to the end, and collaborate on different academic
assignments. A managed learning environment is a learning
system of the electronic management which support teaching
and learning. The learners ofMLE could be tutors, students ormentors. Sometimes it is very common to interchangeManagement Learning Environment with Virtual Learning
Environment. However, the basic objective of both of thesecourses is to benefit the learners.
Web based learning is often called online learning or elearning because it includes online course content. Discussion
forums via email, videoconferencing, and live lectures (video
streaming) are all possible through the web. Web based courses
may also provide static pages such as printed course materials.
[3] The advantages of Web based learning is that it provides
ample opportunities to learn and getting assessment to aenormous amount of knowledge and data collection and its
information. The role of teachers is vital to make the learners
culture through web based learning. They also need to takestock of the learning environment that is provided to thestudents which should be learner friendly. It should also beseen that the needs of the learners are adequately supplemented
and taken care of. There are many advantages of Web basedlearning but it has to be supplemented with adequate
technology requirements. If this is not provided, then thestudent can easily get distracted and disturbed. It should not bemade optional in classrooms, rather there should be appropriate
utilization of technology and should never be experimented
with students in a very casual form or out of curiosity.
Sometimes there is a pressure among the teachers to use thistechnology in their classroom which might be interesting to a1330

teacher. In such cases, the learning fails because of lack ofinterest.
The structure of the paper is organized as follows: Section
II presents the past work related to this field. Section 3
describes about the methodologies used for this research work.
Section 4 describes about teaching folklore through web basedlearning. Section 5 describes about the conclusion and future
work.
II.

RELATED WORKS

There have been many definitions about digital storytellingin related literature Porter (2004) defined storytelling as
combining authentic stories with image, music, graphs andvoice over while Dupain and Magure (2005) described it ascreating a story by integrating multimedia elements such as
visuals, audio, video and animation as well as Chung (2007)highlighted it as a harmonious multimedia presentation
composed of digital components such as text, visuals, video
and audio. However, digital storytelling are overall considered
as generating a story about a subject by using multimedia tools
and software and sharing this story in multimedia environment(Nguyen, 2011; Robin, 2008). Nearly all digital stories are
originated via combining components (text, voice-over, video,
music, and visuals) with an authentic story on a particularsubject. Digital stories , altogether, are short videos created byintegrating image, video, background music and audible or
written narration via some basic hardware and software(Microsoft Photo Story, Windows Movie Maker, Wevideo,
Web 2.0 etc.) with authentic story (Wang and Zhan, 2010).
Nevertheless, digital storytelling is not just a plain power point
presentation (Dreon, Kerper, and Landis, 2011). Robin andPierson (2005) noted that digital storytelling is the activity of
producing meaningful stories that reflect individuals’
imagination. Digital stories can have aim of informative,
instructive and personal narration (Robin, 2008). Digital stories
can rise up in different types ranging from personal narratives
or instructional stories to narratives that recount historicalevents and in many different fields ranging from social scienceto science Coutinho, (2010). Banaszewski (2005) pointed outthat educational technologies have progressively taken place ineducation and instruction environment, accordingly, digitalstory have become widespread in classroom setting and invarious fields of study as stated by many academicians,
researchers and educators. Though the term “digitalstorytelling” is, generally familiar to educators, many still don’tknow what it is or how it works.
Digital storytelling is a process where the narration isusually done through first person. It is usually done byshowing visuals like videos, and other multi- media and music
played at the background. The main aspects of storytelling are,it should have a basic idea or viewpoint which addresses aquestion which is important from the point of view of the story
teller as well as to the listener. Two other key features are theeconomical use of words in the written script and appropriate
pacing and rhythm in the oral narration [4] Secondly,
Creativity of the story teller too is an important aspect ofstorytelling. Thirdly, the fluctuations in the tone of the speaker,
because if the narration is monotonous, it will fail to generateinterests among the audience. In case of narrating it throughICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

