Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
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ICACT Transactions on the Advanced Communications Technology (TACT) Editors
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
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Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
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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
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Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
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Dr. Ho-Jin CHOI, KAIST(Univ), Korea
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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 ⓒ 2014 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

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 3 Issue. 1

1

Ecosystem-Driven Design of In-Home Terminals Based on Open Platform for the Internet-of-Things

5J

369

Zhibo Pang*, Junzhe Tian*** ABB AB, Västmanland, Sweden, ** Royal Institute of Technology, Sweden

2

Concept-Based Readability Measurement and Adjustment for Web Services Descriptions

378

Pananya Sripairojthikoon, Twittie Senivongse
Chulalongkorn University, Bangkok, Thailand

3

An Empirical Assessment of Quasi-Permanently Vacant Channels in Mobile Communication Bands for Cognitive
Radio

389

Neeta Nathani*, Gopal Chand Manna**, S.B.Mule**G.H. Raisoni College of Engineering, Nagpur, India. **Indian Telecommunication Service, BSNL, Jabalpur, India

4

Filtering Performance of Reducing the Sampling Rate of Sound Card: Perspectives on Different Signal-to-Noise
Ratios395

1,2Bing-Yuh Lu, 3Li-Man Tseng, 3Ling-Yuan Hsu, 1Yu-Lin Weng,1,*Shr-Hong Tang, 3Man-Na Hsueh, 4,*Huey-Dong Wu, 5,6,*Jin-Shin Lai
1,3 Catholic St. Mary’s Junior College of Medicine, Nursing, and Management, Taiwan. 2 Tungnan University, Taiwan. 4 National Taiwan University
Hospital, Taiwan. 5 National Taiwan University Hospital and College of Medicine, National Taiwan University, Taiwan.6 Health Science and Wellness
Center National Taiwan University, Taiwan

5402Techniques for System of Systems Engineering in Construction of a Smart Tourism Industry Information System
Liming Bai*
* Guangxi University of Finance and Economics,P.R.CHINA

ICACT Transactions on Advanced Communications Technology
Technology(TACT)(TACT) Vol.
Vol. 3,3, Issue
Issue 1,
1, January
January 2014
2014

369
369

Ecosystem-Driven Design of In-Home
Terminals Based on Open Platform for theInternet-of-Things
Zhibo Pang*, Junzhe Tian*** ABB AB, Corporate Research, Forskargränd 7, 72178, Västerås, Västmanland, Sweden** Royal Institute of Technology, Department of Electronic Systems, Isafjordsgatan 39, 16440, Kista, Sweden
pang.zhibo@se.abb.com, junzhe@kth.se

Abstract—In-home healthcare services based on theInternet-of-Things (IoT) have great business potentials. To turn it
into reality, a business ecosystem should be established first.
Technical solutions should therefore aim for a cooperative
ecosystem by meeting the interoperability, security, and system
integration requirements. In this paper, we propose an
ecosystem-driven design strategy and apply it in the design of an
open-platform-based in-home healthcare terminal. A cooperative
business ecosystem is formulated by merging the traditional
healthcare and mobile internet ecosystems. To support theecosystem in practical technology and business development,
ecosystem-driven standardization efforts, security mechanisms,
terminal design principles, and data handling schemes areanalyzed and corresponding solutions or guidelines are presented.
Thirdly, to verify the proposed design strategy and guidelines, an
open-platform-based terminal is implemented and demonstrated
by a prototyping system.
Index Terms—Ecosystem-Driven Design; Internet-of-Things(IoT); In-Home Healthcare Station (IHHS); Open Platform;

I. INTRODUCTIONT

HE revolution of Internet-of-Things (IoT) is reshaping
the modern healthcare with promising economic and social
prospects [1-3]. Powered by its ubiquitous identification,
sensing, and communication capacities, all objects in thehealthcare systems (people, equipment, medicine, etc.) can betracked and/or monitored on a 24/7 basis [4]. Enabled by its
global connectivity, all the healthcare related information(logistics, diagnosis, therapy, recovery, medication,
management, finance, and even daily activity) can be collected,
managed, and shared efficiently. By using the personal
computing devices (laptop, mobile phone, tablet, etc.) andmobile internet access (WiFi, 3G, LTE, etc.), the IoT-based

Manuscript received July 29, 2013. This work was supported in part by the
VINN Excellence Center of iPack at the Royal Institute of Technology.
Zhibo Pang is with ABB AB, Corporate Research, Forskargränd 7, 72178,
Västerås, Västmanland, Sweden (phone: +46-21 325104; fax: +46-21 323212;
e-mail: pang.zhibo@se.abb.com ).
Junzhe Tian is with Royal Institute of Technology, Department of Electronic
Systems, Isafjordsgatan 39, 16440, Kista, Sweden. (e-mail: junzhe@kth.se).

healthcare services can be mobile and personalized [5-7]. Large
user base and maturated ecosystem of traditional mobileinternet service have significantly sped up the development of
the IoT-powered in-home healthcare (IHH) services, so-called
Health-IoT. At the same time, the Health-IoT extends the
traditional mobile internet services to a new application area.
Especially after the open-source operation systems, such as
Android [8], were introduced and broadly applied, theHealth-IoT has been expected to be one of the “killer”
applications of the IoT. Therefore the development of
Health-IoT solution based on open platform has become a hot
topic.
In recent years, a number of single point devices andapplications have been developed. But as required by the
economy of scale, a generic architecture is needed to support
various applications by a common IoT platform. So, morecomprehensive study is needed. Moreover, this generalarchitecture should be feasible not only from technicalpoint-of-view but also from business point-of-view. Comparing
to the traditional mobile internet ecosystem, the Health-IoT
ecosystem is much more complicated as more stakeholders are
involved. To create sustainable Health-IoT services, theestablishment of a cooperative ecosystem is primarily importantto the whole industry. Such ecosystem should deliver enoughadded values to all stakeholders instead of a part. High levelarchitectures of all technical aspects such as security,interoperability, and enterprise information system (EIS)
integration, should serve for this goal. Therefore,
ecosystem-driven design strategy is necessary in the early stageof technical development. The exiting research on this topic isvery rare.
In this paper, extending our previous works in [14-18], an
ecosystem-driven design strategy for the Health-IoT
applications is presented and demonstrated.
First, a cooperative ecosystem of Health-IoT is formulated by
Value Chain Analysis of the traditional healthcare and mobileinternet ecosystems. The two traditional business ecosystems
are destructed and merged into one new ecosystem.
Second, as the ecosystem of Health-IoT is established uponshared infrastructures, the interoperability of devices from

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ICACT Transactions on Advanced Communications Technology(TACT) Vol.
Vol. 3,3, Issue
Issue 1,
1, January
January 20142014Technology(TACT)

different suppliers is important. By reviewing existing
standardization efforts on device interoperability, we propose a
set of simplified interfaces among different actors within theecosystem.
Third, to support fair distribution of benefits among all
stakeholders, value-centric security schemes are proposed,
including the public authority-based authentication, the secure
element (SE) based cryptography, and the non-invasive
message handover.
Forth, in order to achieve the economy of scale, an IHH
Station (IHHS) is proposed as a universal platform for device
and service integration and convergence. Design principles ofthe open-platform-based terminals are detailed.
Fifth, ecosystem-driven data handling schemes are
introduced including layered data compression andself-contained data formatting.
Finally, to verify the concepts and technical feasibilities, wehave developed a prototype system called iMedBox. It is a
specific case for medication management and in-homemonitoring applications. The iMedBox hardware, software andbackbone system are implemented and evaluated by fielddemonstrations. The positive feedbacks have proven thefeasibility of proposed design methods, proposed architecturesand solutions. Based on the results of this paper, economicallyfeasible services are closer to reality.
The rest of this paper is organized as follows. The ecosystemanalysis is presented in section II. The standardization efforts
are reviewed in section III. The security schemes are presented
in section IV. The design principles of open-platform-based
terminals are given in section V. The data handling mechanisms
are introduced in section VI. The implementation of the
prototyping system and experimental results are discussed in
section VII, and concluded in section VIII.

values to all of stakeholders in that ecosystem?” Hence, theecosystem analysis is the first step of our work.
B. Ecosystems of traditional healthcare and mobile internet
As shown in Fig. 1 (a) and (b), the ecosystems of traditional
healthcare service and traditional mobile internet service areformularized and compared. The main stakeholders involved inboth of them can be classified into four roles: financial sources,
means suppliers, service providers, and end users. The service
providers are the actor of service execution and delivery. Meansproviders provide necessary materials, tools, supplies, etc. tothe service providers but seldom face the end users directly.
Products and services mainly flow from means providers,through service providers, to end users. Payments (obligatory or
optional, depending on different cases) flow back from end
users, through financial sources, to the means providers andservice providers. Thus a close-loop value chain is established.
It is exactly the “close-loop” feature that makes the ecosystemeconomically sustainable. Win-win cooperation is enabled only
if every stakeholder’s benefit is guaranteed.
Financial Sources

Means Suppliers

Service Providers

End Users

taxes
PublicAuthority

healthcare

Hospital
Equipment
Providerfees

equip.
Caretaker

Insurance
Company

healthcare

Pharmaceutical medicine
PharmacyEnterpriseCharity
Organization

Patient

medicine

donation(a)

Product&sercice
Obligatory payment
Optional payment
Sensitive information

Income sharing

Application application
Designerpurchase

II. ECOSYSTEM RECONSTRUCTION
A. Lessons from the failure of Google Health
Since Jan 1, 2012, as one of the most famous Health-IoT
business efforts, the Google Health service has been
discontinued [9]. This has been looked as a big setback. It isdifficult to assert the exact reason but we can learn some lessons
by analyzing the possible reasons. According to the summary of
possible reasons listed by Brian Dolan [10], seven of the tenreasons are related to the establishment of ecosystem: theGoogle Health was not trustworthy (lack of public authority),
not fun or social, not involving doctors, not partnering withinsurance companies, hard to overcome the current
reimbursement barriers, lack of advertising opportunity, andnot useful to consumers.
This finding is consistent with the prediction of ITU when thevision of IoT was introduced: “the Internet of Things will occur
within a new ecosystem that will be driven by a number of key
players” [11]. Before developing the technical solutions, it ismore important clearly answer “how to establish a newcooperative ecosystem, and how to deliver enough added

370
370

pay
buy

advertising
Advertiser

Platform
Providerpurchase
ApplicationStore

download

HW&SW

Content
content
ProviderIncome sharing

Telecom
Operator

telecom
servicepackage

Consumersubscription(b)Fig. 1. Business ecosystems of (a) traditional healthcare service, (b) traditional
mobile internet service

In spite of the above mentioned similarity, we can see
significant differences between the two ecosystems. Firstly, thehealthcare service ecosystem has more complicated financial
sources. Despite the diverse policies in different countries, thepublic authority and insurance company are the most importantfinancial sources, and thus have the highest influence on therules of healthcare services. Another important difference isrelated to privacy and security. The healthcare services deal
with privacies of end users which are much more sensitive thanthat in the mobile internet services. As a result, in the traditional

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ICACT Transactions
Transactions on
on Advanced
Advanced Communications
Communications Technology(TACT)Technology (TACT)Vol.
Vol.3,3,Issue
Issue1,1,January
January2014
2014
ICACT

healthcare ecosystem, the privacy information flows within theservice providers strictly limited by regulations that have been
well established and accepted. These two major differences are
the main concerns and drivers when we formulate the new
Health-IoT ecosystem.
C. The proposed Health-IoT ecosystem
As shown in Fig. 2 the new Health-IoT ecosystem is proposedand formulated by merging the two traditional ecosystems.
Obviously the Health-IoT service is a business established onshared infrastructures including the internet backend facilities,
core networks, access networks, and mobile terminals.