multimedia, if shocking images or a laughing icon is shown,then listeners would find it interesting. While choosing the
topic, the story teller could choose from a wide range of
biographical, anecdotal, historical, cultural theme depending
upon the audience.
There is no specific discipline of Digital storytelling. It
could be chosen from a wide range of topics and could be evenincorporated as the basic syllabus of language and literacy
courses and its application. Storytelling can be relevant in all
streams of education like humanities, culture, management andengineering at all levels like school, college and professional
degrees. The benefit of using narrative writing at college levelis that it can part of a curriculum development or sometimes it
may also be used in order to motivate those students who needmoral support. In a situation where the instructors find biggest
challenges to provide moral support, narrative story telling will
come to their rescue. . Hung, Hwang, and Huang (2012) haveshown that digital storytelling projects can improve student
motivation and problem- solving abilities. [5] Gunter (2012)
opines that digital storytelling could be beneficial in
motivational aspects, distinguishing between “extrinsic” and“intrinsic” motivation. Gunter further says that students could
be motivated externally by giving them prizes or some marks.
whereas intrinsic motivation could be an “authentic artefact”
in order to establish a “personal investment” [6] thus it could
be said that digital storytelling techniques is reliable andbeneficial to impart external as well as intrinsic motivation
which is very much beneficial in the long run for the students
as well as the teachers. “Newer technologies such as computersand video conferencing are not necessarily better (or worse) for
teaching or learning than older technologies . . . they are justdifferent . . . The choice of technology should be driven by theneeds of the learners and the context in which we are working,not by its novelty.”[7].
The issue that has been mentioned is connected with the useof technology. These issues could be easily sorted out when thestudents get used to computer and know how to handle it. In
order to use the Digital Story telling methodology and teaching
literature is concerned, in the classroom, the instructors must
prepare themselves and should be technology competent. It istrue when Boase (2007) says, “the challenge the digitalstorytelling in education poses is how to harness the massive
potential of the story form, with its possibilities to inspire,
engage, transform, through a process that will endow it with
opportunity for reflection, critical thinking, and problem
solving.” [15]III.

METHODOLOGIESThe Digital storytelling is one of the innovative approachesof giving instructions, learning, planning and result oriented
approach, yielding positive outcome. Iannotti (2005) pacifies,"digital storytelling can be overwhelming if it’s not wellthought out". [8] At the same time, Robin and McNeil (2012)
opine for implementation of an instructional design frameworkby using ADDIE (analyze, design, develop, implement, andevaluate). Another important aspect of teaching through DST is
the instructor does not need to be formal while taking the class.
However one needs to understand various critical issues while
using this story telling technique. The issues are appropriate

1331

technology must be used, there should be support services
availability, the students as well as the instructors should beinterested in technology-based learning. at the same time theeducators must caution the students regarding technicalconsiderations like how to save files, how to get a backup andother sophisticated features. Robin and McNeil (2012), Digital
Storytelling Cookbook (Lambert, 2010),guide the students onthese technical sophistication of computer and multi media.
The software which could be used are Adobe Premiere
Elements, MS MovieMaker, Photostory, and WeVideo which
are available for free versions. WeVideo does not require to be
downloaded because it is cloud based. it is just that the user has
to sign up for a free account. MovieMaker and Adobe Premiere
have a built-in recording feature, while Moviemaker does nothave a built-in recording feature so the students can do sothrough their phones or any other device after which they can
upload it. All the Adobe Reader needs to be brought but it isvery essential because it has so many features that it has theability to generate much advanced quality video. The instructor
can experiment with the free options for those classes withmore number of students. There are many advanced quality
videos that are available which does not have fancy features
and are expensive which might not be needed for the purposeof teaching students.
A Digital Storytelling Pilot Technique was implemented bysome teachers in their classes at a community college in New
York City in 2013 after getting enriched by the workshop thatwas organized on Digital Story Telling. The technique that is
discussed was organised one of the participants of theworkshop who intended to make an assessment regarding thetechnical practicability and pedagogical implications of thisprocess of digital storytelling in an ESL class. [9]It is a fact that the culture of the post modern era isconceptualizedas ‘digital’, ‘cyber culture’, ‘Internet culture’.
For any researcher of culture studies or a folklorist, web based
technology or the tech culture might appear to be morenecessary because digital technology also involves deriving
thousands of years old data and reviving the culture and age old
tradition and retrieving archaic texts through digital remixing
and other methodologies. The modern digital culture plays
pivotal role in continuous process of globalization which isagain conceptualized as“the process of increasing
interconnectedness between societies such that in one part of
the world more and more have effects on peoples and societies
far away”.[10] This over emphasizing feature of globalizationconsist of developing the structures of transnational andtranscontinental framework along with the rampant outgrowthof technology, economy of the market , consumerism,
information and culture ethics and so on. The concept ofdigital culture that is elaborated deviates from the original
concept and it has indeed brought about invention of newcommunication strategies among different people and people
who can easily communicate with their office, institutions,organizations and other professional spheres.
The quantity of e-folklore is vast. Furthermore the numberof outlets is enormous, with source addresses changing
constantly. Much of this outpouring is highly ephemeral innature, but on the other hand it is clear that such ephemera areprolific breeders of new ephemerides on an almost exponential