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service providers rather than “create” such contents bythemselves. The healthcare financial sources should encourageand protect such cooperation by paying to the content providers
directly or through healthcare service providers.
Furthermore, the privacy regulations and publicauthentications should be applied to the content providers andtelecom operators, as strictly as they are applied to thehealthcare service providers. This is the primary precondition
for the end users to agree on uploading and managing theirprivacy through these channels. Besides the legislative
approaches, technical approaches should also be in place tomake sure only the owner and specially authorized individuals
can access the private information. These principles are thefoundation of the proposed security schemes.
Additionally, the advertisers should be authorized to provide
specific advertisement services for both healthcare means
providers and healthcare service providers. But thisadvertisement shouldn’t invade any patient’s privacy. It is
important to be aware that advertisement is the most mature andtrusted business model of the mobile internet ecosystem.
Respect on such well-established business model is essential toinitiate new businesses.
III. ECOSYSTEM-DRIVEN STANDARDIZATION EFFORTS

Fig. 2. The proposed cooperative ecosystem of the Health-IoT service

In this ecosystem, the healthcare service providers (likehospitals, elderly houses, pharmaceutical enterprises etc.) andhealthcare financial sources (like public authorities, insurancecompanies, etc.) have larger influence than other stakeholders.
The content providers (like Google, Amazon, Facebook, etc.)
and telecom operators (like China Mobile, Vodafone, Verizon,etc.) cannot rule the ecosystem anymore. Large mobile device
providers (like Apple, Nokia, Samsung, etc.) and medical
device providers (like Roche, Omron, Philips, Johnson, etc.)
should cooperate more than before to ensure the interoperabilityof their products. Due to the application-store-based software
distribution model, consumers and application developers get
more fairness in the ecosystem.
The cooperation between traditional healthcare service
providers and internet content providers is the key to bring theecosystem into reality. On one hand, the healthcare service
providers don’t need to establish new extra infrastructures (likedata centers, servers, software and other backend systems) by
their own. Instead, they should make use of the existing
infrastructures owned by the internet content providers. In thiscase, the contents of healthcare services are delivered to the end
users through the channels of telecom operators. On the other
hand, the internet content providers, as well as telecom
operators, should get the healthcare contents from healthcare

Given the formulation of the new Health-IoT ecosystem,
specific technical requirements can be derived morecomprehensively and clearly. For example, the standardizationof interfaces between any two actors within the ecosystem arenecessary to ensure interoperability. The standardization of
Health-IoT technologies should be ecosystem-driven instead oftechnology-driven. As shown in Fig. 3 three types of interfaces
should be standardized.
A. Interoperability of devices
Firstly, the hardware and software interfaces between
healthcare means suppliers and mobile application designers,
and between means suppliers and mobile platform providers.
For these interfaces, the Continua Health Alliance (CHA), a
major standardization body working on device levelinteroperability, has recommended the Bluetooth Health Device
Profiles (HDP), USB Personal Healthcare Device Profile and
ZigBee Health Care Profile. They all apply a common data
format specified by the ISO/IEEE 11073 family.
Based on these standards, the mobile platform providers andapplication designers can make a common driver for the sameclass of medical devices from different manufacturers. And
then, the mobile devices can recognize a particular medical
device according to its hardware descriptor and automatically
apply correct data parsing and communication protocols. Thus,
the complexity of patients’ operation, hardware and software
costs, and hence time to market, can be significantly reduced.

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then it is transformed into a service repository. All security
credentials are recorded by the repository and supervised by the
public authority.
D. Keep the service logic proprietary
It is necessary to mention that the service logic should be left
proprietary instead of standardized to encourage differential
competition. The healthcare service providers can customize
proprietary apps from application designers to accomplish
specific value-added services. This point is supplementary to
the efforts of IHE which promotes the coordinated use of
established standards such as HL7 to address specific clinicalneed in support of optimal patient care.
IV. ECOSYSTEM-DRIVEN SECURITY MECHANISMS

Fig. 3. Technical interfaces between actors (a) without standardization, and (b)with standardized hardware interfaces, data formats, and security schemes

B. Electronic health record (EHR)
Secondly, the format of electronic health record (EHR)
should be standardized. The HL7, EN 13606 (specified by
European Committee for Standardization), and ISO 18308 are
the major efforts for this purpose. These EHR standards define
1) the protocol to exchange EHR messages; 2) the contents andstructures EHR data, and 3) the mechanisms to ensure privacy
and security of information sharing. By applying the EHR
standards, the technical negotiations between healthcare service
providers and mobile platform providers are simplified or
hopefully avoided. The negotiation between healthcare service
providers and application designers are simplified too.
C. Uniform security mechanisms
Thirdly, security schemes throughout the entire ecosystemshould be standardized. Otherwise, all the parties would
certainly intend to specify their own security mechanisms toprotect their information as well as business benefits. However,
the existing standardization efforts have not provided a solution
so far. The root-cause is, the motivation of these standardization
efforts on the security aspects is opposite to the “cooperative
ecosystem”. Device vendors and service providers mostly
prefer to keep the security mechanisms to be proprietary aiming
for the so-called “vendor lock-in”. Unfortunately, it is themarket itself that is “locked”!
In the solution that will be presented in the next section of thispaper, to be trusted by the whole ecosystem, the traditional
application store should be accredited by public authorities, andA healthy ecosystem should protect the benefits of all
stakeholders by balancing the control and avoiding monopoly.
In the Health-IoT ecosystem, security mechanisms are theprimary technical means to do so (of course, there are many
non-technical measures that should be applied, but they are outof the scope of this paper). As the patients never believe thecontent providers and telecom operators can really protect theirprivacy [10], the only solution is to accredit an independent
mediator, here the service repository, by the public authority.
Principally, only the owner of the private information (thepatient and his/her healthcare service provider) can access theinformation.
Based on the above considerations, the security schemes areproposed in Fig. 4 The public authority, service repository,
healthcare service provider, content provider, telecom operator,
and patient are the main actors in these security schemes.
A. Public-based authentication
As illustrated by the step1~14 to launch a particular
Health-IoT service to the market, the enterprises should get
authentication from the public authority first. The
authentication is granted in the form of credential, so-called
Secrete, which is a set of cryptography software running in thetrusted hardware. The Secrete of each actor should be handed
over by superior and safe approach. For example, it can beregistered and delivered in person and delivered by accredited
couriers. Only a certain person of a certain service provider canaccess a certain patient’s information. That is, the authentication
is tied to individuals instead of organizations.
B. Repository-based credential management
The service repository maintains all the Secretes of actors intrusted facilities. The content provider and telecom operator
only maintain their own Secrets, which ensures the non-invasive
message handover in-between patient and healthcare provider.
The service provider maintains its own Secrete locally and canrequest other actors’ keys from service repository.
C. SE-based cryptography
The secure element (SE) is a secured device that can store andexecute specific cryptography algorithm. The algorithm iswritten by issuer and is extremely difficult to hack. A typical SE
is the SIM (subscriber identification module) card that is

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Fig. 4. The proposed security schemescommonly used in mobile telecom services [12]. When amessage is sent from the sender, it is encrypted and a signature
is attached. Both the encryption key and signature are generated
dynamically by the sender’s SE. When the receiver receives themessage, it sends the signature to its own SE. If the receiver has
been authorized, there should be a copy (symmetric or
asymmetric) of the sender’s Secret in the receiver’s SE, so thatthe receiver’s SE can derive the decryption key. Only ifeverything (the signature, encryption key, decryption key, andSecrets in the SEs of both-sides) matches, the message can bedecrypted. Furthermore, the Secrets in the SE are readable andwritable only by the issuer (here the public authority). So,
although the patient’s SE contains the Secrets of other actors,
the Secrets are not disclosed. And the communication between
apps and SE is local within professional equipment (e.g. theHealth-IoT Station). This reduces the security risk further.
If the SIM card is used as the SE, the logistics of SIM-cardmanagement will be significantly different with that in
traditional telecom services. Traditionally, the SIM card is fully
issued and supervised by the telecom operator. But in
Health-IoT services, the SIM card should be issued andsupervised by the public authority, and the telecom operator can
only manage a part of the SIM as predefined by the publicauthority. This change may cause resistance by telecom
operators. It should be resolved mainly by non-technical means,such as policy enforcement and financial compensation. And
the telecom industry has also prepared technical solutions suchas the remote subscription and SIM supervision [13].

other words, the Health-IoT streams are transparent to them.
This mechanism is called non-invasive message handoverwhich is essential to get trusted from the end users and financial
sources.
V. ECOSYSTEM-DRIVEN TERMINAL DESIGNIn order to establish the proposed Health-IoT ecosystem, thein-home healthcare station (IHHS) is the most important
infrastructure that can be shared by all actors in the ecosystem.
Therefore it should not be a close system. Instead, it should be
open to third party applications like telecommunication andentertainment, so that e.g. the content suppliers and telecom
operators can deliver other value-added services through it.
By installing specific apps, the IHHS can transform into many
variants, from the logbook, to fatal monitor, wheel chain
controller, portable monitor, smart walker, and medicine box(Fig.5). It is suitable for mass production with low cost as it is a“standard” product. It is also broadly acceptable by the wholeecosystem as it is based on an “open” platform.
Mobile Phone

Logbook

Wearable

Wheelchair

WalkerFatal Monitoror

Autism Tool

Medicine Box

D. Non-invasive message handover
The information from patient to service provider andresponse from service provider to patient are illustrated in thestep 51~60 and step 61~64 respectively. As the content provider
and telecom operator only maintain their own Secrets, they haveno access to the messages transmitted through their facilities. In

Fig. 5. Hardware variants of the Health-IoT Station based on open platform

Before look into the technical details, several designprinciples for the terminal should be clarified first. These

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principles are the foundation for the design of hardware andsoftware architectures of a particular solution. More details arealso presented in a previous article [15].
A. Common platform based on 3C products
As required by the business ecosystem, the terminal hardware
should be based on a mass produced 3C (computing,communication, consumer) platform such as mobile phone andtablet PC. The huge volume of 3C products has significantly
minimized the development costs. The latest semiconductor andsoftware techniques are adopted timely. So the IHHS providers
can introduce their products with the shortest time-to-market
and best performance-to-cost ratio. Performance, user
experience, and maturity of developer community are thecriteria for the platform selection. Well established software
distribution channels are also important to establish theproposed Health-IoT ecosystem.
B. Certification of the Health Extension
The IHHS provider needs to customize the specific parts,
so-called the Health Extension, of the terminal. These parts are
not standard peripherals in the 3C platform, e.g. some specificmechanical structures and interfaces to biomedical sensors anddevice. The Health Extension is the importance entrance for thehealthcare service providers and public authorities to be
involved in the Health-IoT ecosystem. So, the IHHS provider
must cooperate with them to offer a complete solution. In
particular, the healthcare service providers can customize
proprietary Health Extension based on their know-how andservice contents. Only the certified products can access theircontents. The public authorities can specify regulations tocertify the Health Extension. Only the certified products andservices can get the financial supports, such as thereimbursement of cost. This is an important technical measure
to guarantee the Health-IoT is under public supervision.
C. Interoperability and extendibility
Devices from third parties are integrated to the IHHS throughthe device adaptors in the Health Extension. The
aforementioned standardized interfaces for devices fromvarious vendors should be supported, such as the ones
recommended by the Continua Health Alliance: the Bluetooth
Health Device Profiles, USB Personal Healthcare Device
Profile and ZigBee Health Care Profile. However the IHHS
developers cannot expect one single interface can fit all devices.
Most of the popular interfaces should be supported such as
USB, Bluetooth, WiFi, NFC (near field communication), RFID,
WSN, etc. At the same time, extendibility for future standards
should be reserved too.
D. Convenient and trusted software distribution
The application software for a particular Health-IoT service
should be distributed through the application stores which haveessentially enabled the mobile internet ecosystems. It is friendly
both to the users and to the apps developers. The drivers for the
Health Extension should be either natively integrated in the 3C
platform or installed together with the apps, so that the device
and service can be plug-and-play. At the same time, the374

publishing and installation of Health-IoT should be supervisedby the public authorities. So the IHHS solution should provide
trusted user identification, authentication and payment
mechanisms e.g. through secured NFC interface. Solid security
is ensured by the secure element which could be the SIM card,
the NFC card, or an embedded secure device.
E. Standardized and secured EHR handling
The handling of EHR data is the center of information andservice integration. For this purpose, a virtual local database isnecessary to isolate the apps from different parties. The formatof data exchanged between devices, apps, and backend systems
should follow international standards such as the ISO/IEEE
11073, EN 13606, and ISO 18308 specifications. At the sametime the access of EHR data should be authorized andcontrolled strictly, e.g. complying with the mechanisms
proposed above.
F. Efficient service composition
The service oriented architecture (SOA) has been broadly
adopted in the integration of healthcare services [15]. But toexecute the intensive SOA middleware on the mobile platform
is still challenging. State-of-the-art simplification andoptimization are necessary.
G. Efficient information integration
The IHHS needs to efficiently process not only the vital
signals from biomedical sensors but also the human activities
collected by many other sensors such as microphone, camera
and infrared sensor. The processing of multimedia data anddiagnostic analysis of vital signals are computation-intensive.
The popularity of multicore processors in 3C platforms has
made it possible to perform such intensive tasks on a low cost
IHHS [14]. Correspondingly, the terminal software architecture
should be optimized to make the best use of the parallel
computation capacity of multicore processors. This will befurther introduced in the next section.

VI. ECOSYSTEM-DRIVEN DATA HANDLING
A. Data flows
As shown in Fig.6, the main data flows between the threeparts of a Health-IoT system, the service backend, local station,and sensor devices are illustrated. The raw data are collected by
the sensor devices with more or less compression because of thelow processing performance of the sensor nodes. Here needs a
trade-off between the processing speed, transmission power
consumption, and price. Based on the data load distribution
along the path, the nodes in the network are designed in two
types called Main Nodes and Sub Nodes to maximize theutilization of the network resource. So the whole data flow starts
from the sub nodes of the sensor network, path through the main
nodes and be first packed and transmitted to the IHHS.
In the IHHS, local database is designed to manage the dataflows and as the uniform data interface between the applications
from various service suppliers. Usually the IHHS device isGiRI(Global
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much more powerful than the sensor nodes, so higher efficiency
data compression and visualization can be implemented. The
IHHS should also receive data from the service backend e.g. thedoctor side or the hospital side. These data could indicate the
new schedule of medication, sampling data command, etc.
These data are saved into the local database and presented to theuser. At the same time compressed or re-packetized data aresent to the service backend.
More complex data analysis and diagnosis tools could be
involved in the service backend. Bigger databases should beallocated to manage all the data from the whole system. Here
service providers e.g. doctors analyze the uplink data andgenerate the feedback data. Feedback data will be sent back to
the IHHS and presented to the user.

Fig. 6. Data flows in the Health-IoT system

B. Self-contained data formatting
As mentioned before, the standardized interfaces can improve
the interoperability between devices and applications.
Complementary approaches are also needed because 1) the
number of standardized interfaces is still quite big; 2) it takes
time until a few dominative standards are proven in practice;and 3) as the root cause, the ecosystem is open and welcomesnew players and new solutions all the time.
As a complementary technical solution, a self-contained data
formatting scheme is proposed. As shown in Fig.7, the data
between the sensor devices and IHHS, and the IHHS between
the service backend are packetized together with concrete
descriptions. Each packet has its own header segment. At the
beginning of the header segment, there is an optional title toidentify the data packets e.g. from which device or patients. And
the following bytes are the description bytes. It uses flags todescribe the characters of the payload in a packet, whichcontains the data information.

Fig. 7. Self-contained data formatting

The descriptions contain all the necessary information to passand further process the data in the payload such as the packet
size, how many samples are contained by this packet, whichkind of sensor is used, the node id of the samples, compressing

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algorithm used for these samples, the compressing step, etc. For
example, if the data is from a wearable ECG sensor device, its
description segment may contain the manufacturer’s
information, sample rate, resolution, length, and thecompressing/decompressing algorithms if the raw data iscompressed.
Because the flag-scheme is used, the description parameters
can be updated easily. New features can be integrated andunsupported features can be removed later. After the header
segment, there is the data segment. It is the main body of thepacket. It contains all the samples data. All the data shouldmatch the described structure. At the end of the packet there isan optional end segment. It indicated the end of the packet.
C. Layered compression
For most of data compression algorithms, after several
compression phases, the data distortion caused by thecompressing/decompressing usually gets worse and worse, andthe computational complexity gets higher and higher. In the
Health-IoT system, from the frontend sensor devices to theremote server, the data content is bigger and bigger, and thedevice processing speed is faster and faster. So the propose
layered compression is to divide the whole data compression
process into multiple steps. Based on different processing
capacity of the devices, different process load is allocated. Aftereach step, the data size becomes smaller and smaller. This will
help to reduce the traffic load and transmission energy
consumption of the second half of data path. Taking theadvantage of the self-contained data formatting, data packets
from one step contains all the information for the next step to
decode and further compress the data.
For example, if the compression algorithm in the former stepis based on a dictionary, this dictionary can be contained in thedescription of the packets. Moreover, because the system
focuses on healthcare application and the data characteristics
are stable, it is possible to use predefined parameters in thecompressing algorithm. Saving these values together with thesoftware in different devices, and then there is no need to send
the compressing parameters with the data packets. Then along
the whole data path, the packet format could keep the same.

VII. THE PROTOTYPING SYSTEM
A. Application scenario
To verify the concepts and design strategy proposed in thispaper, a prototype system has been implemented and evaluatedin field trials. As a typical case of the IHHS, application
scenario of an iMedBox system has been proposed in previous
work [14-17]. The proposed IHHS solution is based on open
source operation systems like the Google Android. It uses
standard mobile internet terminal hardware, such as tablet PC,
provided by various 3C (consumer, communication andcomputing) manufactures. The iMedBox system is a typicalinstance of the above terminal design principles.
As shown in Fig.8, a powerful intelligent medicine box(iMedBox) works not only as in-home medicine container, but

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also as a “medication inspector”, and an “onsite examiner” indaily healthcare monitoring. It connects to the healthcare
service providers through 3G and Wi-Fi networks andcommunicates to medical devices through USB, NFC, RFID,
and WSN (wireless sensor network). Medication and fatal
information is transmitted to the backbone systems andfeedback from service providers is presented to the patient at
home.
Health social network
services

Telemedicine
services

Emergency
services

Medication
services

Wide Area
Network

Good

Bad

112

Sensor Area
NetworkcIn-homehealthcare station

Intelligent
pharmaceutical package

Wearable biomedical
devicesFig. 8. The application scenario of the In-Home Healthcare Station

B. The iMedBox prototyping system376

display with touch screen, a dual-core 1GHz ARM Cortex-A9
CPU, and connections through USB OTG, Wi-Fi and 3G. The
operation system is Android 3.1. As shown in Fig.9, we
extended the hardware through a USB adaptor to support NFC
and WSN connections which are not standard peripherals oftablet PC so far. A functional iMedBox is assembled byembedding the tablet and extension modules into a
hand-molding box. As a part of the demonstration, intelligent
medicine packages are made by attaching inlay RFID tags ontoordinary medicine packages.
The application software for the demonstration is
implemented in Java as standard Android apps. The graphic
engine is the AChartEngine, and database engine is the SQLite.
A dedicated data processing engine for data packet parsing, asecurity engine for authentication and cryptograph, and a web
server for 3G/WiFi connection are implemented based on thebasic Android 2.1 API which is supported by major variants ofAndroid. Two types of GUI are designed so far: a Sensor View
for sensor data and a Medication View for prescription.
C. Users’ feedback and improvement directions
Some field trials have been carried out in nursing centers andelderly houses in Blekinge, Sweden. The system concepts havebeen confirmed by the positive feedback. The medication
reminding and recording functions can significantly improve themedication compliance especially for elderly. Seamlessintegration to the hospital’s prescription system is necessary to
reduce the workload of manual input. The proposed
authentication scheme sounds complicated but necessary toreassure the users.
Some insufficiencies are also pointed out. The user interfaces
are still too complicated for elderlies although they are
acceptable for nurses. The texts are not clear enough. This canbe improved by replacing the Android’s default colors and fonts
which is quite “fashion” but lack of clarity. The network
connection and authentication are too slow (currently around 10
seconds). We will further measure the latency step-by-step tofind out the bottleneck and improve.
VIII. CONCLUSION AND FUTURE WORK

Fig. 9. Implementation of iMedBox prototype

A prototyping system of the iMedBox is implemented basedon the Samsung Galaxy Tab10.1 tablet PC. It has a 10.1 inch

To develop successful Health-IoT solutions towards the IoT,
a cooperative ecosystem should be established first. Technicalarchitectures should be centered to the ecosystem especiallyregarding the interoperability, security and information system
integration.
In this paper, a cooperative ecosystem of Health-IoT isformulated based on the reconstruction of the traditional
healthcare and mobile internet ecosystems. To support thisecosystem, the new ecosystem-driven strategies are applied in
all aspects of the development of a Health-IoT solution. As
show cases, the design principles or guidelines forstandardization
efforts,
securitymechanisms,
open-platform-based terminals, and data handling schemes arepresented with details. To support fair distribution of benefits
among all stakeholders, value-centric security schemes areproposed, including the public authority-based authentication,
the secure element (SE) based cryptography, and theGiRI(Global
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non-invasive message handover. In order to achieve theeconomy of scale, an open-platform-based IHHS solution is
proposed as a universal platform for device and service
integration and convergence. Several important designprinciples are given including common platform based on 3C
products, certification of the Health Extension, interoperability
and extendibility, convenient and trusted software distribution,
standardized and secured EHR handling, efficient service
composition,andefficientinformation
integration.
Corresponding data handling schemes are designed including
the layered data compression and self-contained data
formatting.
Finally, to verify the concepts and technical feasibilities, wehave developed a prototype system called iMedBox. It is a
specific case for medication management and in-homemonitoring applications. The iMedBox hardware, software andbackbone system are implemented and evaluated by fielddemonstrations. The positive feedbacks have proven thefeasibility of proposed design methods, proposed architecturesand solutions. Based on the results of this paper, economicallyfeasible services are closer to reality.
One important future work is to implement the proposed
security mechanisms by involving more external partners
including the SIM card maker and telecom operator. Then weplan to integrate the iMedBox terminal into some existing
healthcare information systems and carry out more trials.
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Healthcare Solution for Medication Noncompliance and Daily
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“Ecosystem Analysis in the Design of Open Platform-based In-Home
Healthcare Terminals towards the Internet-of-Things”. International
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[17] Zhibo Pang, Qiang Chen; Lirong Zheng, Elena Dubrova. “An In-homeMedication Management Solution Based on Intelligent Packaging andUbiquitous Sensing”. International Conference on Advanced
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[18] Zhibo Pang, “Technologies and Architectures of the Internet-of-Things(IoT) for Health and Well-being”, PhD Thesis, Royal Institure ofTechnology (KTH), Stockholm, Sweden, 2013.

Zhibo Pang received B.Eng. degree in Electronic
Engineering from Zhejiang University, Hangzhou,
China, in 2002, MBA in Innovation and Growth from
University of Turku, Turku, Finland, in 2012, and PhD
in Electronic and Computer Systems from the RoyalInstitute of Technology (KTH), Stockholm, Sweden, in
2013.
He is a research scientist at ABB Corporate Research,
Västerås, Sweden. Before joined ABB, he worked astechnical manager in semiconductor industry, designing baseband andapplication processors and turn-key solutions for mobile smart devices. He has
15 patents and over 30 peer-reviewed papers in international journals andconferences. He was awarded the National Great Invention Award by the
Ministry of Information Industry of China in 2005, won the First Place Prize of
the RFID Nordic EXPO in 2008 and Outstanding Paper Awards in
ICACT2013. His current research interests include the Internet-of-Things,
wireless sensor network, industrial communication, real time embedded
system, enterprise information systems, automation networks, and multicore
system-on-chip and network-on-chip. He also works on thebusiness-technology joint research such as business model design, value chain
formulation, strategy, and entrepreneurship & intrapreneurship.

[1]

Junzhe Tian received B.Eng. degree in
Communication Engineering from Beijing University ofPosts and Telecommunications (BUPT), Beijing, China,
in 2010, M.Sc. degree in System-on-Chip Design fromRoyal Institute of Technology (KTH), Stockholm,
Sweden, in 2013. After graduate, he became a Softwaredeveloper at Excosoft AB Stockholm, Sweden.
Currently he is working on design and implementationof Graphical Editing Framework (GEF) and Document
Type Definition (DTD) in publishing industry.