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

scale. Research in this area is often exasperating as even thebest of Internet search engines fail to reveal stories just a monthor so old – and even solemn prayers to the Internet patron saintSt. Isidore of Seville (Isidorus Hispanensis, 560–636 A.D.)
seem to fall on deaf ears! A characteristic of e-folklore in its
various manifestations is the use of specific language forms.
Probably, partly by virtue of its status as the language oftechnologists, English has become the basis of many new ‘elanguages’, for example Cyberspeak, Netlish, Weblish, Internet
Language, etc. Many natural languages have also absorbed or
adapted English words or acronyms to create, specific ‘edialects’ or ‘e-jargons’. The multilingual term “blog”, forinstance, derived from the English “weblog”, has assumed aninternational meaning and is used world-wide. Sub-jargons
have also emerged for mobile phone and Internet users, often
merged with elements of secret languages or special codes forthe exclusive use of certain groups such as children andprisoners. An example is “A-leet”, also known as “leetspeak”
or just “1337”, a strange slang based upon the transliteration ofwords into a mixture of figures, letters and graphic symbols.
A. Teaching Folklore Through Web Based Learning:
A visualization of a literature or a cultural classroom
would certainly not be a one which imparts teaching throughonline mode because of the fact that the methodology of taking
classes is believed to be conventional which would imply
professors would be taking classes chalkboard method. But
with the advancement of technology, faculties and instructors
have begun using technology in their classrooms as amethodology of teaching. This would be beneficial in making
the learner engaged and succeeded in generating interest
among the students. There are various research guide, manuals
and online websites for the trainers who have developed themethodology of teaching literature, culture and folklore online.
These aids provide theoretical usage as well as practicalmanuals to these faculties which are reader friendly and caneasily be read and incorporated in their teaching. This would
enable the students to learn literature and culture in a virtualenvironment.
B. Preservation of Folklore and Culture Through Digital
Technology:
The concept of cultural heritage safeguarding is linked to thenotion of ethnic and cultural identity, and its modern
connotations give rise to interesting issues surrounding the
problem of identity and multiplied identities created by global
multi-media. On the one hand lie considerations of humanrights and acceptance of individual and collective identity. On
the other, there are the social, political, and economic issues
connected with progressive globalisation and theomnipotential cultural power of electronic devices. The
holistic approach to cultural heritage is the subject of
UNESCO governance on a global level. The definition ofcultural heritage is vast, mainly because the concept contains
an expression of human knowledge and creativity, and theways of life developed by communities and passed on fromgeneration to generation. These include customs, practices,
places, artifacts, artistic expressions and values. It originates
out of the interactions between people and places over time,1332