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Concept-Based Readability Measurement andAdjustment for Web Services Descriptions
Pananya Sripairojthikoon, Twittie Senivongse
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
pananya.s@student.chula.ac.th, twittie.s@chula.ac.th

Abstract—Web Services is a technology for building distributed
software applications that are built upon a set of information andcommunication standards. Among those standards is the Web
Services Description Language (WSDL) which is an XML-based
language for describing service descriptions. Service providers
will publish WSDL documents of their Web services so that service
consumers can learn about service capability and how to interface
with the services. Since WSDL documents are the primary sourceof service information, readability of WSDL documents is ofconcern to service providers, i.e., service descriptions should beunderstood with ease by service consumers. Providing highly
readable service descriptions can then be used as a strategy toattract service consumers. However, given highly readable
information in the WSDL documents, competitors are able to learn
know-how and can copy the design to offer competing services.
Security attacks such as information espionage, client
impersonation, command injection, and denial of service are also
possible since attackers can learn about exchanged data andinvocation patterns from WSDL documents. While readability ofservice descriptions makes Web services discoverable, it
contributes to service vulnerability too. Service designers
therefore should consider this trade-off when designing service
descriptions. Currently there is no readability measurement for
WSDL documents. We propose an approach to such measurement
so that service designers can determine if readability is too low or
too high with regard to service discoverability, service imitation,and service attack issues, and then can consider increasing or
lowering service description readability accordingly. Ourreadability measurement is based on the concepts or terms inservice domain knowledge. Given a WSDL document as a service
description, readability is defined in terms of the use of difficult
words in the description and the use of words that are key concepts
in the service domain. As an example, we measure readability of
the WSDL document of E-commerce Web services, andexperiment on redesigning of WSDL terms to adjust readability.
Keyword—Concept Hierarchy, Ontology, Readability, Web
Services

Manuscript received August 28, 2013.
P. Sripairojthikoon is with the Department of Computer Engineering,
Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
(e-mail: pananya.s@student.chula.ac.th).
T. Senivongse is with the Department of Computer Engineering, Faculty ofEngineering, Chulalongkorn University, Bangkok 10330, Thailand.
(Corresponding author phone: +66 2 2186996; fax: +66 2 2186955; email:twittie.s@chula.ac.th).

I. INTRODUCTIONW

EB services is a technology for building distributed
software applications. The building blocks are services
which are software units that are built upon a set of informationand communication standards. Among those standards is the
Web Services Description Language (WSDL) which is an
XML-based language for describing service descriptions. A
WSDL document is defined by a service provider and used byservice consumers in discovering service capability andestablishing interaction between consumer-side applications
and the Web service. The structure of a WSDL document(version 1.1) which describes what the service is capable of andwhat data are exchanged comprises the XML elements <types>,<message>, <portType>, and <documentation> [1].
Since WSDL documents are the primary source of service
information, readability [2] of WSDL documents or Web
services descriptions is of concern to service providers, i.e.,
service descriptions should be understood with ease by service
consumers. Meaningful names should be given to the service
interface, operations, input and output messages, and data. In
addition, sufficient documentation should be provided
regarding functional scope and limitation of use. Providing
well-defined readable service descriptions can be used as a
strategy by service providing organizations to attract service
consumers.
Despite being desirable, readability of service descriptions
has its downside. Other organizations can gather informationfrom a service WSDL document to learn know-how and thencopy the design to offer competing services. Also, published
WSDL documents can provide security attackers withinformation like schemas of exchanged data, invocation
patterns, and service location. Attackers may be able to guessother private operations. This leads to more serious attacks suchas information espionage, client impersonation, command
injection, and denial of service.
While readability of service descriptions makes Web services
discoverable, it contributes to service vulnerability too. Service
designers therefore should consider this trade-off when
designing service descriptions. Currently there is no readability
measurement for WSDL documents. We therefore aim topropose an approach to such measurement so that service
designers can determine if readability is too low or too high with

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regard to service discoverability, service imitation, and service
attack issues, and then can consider increasing or lowering
service description readability accordingly.
We apply the concept-based readability measurement model
proposed by Yan et al. [3] to the context of Web service
descriptions. As the name implies, our readability measurement
is based on the concepts or terms in service domain knowledge.
Given a WSDL document as a service description, readability isdefined in terms of the use of difficult words in the description
and the use of words that are key concepts in the service domain.
For example, if the service description contains simple words orclosely-related terms within the domain, it should be easy tounderstand the functionality of the service from the service
description. Here, service domain knowledge is described as
ontology [4] that defines vocabulary of concepts and properties
as well as their relationships, using an XML-based OWL
language [5]. Readability assessment can be conducted by thequality assurance team or service designers who haveknowledge of the service domain, and can be of several uses.
The assessors can compare readability of their WSDL
documents with that of the competing services and may evaluate
if readability should be improved to attract more consumers. Ina certain case, the assessors may consider adjusting the service
descriptions if security issues are of concern. We also outline amethod to increase and lower readability of service
descriptions, and experiment on redesigning of WSDL terms ofE-commerce Web services to adjust readability.
The rest of this paper is organized as follows. Section II
discusses related work and Section III presents theconcept-based document readability model. We propose amethodology to assess readability of WSDL documents andhow to adjust it in Section IV. In Section V, we present anexperiment on readability measurement for three Web services
in the E-commerce domain for comparison, followed by anexperiment to adjust readability of their WSDLs in Section VI.
Section VII concludes the paper with a discussion of theapproach and future outlook.

II. RELATED WORK
By definition, readability means “the level of ease ordifficulty with which text material can be understood by aparticular reader who is reading that text for a specific purpose”[2]. Readability is dependent upon many characteristics of boththe text and the readers, and its concept has been applied tomany kinds of text material including books, technicaldocuments, online documents, and Web pages. Many formulas
for measuring text readability are available and most of them
deal with only text features. That is, texts that use difficult words
are more difficult to understand than those with simple words,
and texts with long sentences and complex syntax are difficult toread.
Yan et al. [3] propose a different but interesting approach tomeasuring text readability in the context of online documents.
They argue that not only domain experts but also average users

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are searching more and more for domain-specific informationfrom online documents, particularly in the medical area, andthese documents are of different readability level. However,traditional readability formulas are designed for general
purpose texts and insufficient to deal with technical materials ina specific domain. Therefore, for the document ranking
purpose, their model takes advantages of a traditionalreadability formula and domain knowledge to measurereadability of domain-specific documents at the word level, i.e.,
it focuses on how the domain-specific terms in a document
affect readability of the document. For example, if the document
contains closely-related terms in the domain vocabulary, it
should be more easily readable and comprehensible to readersof that domain. In their approach, the domain knowledge isrepresented as a concept hierarchy or ontology.
Zhao and Kan [6] argue that the ontology-based approachsuch as [3] has a limitation in that it requires expert knowledge
which is still expensive and not readily available in mostdomains. They hence present an iterative computation on a
resource-concept graph based on the intuition that readability ofthe domain-specific resources (or documents) and difficulty of
domain-specific concepts provide accurate estimations of eachother. The algorithm constructs a graph that represents what
concepts are contained in a particular resource and what
resources contain a particular concept. Then readability
computation for the resources is based on a simple mutually
recursive observation as follows: (1) a domain-specific resource
A is less readable than another domain-specific resource B if A
contains more difficult domain-specific concepts than B, and (2)a domain-specific concept A is more difficult than anotherdomain-specific concept B if A is mentioned in less readable
domain-specific resources than B. This approach is effective
and less domain-dependent but it requires multiple resources inorder to determine their readability, i.e., readability of any
single resource cannot be determined on its own.
The research by Jatowt et al. [7] attempts to measurereadability of Web pages based on link structure. It is motivatedby their study on the correlation between readability of thesource pages and that of the linked pages, i.e., there is a trendthat Web pages would link to other pages with generally thesame level of difficulty. They are also inspired by the TrustRank
algorithm in which scores are propagated from good pages in an
attempt to separate useful Web pages from spam. Therefore,
their algorithm utilizes the Web link structure by propagating
the readability scores of the source pages to the linked pages.
This approach is useful since it becomes possible to measure thereadability of Web pages that have little texts and tocomplement traditional readability measures which rely only on
textual content.
It is seen that several research attempts propose different
ways to determine the readability score of a document but noneof them address measurement of WSDL documents readability.
Since a WSDL document is self-contained and we should be
able to determine its readability using its own content, we will
adopt the concept hierarchy-based model by Yan et al. [3]. This

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model applies at the word level, meaning that it is based on
concepts or terms within a document, rather than sentences orparagraphs; this suits well with the case of a WSDL document.
Using this model, we then rely on the availability of the service
domain ontology on the Internet. In the case that no domain
ontology is available, we argue that an approach such as [8],which builds domain ontology using terms from the WordNet
database [9], can be taken.

III. CONCEPT-BASED DOCUMENT READABILITY MODEL
The concept-based readability model considers difficulty ofterms in a WSDL document and two features of the WSDL
document, namely document scope and document cohesion,
with respect to the presence of domain terms in the WSDL
document. As mentioned earlier, vocabularies of the service
domain is formed as a concept hierarchy. The terms in the
document which have a match in the concept hierarchy are
regarded as domain terms; otherwise they are non-domain
terms. The detail of the model components [3] is as follows.
A. Document Scope (DS)Document scope is defined as the coverage of the domain
concepts in the document. The coverage is viewed from twoangles. First, the more the document contains domain terms, theless readable the document tends to be since the document islikely to contain a larger number of specific concepts. Second,the deeper the domain terms appear in the concept hierarchy, themore difficult the document is to read. The document scope DS
of a WSDL document di can be computed by (1):

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where len(ci,cj) = shortest path between ci and cj in theconcept hierarchy,
D = maximum tree depth in the concept hierarchy, andn = total number of domain concepts in the WSDL documentd i.
C. Simplified Dale-Chall’s Readability Index (DaCw)
A well-known traditional readability formula is theDale-Chall’s readability index [10]. This index sees that, thelength of the sentences in a document and the difficulty of words
correlate with the difficulty of reading material. Since theconcept-based readability model measures readability at theword level, sentence-level complexity is not applicable andhence only word difficulty is considered. To determine thedifficulty of words, words in the document are identified aseither familiar or unfamiliar words. That is, they are familiar
words if they can be found in the Dale list of approximately
3,000 familiar words. Otherwise, they are unfamiliar, and hencedifficult, words. The simplified version of the Dale-Chall’s
readability index DaCw of a WSDL document di can becomputed by (5):DaCw( di ) = PDW(5)where PDW = percentage of difficult words in the WSDL
document di (i.e., number of difficult words divided by numberof words and multiplied by 100).
We will use the word-level model above to measurereadability of WSDL documents since contents of WSDL
elements are mainly words, and not sentences.

n−(

DS (di ) = e∑ depth ( ci ))
i =1

(1)

where depth(ci) = depth of domain concept ci in the WSDL
document di, with regard to the concept hierarchy.
B. Document Cohesion (DC)Document cohesion refers to how focused the text is on aparticular topic. It can be computed by the semantic relatedness
between the domain terms in the document which is reflected by
the links (or shortest path) between them with respect to thegiven concept hierarchy. The more cohesive the domain terms
in the document are, the more readable the document is. The
document cohesion DC of a WSDL document di can becomputed by (2)-(4):n∑ Sim(c , c )i

DC ( di ) =j

i , j =1

Number of Associations, where n > 1, i < jlen(ci, cj )Sim(ci, cj ) = −log
2D
Number of Associations =n(n − 1)
2

(2)

(3)

(4)

D. Concept-Based Readability
The concept-based readability model which determines theoverall readability score of a WSDL document di can becomputed by (6):CRS (di ) = DS ( di ) + DC (di ) + DaCw(di ) −1

(6)

IV. CONCEPT-BASED READABILITY ASSESSMENTMETHODOLOGY
The concept-based readability of a WSDL document can bemeasured by an assessor who is a member of the quality
assurance team or a service designer. The assessor must haveknowledge of the service domain in order to choose (orconstruct) the concept hierarchy or domain ontology
appropriately, and at the end evaluate the readability score. The
assessment methodology comprises the following steps.
A. Service Information Preparation
To prepare for assessment, the assessor first does thefollowing.
Select WSDL Document
The assessor selects a Web service and acquires its WSDL
document. Note that if the assessor wants to compare readability