and influences contemporary life in all aspects; social,economic and technological. From architecture to transport,
technology, museology, ethnic studies, folklore and oral
culture, language, education, and audiovisual heritage amongst
many others. These resources are obviously non-renewable
and unique and as such are of great importance for its
sustainability and benefit to future generations. Furthermore
the definition of cultural heritage is subject to a constantevolution. What one generation considers ‘cultural heritage’
may be rejected by the next generation, only to be revived by asubsequent generation. In summary, the cultural heritage ofany world region consists of all the resources that are inheritedfrom its past in all forms including tangible, intangible and –in the postmodern world – a digital heritage as well. That is
why digital heritage10 safeguarding is a matter not only of thepast but more so of the future.
The heritage digital movement includes not only
digitisation of libraries and archives, but also archiving ofdigitally created sources such as graphics, design, digitalstorytelling, etc. Here we should mention the project called
DIHE (Digital Intangible Heritage of Europe) that has been
Swedish reply to DIHA (Digital Intangible Heritage ofAsia).[11] DIHE was founded in 2012 as a joint venture
between The Interactive Institute and the Digital Heritage
Center in Sweden as “a truly unique initiative, which focuseson aesthetics and creativity by combining artistic developmentwith research in design and technology. In addition, it is areal-life experiment in the organization of cross-discipline
research combining art, design, anthropology, computerscience, interaction design, ethnography and many otherdisciplines”.[12] DIHE develops the interdisciplinary andcross-sectoral methodological approach that spans acrossvisitor and museum studies, interaction design, human
computer interaction, cultural studies and ethnography.
Special attention is put to related fields such as cognitive andaffective disciplines, or cognitive semiotics helping tounderstand the meaning making processes within theframework of communication channels and modalities.
C. Teaching Folklore through Web Based Technology:
The visual practice of web posting differs from the vernacularuse of photography, photocopying, and faxing because it ismore widely available and can be more thoroughly
personalized. In Dundes’s joke, the machine runs by itself; on
the Web, people imagine that they personalize the machine intheir own image and often approach it like a workshop in
which the screen constitutes a virtual canvas or desktop. Users
can arrange and symbolize material on the Web as virtual
reality to create a persona that was literally screened throughpostings and sought-out kindred spirits. The wonder of the
Web is the graphic material open to view—graphic in a visual
sense and also in its uncensored quality, suggesting thefreedom of expression of a folk, or informal, commons inwhich participants regulate action through tradition rather than
through arbitrarily imposed rules. Cognitively, a binary has
been constructed between analog print as the regulated,
institutional world of potential censorship and the digital Web
as the open, uncensored folk domain. Theoretically a wide

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

open field, the Internet’s cultural hangout has proved
especially attractive to youth, who, the public imagines, better
their elders with the informational capital of new media andoften use it as a secret language beyond parental andprofessorial monitoring (Bronner 1995, 232–46; Sullivan2005). In an individualistic society placing faith intechnological progress, the energy of youth is channelled into
innovation that will displace the establishment culture of older
stuffed shirts; fashion, fads, and trends of the young dictate thepopular culture, media and retail outlets remind consumers.
Children embrace the communication potential of the folk
Internet and shape it into their own image and culture
supposedly because they are preoccupied with social andpubertal concerns rather than business applications.
Besides freedom of expression, the Internet putatively
liberates artistic communication from materiality, but hardly
immaterial, the folklore of the Internet is consequential stuff
that invites human participation. In this way, it is conceivableto envision the difference in method and theory between
natural and virtual reality that Kirshenblatt-Gimblett invited
folklorists to contemplate by thinking about the betwixt andbetweencharacteristicofInternet
communication.
Additionally virtual traditions deviate from the definition offolklore as artistic communication in small groups, which Dan
Ben-Amos inventively suggested as a modern definition offolklore in context; in a digital age they appear as layered (and
often non-linear) symbolizations or processes in multivariable,interactive networks (Ben-Amos 1972; Labbo 1996; Laske
1990; Sommerer and Mignonneau 1999). Althoughnaturalistic tradition is often associated with precedent fromway back when, the Internet’s flattening, or disregard, of time
invokes the view that something being on the Internet issufficient to show this pre-existent characteristic of tradition.
The implication is that this something has its own independent
existence that involves an artificial fusion of new and old, text
and image, and creativity and tradition. Folklorists and othercultural scholars may be concerned that in this kind of
tradition the electronic tools of forwarding and copying andpasting standardize and stabilize texts, taking away thevariability that marks cultural identities in natural contexts, but
the serial reproductive process of homepages and forums
appears to foster commentary and communal alteration, oftenwith an instrumentality that signifies cultural space (Baker andBronner 2005, 346).
There are various ways by which folklore can be taught tostudents:

•
•

•

•
•
•

•
•
•
•
•
•

Learning Objectives

To activate prior knowledge and relate it to thereading selection.
To demonstrate grade-level proficiency in writing to
inform and explain concepts, and express personalideas in order to persuade.
To demonstrate grade-level proficiency to read forliteracy experience using before, during, and after
strategies.
To identify meanings of terms unique to literarylanguage.
To identify the structure of literary or narrative text.
To Interact with text using the four stances:
Global understanding
Developing interpretation
Personal reflections and responses
Critical stanceInterpret tall tales.
Present a report to the class in the form of a dramaticmonologue or a news report.
Read for literary experience.
Respond to literature through writing and discussion.
Use strategic reading behaviours to construct, extend,
and examine meaning for a variety of texts.
Write for various audiences and address thefollowing purposes:
To inform
To persuade
To express personal ideasB) Tell Tales:
In this lesson, students are introduced to the genre ofAmerican tall tales. Students are exposed to several
traditional tall tales, then prompted to write an originaltall tale set in contemporary America. The tall tale must
address a current event or issue and must feature a"larger-than-life" main character. The students use
exaggeration and hyperbole to portray the way in which
the main character resolves the issue or problem. Students
then dramatize their tall tales for the class.
Learning Objectives••

A) Teaching Folklore to students:
Student will explore the common elements of folktales
and tall tales while learning how these tales built the spiritof American people. Students will identify the tall taleelements. Students will also write responses to these tales,
including a composition in the form of a monologue or anews report. They will perform these compositions for theclass.

1333

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•
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to Recognize tall tales as a vehicle of entertainment
and identity for the pioneers
to Read for literary experience several American tall
tales from the different regions of the United States
to Identify and analyze components of a tall tale
to Create an original tall tale
to Perform original tall tales for classmates

Teaching Methods• Guided Listening• Discussion

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020•

Lesson Set up

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1334

Small Group Instruction
Individualized Instructiona) Teacher Background
Staging Performance:b) Background knowledge of the students:•••

Have some experience with the writing process.
Know the term “pantomime” and have someexperience of miming actions.
Be familiar with newspapers and the concept ofcurrent events.

Arrange handouts in the order the class will address them. Post
content vocabulary words for easy reference. If you choose toreference props during the telling of the folktale, have themplaced in the appropriate places.
2) Assesment:
Arrange srrange seating so that deaf/hard of hearing
students are close to where instruction will be delivered.
• Blind/Low Vision students will benefit from handouts
with large print or braille.
• English Language Learners may benefit fromsupplemental vocabulary sheets that define literary terms anddifficult words.
• An adjusted workload (brief examples of folktales) will
help struggling/striving readers.
• Interpersonal and Intrapersonal learners may want todevelop the characters in their folktales. Provide a list thatoutlines all folktale components so that their characters do not
overpower the tales.
• Folktales may include animals as characters andhighlight setting. Naturalist learners may benefit from stories
that use animals to relay events and that vividly relay setting.

•c)Grouping Process••

Small Group Instruction
Large Group Instructiond) Performance:
Teacher should prepare a brief story to tell in Step 1 below.
Choose a fairly plain narrative that can be transformed into atall tale: a vacation trip, for example, running an errand, orsomething seen on the way to school.
C) Writing Folktales:
Students identify and analyze folktales. They learn thecharacteristics of folktales and use them to evaluate existingtales and to create original tales of their own. Students apply
the writing process to strengthen writing skills and to develop
creativity.
Learning Objectives
Students will:
•
•
•

•

Identify, examine, analyze, and evaluate folktales
Incorporate the elements of traditional folktales inoriginal folktales of their own
Write for literary purposes and for a variety ofaudiences: peers, teachers, parents, school-widecommunity, and beyond
Pre-write, draft, revise, and proofread as part of thestrategic approach to effective writing

Teaching Methods
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Hands-On Learning
Visual Instruction
Group or Individual Instruction
Guided Practice
Independent PracticeGrouping Process

IV.