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of two Web services, both services will be assessed individually
but they must be in the same domain and share the same concept
hierarchy.
Obtain Concept Hierarchy of Service Domain
The concept hierarchy can be a domain ontology defined bydomain experts. We consult either search engines or Web sitesthat publish domain-specific ontologies, e.g., [11]-[13], todiscover relevant ontology for the Web service. In the case thatmultiple ontologies of the service domain are found, a tool like
Protégé [14] can be used by the assessor to merge them into asingle integrated ontology.
If relevant domain ontology is not available for the Web
service, WordNet can be used to help generate the concept
hierarchy [8]. The key terms in the WSDL document will be
extracted and interwoven with their hypernym terms fromWordNet to build up the concept hierarchy. Nevertheless,obtaining the concept hierarchy through ontology libraries ispreferred to obtaining it through concept hierarchy generation.
This is because concepts in domain ontology are defined bydomain experts and can include specific terms of the domain
which better reflect domain knowledge, whereas concepts takenfrom WordNet are likely to be more generic terms. Only when
the Web services are built for specific domains and appropriate
domain ontology is not readily available should the concept
hierarchy be generated from WordNet.
B. Readability Measurement
Generally the assessor measures readability of the whole
WSDL document, but in some case it might be useful tomeasure readability of particular WSDL elements for a moredetailed analysis. The assessor can first select to measurereadability of either the whole document or certain elements,
and then apply the concept-based readability model (6). In thecase that the measurement targets a particular WSDL element,
the content of the element corresponds to a textual document of
the model.
To support WSDL readability measurement, we develop atool called WSDL Readability Calculator which is shown in Fig.
1. The tool is developed by using Eclipse Java EE IDE [15].
Protégé Java library [14] is used to read the domain ontology
file. The tool requires the following input from the assessor:• Web service description URL or a WSDL file;• Service domain ontology URL or an OWL file;• Calculation method, i.e., whole document or specific
WSDL element.
Once the assessor inputs all service details and starts thecalculation, the tool will perform the seven steps below:
Step 1: Extract terms from WSDL elements. WSDL syntax(such as tag names, types, cardinality) is excluded; only theircontents will be considered. Duplicate terms are also removed.
The following is an example of an input message element of a
WSDL operation and a term extracted from this element.
• Input
message:<wsdl:input
message=“services:CreateInvoiceRequest”
name=“CreateInvoiceRequest” />Fig. 1. WSDL readability calculator.
• Extracted term: CreateInvoiceRequest
Step 2: Extract key individual terms from the list of extracted
terms in step 1. The terms will be changed to their singular form.
Duplicate terms and single character will be removed. For
example, the term “CartItems” will be extracted into two
individual terms: “Cart” and “Item”.
Step 3: Map key individual terms to the concepts in theconcept hierarchy. To measure readability score of a WSDL
document, only the WSDL terms that are present in the concept
hierarchy will be used for measuring the document scope anddocument cohesion. On the other hand, all terms in a WSDL
document will be used for measuring the simplified
Dale-Chall’s readability index.
Step 4: Calculate the document scope using (1). To calculate
the document scope, the depth of each domain concept found in
the WSDL document is determined with regard to the concept
hierarchy.
Step 5: Calculate the document cohesion using (2)-(4). To
calculate the document cohesion, the shortest path between eachpair of domain concepts that are found in the WSDL document
is determined, with respect to the concept hierarchy.
Step 6: Calculate the simplified Dale-Chall’s readability
index using (5). To calculate the simplified Dale-Chall’s
readability index, the percentage of difficult words not found in
the Dale list of familiar words is determined.
Step 7: Calculate the concept-based readability score using(6). The readability score of a WSDL document is calculatedusing the concept-based readability model.

C. Evaluation
Once the readability score is obtained, the assessor evaluates
if readability of the whole WSDL document (or a particular
WSDL element) is appropriate. The assessor may be concerned
about attracting the consumers and at the same time being
vulnerable to attackers and competitors. The assessor can use
the readability score for comparison purpose, e.g., comparing
with the scores of competitors’ services. The scores can be
adjusted if the assessor sees fit.

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D. Readability Adjustment
The concept-based readability score is dependent on thedesign and naming of terms within the WSDL document and thequality of the concept hierarchy. That is, the assessor shouldchoose service domain ontology from a reliable source.
Measuring readability of specific WSDL elements can help
the assessor to pinpoint which parts of the WSDL document
should be redesigned and how to adjust their contents in order toincrease or lower the score appropriately. According to (6), it isapparent that we can adjust the readability score by redesigningor renaming of terms within the WSDL document since changeof terms can affect the document scope, document cohesion, andsimplified Dale-Chall’s readability index in the following ways.
• Document Scope: For a WSDL document, adding terms
or changing to deeper terms in the concept hierarchy will
decrease its readability score.
• Document Cohesion: For a WSDL document, adding
terms or changing to terms that are more cohesive, i.e.,
more closely associated, with respect to the concept
hierarchy will decrease the shortest path and hence
increase its readability score.
• Simplified Dale-Chall’s readability index: For a WSDL
document, adding or changing to terms in the Dale list offamiliar words will increase its readability score.
Therefore, to increase the readability score to improve
service discoverability, the assessor may, where appropriate,• Change domain terms in the WSDL document tonon-domain terms;• Change domain terms in the WSDL document to thosethat are shallower in the concept hierarchy;• Change or add domain terms so that the WSDL document
contains domain terms that are more cohesive withrespect to the concept hierarchy;• Change or add terms so that the WSDL document
contains more of Dale’s familiar words.
On the other hand, to lower the readability score to make theservice description less comprehensible to competitors andattackers, the assessor may, where appropriate,• Change non-domain terms in the WSDL document todomain terms;• Change domain terms in the WSDL document to thosethat are deeper in the concept hierarchy;• Change or add domain terms so that the WSDL document
contains domain terms that are less cohesive with respect
to the concept hierarchy;• Change or add terms so that the WSDL document
contains less of Dale’s familiar words.
After redesigning or renaming of terms within the WSDL
document, the assessor will repeat the measurement process to
obtain the adjusted score. The adjustment and measurement canbe repeated as necessary until the assessor is satisfied with thescore.

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V. EXPERIMENT ON READABILITY MEASUREMENTAs an experiment, this section describes the application of themethodology in Section IV to three Web services in the
E-commerce domain, i.e., Amazon [16], PayPal [17], and eBay[18]. The WSDLs of these services are processed by the WSDL
readability calculator to determine readability of the whole
WSDL documents. Note that, to measure readability of a
specific type of WSDL elements, the methodology can beapplied in a similar manner.
For demonstration purpose, we show step-by-step applicationof the methodology to measure readability of the whole WSDL
of Amazon Web service.
A. Service Information Preparation
Amazon Web service delivers a set of E-commerce services,
i.e., payment management, stock management, and package andshipping management. Its WSDL can be found at [16]. The
GoodRelations ontology [19] is selected as the concept
hierarchy for the E-commerce domain. Part of it is depicted inFig. 2. A number of concepts such as account, contact
information, payment method, etc. are listed in this ontology.
B. Readability Measurement
The process to measure readability of Amazon’s WSDL is asfollows.
Step 1: Extract Terms from WSDL Elements
Element names are extracted from WSDL syntax. Duplicate
terms are also removed. In total, there are 373 extracted terms.
Table I shows some of the terms that are extracted from a
WSDL snippet in Fig. 3.
Step 2: Extract Key Individual Terms
The extracted terms are further extracted to obtain key
individual terms and are changed to a singular form. Again,
duplicate terms and any single character are removed. For
example, the term “SimilarProducts” will be extracted into two
individual words “Similar” and “Product”. In total, there are
275 individual terms, and Table II shows some of them.

Fig. 2. Part of GoodRelations ontology.
TABLE IEXAMPLE OF EXTRACTED TERMS FROM AMAZON’S WSDL
PurchaseURL
SimilarProducts
MobileCartURL
TopSellers
SubTotal
TradeInValue
CartItems
SimilarViewedProducts
SavedForLaterItems
LowestNewPrice

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TABLE V
EXAMPLE OF SHORTEST PATHS BETWEEN PAIRS OF DOMAIN TERMS IN
AMAZON’S WSDL
cj
len(ci, cj)
ci
Item
Product
6
Item
Value
6
Item
Price
5
Product
Value
5
Product
Price
4

Fig. 3. Snippet of Amazon’s WSDL.
TABLE II
EXAMPLE OF INDIVIDUAL TERMS FROM AMAZON’S WSDL
Purchase
Url
Mobile
Cart
Sub
Total
New
Price
Saved
For
Later
ViewedSimilar
Product
Top
Seller
Item
Trade
In
Value

Step 3: Map Key Individual Terms to Concepts in Concept
Hierarchy
Individual terms will be mapped to the concepts in the
GoodRelations concept hierarchy. In total, there are 21
individual terms that can find a match in the concept hierarchy.
Table III shows some of these domain terms.
Step 4: Calculate Document Scope
The depth of each domain term found in step 3 is determinedwith regard to the concept hierarchy. Table IV shows the depthof some domain terms. Given (1), the document scope of
Amazon’s WSDL document is 5.1091E-12.

Step 6: Calculate Simplified Dale-Chall’s Readability Index
There are 86 out of 275 individual terms which are found in
the Dale list of familiar words. Table VI shows some of them.
Therefore there are 189 terms that are considered difficult.
Given (5), the simplified Dale-Chall’s readability index of
Amazon’s WSDL document is 68.7273.
Step 7: Calculate Concept-Based Readability Score
Given (6), the concept-based readability score of Amazon’s
WSDL document is 0.2452.
C. Readability Comparison
Similarly to Amazon’s WSDL, the WSDLs of PayPal andeBay can be processed to determine readability based on
GoodRelations E-commerce ontology. Table VII shows themeasurements of all three services in comparison. PayPal’s
Web service provides the most readable WSDL document
whereas readability of Amazon’s WSDL is the lowest. Readable
and comprehensible service description can be one factor in the
popularity of PayPal Web service, while WSDL readability ofeBay, and Amazon follows PayPal’s very closely, indicatingthat they can be competing Web services providing similarly
readable service descriptions. On the other hand, Amazon’s
WSDL document is likely to expose less information tocompetitors and attackers.

VI. EXPERIMENT ON READABILITY ADJUSTMENT

Step 5: Calculate Document Cohesion
The shortest path between each pair of 21 domain terms isdetermined with respect to the concept hierarchy of depth 5.
Table V shows the shortest path between some of them. Given(2)-(4), the document cohesion of Amazon’s WSDL document
is 0.2307.

After the readability score is obtained, the assessor evaluates
if readability of the whole WSDL document (or a particular
WSDL element) is appropriate. If not, the assessor determines
whether readability score of WSDL document should be
increased or lowered compared to the current score.

TABLE III
EXAMPLE OF DOMAIN TERMS FOUND IN AMAZON’S WSDL
Item
Product
Value
Price

TABLE VI
EXAMPLE OF FAMILIAR WORDS FOUND IN AMAZON’S WSDL
For
Cart
Top
New
Value
Price

TABLE IV
EXAMPLE OF DEPTH OF DOMAIN TERMS IN AMAZON’S WSDL
Concept ci
Depth
Item
3
Product
2
Value
1
Price
1

TABLE VIIREADABILITY MEASUREMENTS
AmazonPayPalDocument Scope
5.1091E-12 1.0262E-10
Document Cohesion
0.23070.2989Simplified Dale-Chall’s
68.727359.659Readability Index
Concept-Based Readability
0.24520.3156Score

eBay
5.7495E-19
0.287579.54820.3001

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Adjustment of WSDL readability score can be done
according to the criteria that have been described in Section
IV.D. With the adjustment process, readability score calculated
from the document scope, document cohesion, and simplified
Dale-Chall’s readability index will be affected.
A. Increase Readability Score of WSDL Document
To provide an example of how to increase the readability
score of a WSDL document, we redesign terms in the WSDL
document of Amazon’s Web service.

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TABLE IXCHANGE TO SHALLOWER DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original
Original
Redesigned
New
DomainDepth
Domain
Depth
Term
Term
ItemSearch
Item3
Product
1
Model
Model
2
Type
1
AWSECommerce
Service2
Product
1
Service

Change Domain Terms in WSDL to Non-domain Terms
Domain terms found in WSDL elements may be redesigned
to non-domain terms as shown in Table VIII. After redesigningterms, the readability score is recalculated and new relevant
scores are obtained in Fig. 4. The new readability score isincreased.
Change Domain Terms in WSDL to Those That Are Shallower
in Concept Hierarchy
Domain terms found in WSDL elements may be redesigned
to those that are shallower in the concept hierarchy as shown in
Table IX. After redesigning terms, the readability score isrecalculated and new relevant scores are obtained in Fig. 5. The
new readability score is increased.
TABLE VIIICHANGE OF DOMAIN TERMS TO NON-DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original Domain Redesigned
Term
Term
AWSECommerceService
ServiceWork
SimilarProducts
Product
Goods
Model
Model
Form
NonNegativeIntegerWithUnits
IntegerNumberBrowseNodes
Node
ObjectFig. 5. Adjustment result – Change domain terms to shallower domain terms.

Change or Add Domain Terms So That WSDL Contains
Domain Terms That Are More Cohesive with Respect toConcept Hierarchy
Domain terms found in WSDL elements may be redesigned
so that they are more closely associated with each other withrespect to the concept hierarchy, i.e., the shortest paths between
them are shorter. The redesigned terms are shown in Table X
and new associations in Table XI. After redesigning terms, thereadability score is recalculated and new relevant scores are
obtained in Fig. 6. The new readability score is increased.
Change or Add Terms So That WSDL Contains More of Dale’s
Familiar Words
Domain terms found in WSDL elements may be redesigned
to terms that are in the list of Dale’s familiar words as shown in
Table XII. After redesigning terms, the readability score isrecalculated and new relevant scores are obtained in Fig. 7. The
new readability score is increased.
Summary
In Fig. 8, we summarize the new readability scores obtained
from the adjustment approaches to increase readability. It is
likely that change to shallower and more cohesive domain terms
has a strong positive impact on readability.

Fig. 4. Adjustment result – Change domain terms to non-domain terms.

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TABLE XCHANGE TO MORE COHESIVE DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original Domain Redesigned
Term
Domain Term
ItemSearch
Item
Product
Model
Model
Type
AWSECommerceService
Service
Product

TABLE XISHORTER SHORTEST PATHS BETWEEN PAIRS OF DOMAIN TERMS
Original Pair
Original
Redesigned Pair
New Length
LengthItem-Sale
7
Product-Sale
6
Item-Value
6
Product-Value
5
Item-Unit
6
Product-Value
6
Model-Sale
6
Type-Sale
4
Model-Value
5
Type-Value
3

Fig. 7. Adjustment result – Change to terms among Dale’s familiar words.

Fig. 8. Comparison of adjustment approaches to increase readability score.

Fig. 6. Adjustment result – Change domain terms to more cohesive domain
terms.
TABLE XIICHANGE TO TERMS AMONG DALE’S FAMILIAR WORDS IN AMAZON’S WSDL
WSDL Element
Original Term
Redesigned Term
ItemDimensions
Item
Whole
BinParameter
Parameter
Writing
RequestRequest
Call
Brand
Brand
Name
BrowseNode
BrowseSearchB. Lower Readability Score of WSDL Document
To provide an example of how to lower the readability scoreof a WSDL document, again we redesign terms in the WSDL
document of Amazon’s Web service.

Change Non-domain Terms in WSDL to Domain Terms
Non-domain terms found in WSDL elements may beredesigned to domain terms as shown in Table XIII. Afterredesigning terms, the readability score is recalculated and new
relevant scores are obtained in Fig. 9. Note that, in thisexperiment, the change of terms can decrease the document
scope but at the same time it increases the document cohesion
and simplified Dale-Chall’s readability index, resulting inhigher overall readability.
TABLE XIIICHANGE OF NON-DOMAIN TERMS TO DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original Non-domain Redesigned Domain
Term
Term
Detail
DetailSpecification
Shipping
Shipping
DeliveryFeatureFeatureFunction
PackageDimensions
Package
ParcelIsCategoryRoot
Category
EntityCopyright
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terms always affect all three components of the readability
model. In this example as seen previously in Fig. 9, the added
domain terms that help decrease the document scope also havepositive effects on both document cohesion and simplified
Dale-Chall’s readability index. This hence results in an increase
in readability. It is seen that selecting the right terms to remove
from or add to the WSDL document may not be easy and asingle adjustment approach may not achieve the desired effect.
TABLE XIV
CHANGE TO DEEPER DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original
Original
Redesigned
New
DomainDepth
Domain Term Depth
Term
Brand
Brand
1
Entity2
BrowseNode
Node1
Location
2
IssuesPerYear
Per
1
Individual
3
Offers
Offer
1
Provisioning
2

Fig. 9. Adjustment result – Change non-domain terms to domain terms.

Change Domain Terms in WSDL to Those That Are Deeper inConcept Hierarchy
Domain terms found in WSDL elements may be redesigned
to those that are deeper in the concept hierarchy as shown in
Table XIV. After redesigning terms, the readability score isrecalculated and new relevant scores are obtained in Fig. 10.
The new readability score is lower.
Change or Add Domain Terms So That WSDL Contains
Domain Terms That Are Less Cohesive with Respect to Concept
Hierarchy
Domain terms found in WSDL elements may be redesigned
so that they are more loosely associated with each other withrespect to the concept hierarchy, i.e., the shortest paths between
them are longer. The redesigned terms are shown in Table XV
and new associations in Table XVI. After redesigning terms, thereadability score is recalculated and new relevant scores are
obtained in Fig. 11. The new readability score is lower.
Change or Add Terms So That WSDL Contains Less of Dale’s
Familiar Words
Domain terms found in WSDL elements may be redesigned
to terms that are not in the list of Dale’s familiar words as shownin Table XVII. After redesigning terms, the readability score isrecalculated and new relevant scores are obtained in Fig. 12.
The new readability score is lower.
Summary
In Fig. 13, we summarize the new readability scores obtained
from the adjustment approaches to lower readability. Comparedto the original readability score, the readability scores obtained
from three approaches decrease as expected. However, thereadability score of the approach in which more domain terms
are added increases. This is possible because change of domain

Fig. 10. Adjustment result – Change domain terms to deeper domain terms.
TABLE XVCHANGE TO LESS COHESIVE DOMAIN TERMS IN AMAZON’S WSDL
WSDL Element
Original Domain Term Redesigned Domain
Term
Brand
Brand
EntityBrowseNode
Node
LocationIssuesPerYear
Per
Individual
Offers
Offer
Provisioning

TABLE XVILONGER SHORTEST PATHS BETWEEN PAIRS OF DOMAIN TERMS
Original Pair
Original
Redesigned Pair
New
LengthLengthBrand-Offer
3
Entity-Provisioning
4
Node-Value
3
Location-Value
5
Per-HTTP
2
Individual-HTTP
6
Offer-Hour
3
Provisioning-Hour
4
Offer-Warranty
3
Provisioning-Warranty
4

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Fig. 13. Comparison of adjustment approaches to lower readability score.

VII. DISCUSSION AND CONCLUSION

Fig. 11. Adjustment result – Change domain terms to less cohesive domain
terms.
TABLE XVIICHANGE TO TERMS NOT AMONG DALE’S FAMILIAR WORDS IN AMAZON’SWSDLWSDL Element
Original Term
Redesigned Term
Name
Name
Brand
SearchBinSet
SearchBrowseNarrowBy
NarrowSpecify
CartGetGet
AcquireLengthLengthDurationFig. 12. Adjustment result – Change to terms not among Dale’s familiar words.

This paper presents an application of a concept-based
document readability model to measure readability of Web
services descriptions. The approach is motivated by a designprinciple to expose readable service information and bypotential risks from service imitation and attacks which may
follow such information exposure. The main components of themodel are the scope and cohesion of a WSDL document withregard to the vocabulary of its service domain, as well as thedifficulty of terms that are used in the document.
Readability can be one factor to service popularity andservice imitation and attacks, but it does not say that a highly
readable service description will make the service more popularamong service consumers, or become a target of competitors
and attackers. A WSDL document with high readability score
may not attract service consumers if other kinds of service
quality are not well-maintained. Likewise, the service provider
may also take additional measures to handle vulnerability fromexposure of highly readable information.
It is important to note that the readability score of a WSDL
document can change if the service domain ontology and the listof familiar words are changed. Several ontologies of different
quality may be published on the Internet for a particular service
domain, and thus the assessor should pick the domain ontologythat is most complete and from a reliable source. We also seethat the use of the familiar word list is a useful intuitive
approach. Despite the fact that the Dale’s list contains familiar
words that are known in reading by at least 80 percent of thechildren in Grade 4, the terms familiar and unfamiliar
describing the words here are used in a statistical sense. The
percentage of words outside this list is a very good index of the
difficulty of reading materials, and has so far been used todetermine readability in various contexts including that oftechnical materials. Therefore we still use the Dale’s list in themodel even though we see the possibility that an extended list,
constructed for readers at higher grade levels, should reflect
better the readability scores of technical WSDL documents
whose readers are programmers.
Readability adjustment requires that the assessor or service
designer give careful consideration to the context of WSDL
elements before redesigning them with new terms. It may not be

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easy to select new terms that are appropriate and give theexpected change to the readability level.
Our future work is to experiment with a number of service
providing organizations in Thailand to see if the approach canhelp them with the design of service descriptions and how they
use the adjustment method to adjust readability of their WSDL
documents. The result from this experiment can be used todetermine the appropriate WSDL design which gives the clientsa satisfactory readability level. In addition, the readability score
and readability adjustment method should be validated. To doso, a number of service clients will be asked to answer aquestionnaire to validate if the score truly reflects WSDL
readability and the adjustment method can truly increase or
lower readability.
REFERENCES[1]

World Wide Web Consortium (W3C). (2001, Mar. 15). Web Services
Description
Language(WSDL)
1.1[Online].
Available:
http://www.w3.org/TR/wsdl[2] J. J. Pikulski. (2013, Aug.). Readability. Houghton Mifflin Harcourt.
[Online]. Available: http://www.eduplace.com/state/author/pikulski.pdf[3] X. Yan, D. Song, and X. Li, “Concept-based document readability indomain specific information retrieval,” in Proc. 15th ACM Int. Conf.
Information and Knowledge Management (CIKM 2006), 2006, pp.
540-549.
[4] T. Gruber. (2013, Aug.). Ontology [Online]. Available:
http://tomgruber.org/writing/ontology-definition-2007.htm[5] World Wide Web Consortium (W3C). (2004, Feb. 10). OWL Web
Ontology
Language
Overview[Online].
Available:
http://www.w3.org/TR/owl-features/[6] J. Zhao and M. Y. Kan, “Domain-specific iterative readability
computation,” in Proc. 10th Annual Joint Conf. Digital Libraries (JDCL2010), 2010, pp. 205-214.
[7] A. Jatowt, K. Akamatsu, N. Pattanasri, and K. Tanaka, “Towards morereadable web: measuring readability of web pages based on link
structure,” SIGWEB Newsletter, Winter 2012, pp. 1-7.
[8] Y. J. An, J. Geller, Y. T. Wu, and S. A. Chun, “Automatic generation of
ontology from the deep web,” in Proc. 18th Int. Workshop Database andExpert Systems Applications (DEXA 2007), 2007, pp. 470-474.
[9] Princeton University. (2013, Aug.). WordNet A Lexical Database forEnglish [Online]. Available: http://wordnet.princeton.edu/[10] J. S. Chall and E. Dale, Readability Revisited: The New Dale-Chall
Readability Formula. Brookline Books/Lumen Editions, 1995.
[11] Stanford Center for Biomedical Informatics Research. (2013, Aug.).
Protégé
Ontology
Library[Online].
Available:
http://protegewiki.stanford.edu/wiki/Protege_Ontology_Library[12] DARPA. (2013, Aug.). DAML Ontology Library [Online]. Available:
http://www.daml.org/ontologies/[13] Open Knowledge Foundation (OKFN). (2013, Aug.). Linked OpenVocabularies(LOV)[Online].
Available:
http://lov.okfn.org/dataset/lov/index.html[14] Stanford Center for Biomedical Informatics Research. (2013, Aug.). The
Protégé Editor and Knowledge Acquisition System [Online]. Available:
http://protege.stanford.edu[15] Eclipse Web Tools Platform Project. (2013, Aug.). Eclipse Java EE IDE
forWeb
Developers[Online].
Available:
http://www.eclipse.org/webtools[16] Amazon Web Services LLC. (2013, Aug.). eCommerce Web Service[Online].
Available:
http://webservices.amazon.com/AWSECommerceService/AWSEComm
erceService.wsdl[17] PayPal. (2013, Aug.). Invoicing Service API [Online]. Available:
https://svcs.paypal.com/Invoice/CreateAndSendInvoice?wsdl[18] eBay Inc. (2013, Aug.). eBay Shopping API [Online]. Available:
http://developer.ebay.com/webservices/latest/ShoppingService.wsdl