LIMITATIONS

A. Technical problems:
Web based learning is no doubt advantageous to thestudents, yet it is not free from drawbacks. As it is media
based, therefore it is evitable that all instructional media failure
can occur. For example, while planning to teach the students
through power-point presentation and it may so happen that
there is a power failure without any power backup. The
traditional method of teaching through chalk board might beless lucrative but it is free from these hassles. The students aswell the teachers might face problems if they are not net savvy
and a small problem could lead to everlasting consequences.
B. Poor instructional design:
There is a variance in the pattern of quality of all theteaching process. But in case of Web based learning, it has tobe explicitly planned and executed because once it is uploaded;
it is seen as well as evaluated by others. And if an instructor isvery much efficient, he/ she can take a class with minimum
preparation which is not possible in case of Web basedlearning. It is also seen that WBL course structure has inferior.
For instance, shoomp.com, spark notes etc’ which publishes
notes and critical analysis of various text books in literature
which cannot be considered as scholarly. These types ofcourses are less advantageous and are not suitable for thelearners and the instructors. WBL also pose challenges of
implementing online with unknown users.

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 9, Issue 6, November 2020

C. Ignorance of the users as well as the trainers:
There It is also seen that the users are not much techno
savvy and hence they have difficulty in handling technology
during the classes. Similarly there are some trainers who arealso unaware of the process of using computers and online
technology to impart education due to which the education
cannot be imparted.
V.

CONCLUSION

Technological expansion which is taking place at a very
faster rate with increased software capacity has succeeded inmotivating the learners and the educators. The rapid expansionof the Internet and increasing software capabilities are
influencing the entire teaching process at different starts. The
students learn how to learn, organize, build and relate. Their
analytical skills develop. The students get to know about
different culture along with their own. Their writing skills
develop in a very constructive way. During presentation of thestory, the student's oral skills also develop. The Students wereadvised to use Skype as an option, but it was not possible forsome students to get an access to Skype.However, due to thecollaborative efforts of the students with a positivity attitude
appeared to enrich the projects quality in which they are
involved. The students collaborated with their fellow students
of different sections but sometimes it was difficult for thosestudents to use computers who stay in the campus premises ofa non commuter college. Therefore it is suggested to allow thestudents more time for such classes which can give peer
feedback as well as teachers comments. The students could
involve themselves in a group activity where a group of 7 to 8
students can form a story together thereby each student
supporting other students in the group. It is definitely true
when Iannotti (2005) states, "Having fewer projects to manage
would make it more feasible for the instructor to provide
substantial feedback to students. As with the implementation of
any new tool, digital storytelling requires a high level ofcommitment and effort throughout the process. [13] ". As awhole, there are benefits of the process which involves
teaching and learning. The process is very easy because oncethe student knows how to use the technology it becomes lessweird. The learner will understand how much beneficial it is to
the them. Most of the digital preservation projects focus mainly
on digitizing artifacts, in which the crafts of how to make themand skills of how to use them are neglected. Besides, folklore
and religious rituals embed spiritual meanings. Step-by-stepprocedure of a ritual is not trivial for a people or a religion. A
website served as an extension to digital library of folklore
artifacts has been constructed to be used as an e-learning
platform for folklore education in obligatory and higher
education. The system not only constructs a digital library forfolklore preservation but provides instructional interactive
materials with media richness to support a more effective
method for folklore education than non-interactive or
traditional classroom learning. [14]1335

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[13] Robin, B.,“The effective uses of digital storytelling as a teaching andlearning tool”Handbook of research on teaching literacy through thecommunicative and visual arts New York. Lawrence Erlbaum
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Deepanjali Mishra has completed her PhD in English
from Utkal University, Bhubaneswar. Area of interest
includes Feminism and Gender Studies, Sociology,
Linguistics, Management. Has over 15 years ofteaching and research experience "Published a booktitled I am A Woman". Organized a National Workshop andInternational Conference in December 2017.
Mangal Sain received the M.Sc. degree in computer
application from India in 2003 and the Ph.D. degree incomputer science in 2011. Since 2012, he has been an
Assistant Professor with the Department of Computer
Engineering, Dongseo University, South Korea. His
research interest includes wireless sensor network,
cloud computing, Internet of Things, embedded
systems, and middleware. He has authored over 70 international
publications including journals and international conferences. He is amember of TIIS and a TPC member of more thanteninternationalconference.