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[19] E-Business and Web Science Research Group. (2011, Oct. 1).
GoodRelations Language Reference Version 1.0 [Online], Available:
http://www.heppnetz.de/ontologies/goodrelations/v1.html

Pananya Sripairojthikoon has been working as asoftware engineer for Accenture Thailand since June
2012. She received B.Eng. in Computer Engineeringfrom King Mongkut University of Technology
Thonburi, Thailand in 2010, and M.Sc. in Software
Engineering from Chulalongkorn University, Thailand
in 2013. Her research interest includes service
computing and application of semantic technology.
This research was conducted while she pursued a
Master degree.
Twittie Senivongse is an associate professor at the
Department of Computer Engineering, Faculty ofEngineering, Chulalongkorn University, Thailand. She
received B.Sc. in Statistics from Chulalongkorn
University in 1989, M.Sc. in Computing Science from
Imperial College, UK in 1992, and Ph.D. in Computer
Science from University of Kent, UK in 1997. Her
research interest includes service computing andapplication of semantic technology.

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389

An Empirical Assessment of Quasi-Permanently
Vacant Channels in Mobile Communication Bands
for Cognitive Radio
Neeta Nathani*, Gopal Chand Manna**, S.B.Mule**Research Scholar, Department of Electronics Engineering, G.H. Raisoni College of Engineering, Nagpur, India.
**Senior General Manager, Indian Telecommunication Service, BSNL, Jabalpur, India.
neeta_nathani@yahoo.com, gcmanna@gmail.com,mulesb1@gmail.com

Abstract- Basis of cognitive radio is to exploit unused
frequency channels in licensed band. Recently standardized
IEEE 802.22 set of cognitive radio protocols envisages fixedand nomadic receivers at below 800 MHz bands. Radio link
design for this cognitive radio consider that the channels areavailable only dynamically to secondary users. Scanning
period is thus embedded in link layer control as overhead andreduces overall efficiency of cognitive radio technology. For
mobile receivers, availability of permanent channels for radio
link control is essential for in-band signaling. Existing mobile
communication system uses CDMA 800, GSM 900, GSM 1800
and WCDMA 2000 MHz bands for which approximately 1/8 thof the band capacity is used for in-band signaling. Present
work provides an assessment of vacant channels in mobile
communications range which were permanently available atthe time of measurement. The study used conventional Radio
frequency scanners available for different bands anddedicated engineering handsets for tracking active
frequencies. The tests were performed with assemblies carriedin a vehicle and across the length and breadth of each city. The
allotment of frequencies by Frequency Regulatory Authority
to local mobile operators along with the frequency bands
reserved for further distribution were also considered. The
experimentally collected data were analyzed using RF analysis
software and spread sheet database. An analysis of thecollected data lead to arrive at the conclusion that more than
1/8th part of resources of each band are nearly permanently
vacant which is enough to design in-band common control
signalling methods for cognitive radio. Also, with the help ofcollected data of eight cities, an empirical relationship has
been established which can be taken as a thumb rule forprojection of channel occupancy from densely populated coreareas of big cities to less populated township areas whenpopulation is known.
Keywords: Cognitive Radio, Mobile communication, Inband signalling, Radio link control, common control
signalling.

Manuscript received October 5, 2013.
Neeta Nathani is with the Department of Electronics Engineering
as a Research Scholar in G.H.Raisoni College of Engineering, Nagpur,
Maharashtra,
India
(Tel:
91-9584006543;
email:
neeta_nathani@yahoo.com).
Dr.G.C. Manna is with the Department of Telecommunications asa Senior General Manager in Inspection Circle, BSNL, Jabalpur, Madhya
Pradesh, India (Tel: 91-9425803500; fax: 91-761-2678592; email:gcmanna@gmail.com)
S.B.Mule is with the Department of Electronics Engineering, as a
Research Scholar in G.H.Raisoni College of Engineering, Nagpur,
Maharashtra, India (Tel: 91-9822427815; email: mulesb1@gmail.com)

I.

INTRODUCTION

T

HE continued success of mobile wirelessindustry is placing more demands on an already scarce andvaluable radio frequency resources banking oncommercially successful technologies. Regulators of every
country put an eye to oncoming recommendations arisingout of standardization institutes and mark corresponding
blocks as reserved for future licensing. This has been added
up with continued demand for higher bandwidth due to data
and video. As a result, one most promising technology hascome up for efficient spectrum utilization of the unused
channels in the licensed band termed as Cognitive Radio(CR). Cognitive Radio users are termed as secondary users
whereas licensed users are termed as primary users. Since
secondary user uses the same band as that of a primary user,
the former has to take care that the later one is not (orminimum) disturbed in terms of access and interference.
CDMA in 800 MHz band, GSM at 900MHz and 1800 MHz
bands, WCDMA in 2000MHz band, LTE in 2300 MHz
band and WiMAX at 2500MHz and 3500MHz bands are
dominant players in mobile communication radio. Wi-Fi
and other low power radios play at ISM band at 2400MHz
band. To give space to a demanding frequency channel of aband to a primary user, secondary user has to keep track of
all the vacant channels of all bands and their respective
qualities in terms of SNR and throughput. Thus, cognitive
radio users deploying opportunistic spectrum access has toscan continuously the whole range of spectrum with areasonable bandwidth and scanning frequency. This is avery cumbersome and computing intensive mechanism.
Alternatively, a scanner which scans only predefinedchannels of specified bandwidths and across licensed
mobile radio frequency bands only, shall be very efficientand trustworthy. Matched filtering and coherent detection
technique is most suitable in such environment for it’s short
detection time, high SNR, low probability of missed
detection and false alarm [1]. Matched filtering closely
resemble to SDR compatible Mobile Station (MS) available
commercially and also in digital RF scanner used for drive
test purpose. Also, most of the communication systemneeds a pilot and dedicated control channels which remains
as fixed during the entire period of communication between
two users. This needs association of a permanent frequency
channel with cognitive radio, preferably in-band hired

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channel along-with primary users or select a channel out ofband. In opportunistic spectrum access, secondary users
will use licensed spectrum when primary users do nottransmit. According to Beibei Wang and K J Ray Liu,
availability of such a global control channel is not realistic
since there may be no permanent channel available forsecondary users [2]. Probability to get an opportunistic
control channel in licensed band for all nodes in cognitive
radio network has been narrated as dramatically small by
Antonio De Domenico and co-authors [3]. Thus,
exploration for availability of Quasi-permanent channels inlicensed mobile communication bands for cognitive radio
shall truly facilitate with the advantages of working with
common control channel in MAC layer. In the present
study, the licenses allocated to mobile service providers ofIndia has been taken into consideration for estimation ofgrossly available vacant channels of reserve bands anddrive tests were conducted in eight different cities forcompletely allocated GSM bands. It was established that
more than one-eighth part of all bands are vacant whichclosely resembles to the requirement of one signaling
channel for seven traffic channels.
The remainder of the paper is organized as follows:
Section II explains the signaling channel part requirement
as a fraction of channel capacity for mobile communication
technology systems which enable us to draw empiricalstatement for access of the whole licensed band bycognitive radio. Section III covers literature survey
indicating the availability of channels in different
commercial bands. Section IV discusses about the reserve
spectrum and actual allotment of channels by the spectrum
allocation authority. Section V describes the measurement
plan and collection of data at different cities. This isfollowed by presentation and discussion of the collected
results in Section VI. Finally, the conclusions are presented
in Section VII.
II.

EMPIRICAL STATEMENT

Regulatory Authorities of every country identify
frequency bands for public mobile communication
technologies e.g. CDMA, GSM, WCDMA, WiMAX etc.
Each band is divided into radio frequency (RF) channels forlicense purpose. Based on technology deployed, operators
divide each RF channel into communication channels which
consists of common control (CC) channels and data (traffic)
channels. Common Control channels services traffic
channels and are within licensed RF i.e. in-band. In 800
MHz band, CDMA IS-95 uses 1.25MHz bandwidth ascommunication channel with one pilot and seven common
control channels i.e. total 8 channels out of total 64 Walshcode channels. Walsh code count was 128 for CDMA 2000
1x i.e. one RF. In 1x EV- DO there are 8 control channel
time slots out of total 256 time slots and 96 chips of pilot isembedded in each data slot of 1024 chips which together
counts for 1/8th part of the channel. In WCDMA, out of
total radiated power of 20Watts, primary and secondary
control channels and pilot channels constitutes 2.585 Watts
leaving 17.415 Watts for traffic. In GSM, if there is one RF,
the first time slot works as the pilot and common control

390

channel for remaining 7 time slots. In case of 2 or moreRFs, on an average, one time slot of first RF is used ascontrol channel for each additional RF. In OFDMA, for a 5
MHz channel, there are 60 pilot sub-carriers for 360 datasub-carrier and 92 null sub-carriers and thus the ratio isslightly higher than 1/8th part of the whole RF channel.
A: Necessary Condition : Designers of mobile network
communication distributes the communication accesssystem equipments based on population density at different
places. The population density has been identified as denseurban, urban, sub-urban, highway and rural. Conventional 3
sectored antenna system is used for all cell sites except
highway sites where only 2 sectored antenna is deployed.
We take into consideration urban areas where occupancy of
deployed channels are considerably higher. (i) It shall befair enough to estimate that a minimum of 30% of thechannels are occupied in the sub-urban areas even if
operators uses them inefficiently. (ii) Occupancy grows
linearly with population at lower population areas and theefficiency of channel utilization also increases. (iii) With
increase of population occupancy of channels increases,
operators use different optimization techniques for
maximum use of available channels but slowly attains
saturation. The increased need of channels with population
increment and counter-balancing the need through efficient
optimization techniques can be mathematically expressed as
negative requirement of channels and graphically it can be
expressed as saturation. With further increase of population,
operators has to borrow traffic channels from higher
frequency band lowering the occupancy at lower frequency
band and consequently it may be reflected as occupancy
depression in lower band. Thus, the occupancy of channels
with increase of population can empirically be expressed by
the relation: y= 30+15x-x2 where; y=percentage
occupancy of channels in lower frequency band and x=
population size in millions.
B. Sufficient Condition : Approximately 7/8th part of alicensed RF channel of a frequency band is available for
useful communication and the remaining part is used forcontrol purpose. As a corollary of the above, we can saythat 7/8th part of a band is used for purposeful data
communication and remaining 1/8th part for common
control and signaling purpose. Alternatively, if 1/8 th part ofa frequency band is permanently available as spare and thesame is used for common control purpose, the total band
can be completely accessed for cognitive radio
communication purpose.
III.

LITERATURE SURVEY

An extensive measurement campaign was conductedin the city of Aachen located in Germany near to the border
to Netherlands and Belgium. The spectrum occupancy ofseveral frequency bands was measured over longer time
periods up to seven days. The Authors deployed theamplitude probability distribution method to decide
spectrum occupancy data which can help to optimize thesearch for unused spectrum i.e. white space[4]. The Authors
identified FM radio bands around 100MHz and the GSM

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channels bands around 900MHz as the measured minimum
level was above -80 dBm. Marc McHenry did large scale
spectrum occupancy measurement and observed that the
spectrum occupancy in several American cities was found tobe always below 25%, which is mostly caused by a rather
high decision threshold between -90 and -105 dBm [5]. In
Qatar, A comprehensive wideband spectrum occupancy
measurement was performed over multiple dimension (time,
frequency and space) concurrently over anutilized
bandwidth in the 700 − 3000MHz band over a three day
measurement period. Across four different locations, thebandwidth utilization varies between 4% and 15%. It wasobserved that bandwidth utilization is time variant with peaktimes ranging from afternoon to sunset in almost all of thelocations [6]. In Romania, corresponding to the downlink
communication direction i.e. for 880 - 960 GSM, E-GSM,
Military bands, occupancy level was found by Authors to bewhich covered the frequency range from 75MHz to 3GHz in
an outdoor urban environment. The measured frequency
range was divided into six consecutive 500 MHz blocks.
Each block was measured during a continuous period of 48
hours and was observed that only 22.57% of the whole
frequency range between 75MHz and 3GHz was occupied.
46.80% and for 1710 - 1880 GSM 1800, occupancy was
22.86% [7]. A survey was conducted in Barcelona, Spain[8] to carry out a broadband spectrum measurement
campaign A similar type of survey was conducted in
Singapore [9] to find the spectrum usage pattern infrequencies from 80 MHz to 5.85 GHz. The 24-hourmeasurement had been taken at the roof top of Institute for
Infocomm Research’s building over 12 weekday periods,

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391

which indicated that the average occupancy on thefrequency range from 80 MHz to 5.85 GHz was only about
4.54%. In a survey at the Loring Commerce Centre [10], themeasurements were made during a normal work week for 3
days for the frequency ranges between 100 MHz to 3 GHz.
Based on the results of the study, it was observed that the
average spectrum usage during the measurement period was
1.7%.
The static measurement shows that the mobile
licensed bands and ISM bands are partly occupied and theremaining parts of the spectrum resembles noise. It isapparent that no study has been made to assess actual
occupancy of mobile communication bands based on
measurements at different locations of the same city inlicensed band. Also, no study has been made to findvariation of channel occupancy for different type of cities.
These studies shall help us to accurately identify thefrequency channels with low or no active utilization andsuccessfully deploy them in cognitive radio technology.
IV. RESERVE SPECTRUM AND ALLOCATIONIn India, the radio frequencies are being used for
different types of services like mobile communication,broadcasting, radio navigation, satellite communication,
defense communication, etc. The National Frequency
Allocation Plan (NFAP) forms the basis for development
and manufacturing of wireless equipment and spectrum
utilization in the country. Frequency bands allocated tovarious types of mobile communication services in India
are mentioned in Table I.

TABLE I
LICENSED BANDS OF DIFFERENT WIRELESS TECHNOLOGIESTechnology
usedBand and Channel WidthCDMA

890-915MHz; 935-960MHz; Channel B/W
1.25MHz FDD; 20 channels
824-849MHz; 869-894MHz;
124 channels of 200 kHz spacing

GSM
900MHz
GSM
1800 MHz
WCDMA*Allocation*Occupancy/ Remarks

50% vacant

25+25

5 Operators2 RF each
4 Operators

1710-1785MHz;1805-1880MHz; 374 channels

75+75

4 Operators

1920-1980MHz;2110-2170MHz

60+60

3Operators-5
MHz each
3 Operators- 15
MHz each
One -20MHz

WiMax &
2500–2690MHz
4G
WiMax
3400-3600MHz(fixed)*As per available information.

So far CR technology has been developed keeping into
consideration that all channels in licensed bands are
dynamically accessed and released. Game theory is applied
for capturing a vacant channel inviting competition amongst
CR operators. Further, there is a possibility of occupying avacant band by CR secondary user in the guise of a primaryuser. In addition, a concept of bidding for vacant channels
has also come up for use by secondary users at a comparative
cost. All these factors introduces the concept of business out

Technology
Bandwidth
in MHz
25+25

190
200

Fully Licensed- vacantchannels to find out
experimentally
60% vacant channels;
actual to find
75% vacant channels
77% vacant channels
90% vacant channelsof the unused channels of a licensed user with or without
having a permanent infrastructure for CR. It is thus
envisaged that mainly public authorities providing public
utility services at almost zero cost may be authorized tooperate over unoccupied spectrum even though licensed.
However the number of such public utility service providers
is expected to be very limited. Table I clearly indicates that
one out of total eight communication channel in each band ispermanently available as vacant for using as pilot and

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signalling purpose which is necessary for the working of
Cognitive Radio communication except GSM 900 MHz
band. Conduction of field test is absolutely necessary forassessment of quasi-permanently vacant channels for use of
in-band common control signalling purposes.
V. MEASUREMENT SETUP AND DATACOLLECTION
The measurement setup used is a drive test equipment
which performs tests in a cellular network and collects dataon a moving vehicle. The software and hardware used are:
Laptop with charger and USB hub, GPS and data cable,
Digital RF Scanner, License dongle for TEMS, Cell site
database and Link budget, Clutter diagram from Google
website, MapInfo software, Engineering handsets with 4(2G/3G) SIMs of different operators mounted simultaneously
and cable terminal. In the setup, data collection software is
installed in the laptop which uses a mobile phone along withGPS system. Drive test route was carefully selected and data
was collected accordingly. GPS collects the data of latitude
and longitude of each point while mobile collects data likesignal strength, uplink and downlink frequency, etc. All theinformation is stored with their respective date and time aswell as their geographical locations. File sizes are kept
within limit of about 25MB in each part trace and a new fileis open in each part. Data from all part files are groupedfrom collection software and are stored in some output file.
Using the above setup, measurement was taken on eightcities at different geographical locations viz. Kolkata, Patna,
Ranchi, Dibrugarh, Jaipur, Shillong, Bhopal and Port Blair
which have population ranging from 1.5 million to 6.6
million shown in Table 2.
Figure 1 shows the measurement route map of Jaipur.
Jaipur city has a population of about 6.66 million. One sideof measurement route is heavily populated while it is lesspopulated on the other side. The lower band of frequency
channels is distributed among three operators while the upperband is allocated to one operator. While moving fromlocation having 75.828940E and 26.914420N, it is found thatin lower band of Operator1 -14 BCCH are occupied namely
71,73,75,76,77,78,80,81,82,87,88,111,112,113 while 29
BCCH are vacant (72,74,79,83 to 86,89 to 110) of which
BCCH no. 89 to 110 is not used at all. Among hopping
channels, two hopping channels 114 and 121 are vacant.
While in Operator2, 13 BCCH (1 to 13) and 18 hopping
channels (14 to 31) are fully occupied. In Operator3, 13
BCCH (50 to 62) and 18 hopping channels (32 to 49) arealso fully occupied .The frequency band is switched to upperband used by Operator 4, in which 15 BCCH (530 to 544)
and 15 hopping channels (546 to 560) are occupied while
one BCCH number 545 is vacant. So, on an average 75% of
frequency spectrum of lower band is utilized.

Fig. 1. Measurement Route map of Jaipur

Kolkata is a densely populated city which has a populationof about 4.48 million in core Municipal area. During thesurvey, the area selected was busiest one which has tallbuildings towards right side at North and scattered single
storied structure towards the river. Towards South, it isalmost green field; measurement route is shown in Figure 2,
it is found that the lower band and upper band of frequency
channels is distributed among three operators. In the two
operators of lower band in Kolkata all the BCCH viz. 70,
71,73 to 76, 78,80,82,87,88,111,112,113 in Operator 1 and
50 to 62 in Operator 2 are fully occupied but 30 BCCH i.e.
72,77,79,81,83 to 86, 89 to 110 are vacant in third operator;and 4 hopping channels i.e., 40,48,49,96 are vacant. In theupper band only one BCCH, i.e., 873 is vacant among thethree operators; while no hopping channels are available. In
Kolkata, it is found that average spectrum occupancy isabout 72.58%.

Fig. 2. Measurement Route map of Kolkata

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Being the least populated place, its spectrum occupancy is
43%.
%% Spectrum Occupancy in various cities
S
p 100
e
90
c
t
80
r
u
70
m

60
O
c
c
u
p
a
n
c
y

Fig. 3. Measurement Route map of Dibrugarh, Assam

Figure 3 shows the measurement route map ofDibrugarh. Dibrugarh district, well known as the “TeaCity of India”, has a population of about 1.5 million. It is
one of the least populated district where the Brahmaputra
river flows in its north. The lower band of frequency
channels is distributed among three operators while theupper band is allocated to two operators. While movingfrom location having 94.997490E and 27.452830N, it isfound that in lower band of Operator1 - 7 BCCH are
occupied namely 1,3,4,5,6,7,8 while 3 BCCH are vacant(2,9,10) and none of the hopping channels are vacant.
While in Operator2, 4 BCCH (23 to 26) and 5 hopping
channels (27 to 31) are fully occupied. In Operator3, 8
BCCH , i,e; 74,76,78,81,87,88,111,113 and 16 hopping
channels namely 63,64,66 to 69, 115 to 120, 122,123,124
are fully occupied .The frequency band is switched toupper band used by Operator4, in which 13 BCCH
(536,537,538,867 to 870, 872 to 875,747) and 10 hopping
channels (876 to 885) are occupied while 18 BCCH are
vacant. In the upper band of Operator2 all 9 BCCH and 3
hopping channels ( 845 to 847) are occupied. So, on anaverage 50% of frequency spectrum of lower band isutilized.
Data was also collected for spectrum occupancy
measurements in GSM at 900MHz bands in an outdoor
environment other cities viz. Bhopal, Patna, Ranchi and
Dibrugarh, Shillong & Port Blair. In Bhopal, the spectrum
occupancy in lower band is 74.19% while in Ranchi it is only
52.42% as it switches to upper band where it has a spectrum
occupancy of 83%. The measurements shows that the
spectrum occupancy in lower band of Patna is 75.8%.
Shillong, capital of the state of Meghalaya of India is locatedat 25.57° N and 91.88° E on a plateau in the eastern part ofthe state. The total population of the city is 1.43 million, hasa spectrum occupancy of 54%. Port Blair is the capital city ofAndaman and Nicobar Islands in India. Located at 11° 40' N
and 92° 46' E, Port Blair is the municipal council in thesouthern part of Andaman, a part of India's Union Territory.

50
40
30
20

10
0

0

2

4

6

8

10

Population in various cities (in millions)Fig. 4. Population Spectrum Graph

VI. RESULTS AND DISCUSSION
Collected data of eight cities has been recorded intable II along with city population figures. The population
data has been collected from internet and limited to city
municipal areas only as declared by local civic authority.
The population was mostly taken on the basis of 2011
sensus. Figure 4 depicts the spectrum occupancy in eightcities of India In figure 4 above, it is projected that eventhe most sub-urban area with scanty population shall havenearly 30% occupancy. For population between 1 million
to 4 million, the increase is nearly linear. The occupancy
saturation is observed in the range of 4 million to 7
million. Also, with projected expansion of densely
populated city core areas to 8 million, occupancy level isestimated go upto 86%, leaving a clear space of 14% ofchannels for use of cognitive radio.
TABLE II POPULATION SPECTRUM CHART

S.No.

City

Population(inmillions)

1

Bhopal

2.36

74.19

2

Patna

1.68

75.8

3

Kolkata

4.48

72.58

4

Ranchi

2.91

52.42

5

Jaipur

6.66

75

6

Dibrugarh

1.5

50

7

Shillong

1.43

54

8

Port Blair

0.9

43

% Spectrum
Occupancy

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The obtained results demonstrated the existence ofsignificant amounts of spectrum potentially available forthe future deployment in CR networks in lower bands of
GSM which are not at all used.
VII. CONCLUSION
Discussion from above reveals that more than 20% of thecompletely licensed bandwidth is practically vacant in asaturated market environment. This is well above therequirement of 1/8th part of the band to get access to thewhole of the bandwidth at a time by cognitive radio andadequate to take additional MAC level overhead required
for Cognitive Radio. There is no immediate necessity ofthese channels by the licensed operators which can besafely deployed as common control channel for cognitive
radio purpose. Further, to doubly enhance protection ofcommon control channel, a disaster recovery common
control channel may be designated which will hold thereplica of allotments and processing status of CR Primary
Common Control Channel. Also, depending upon the
population, it is possible to approximate channel
occupancy and hence cognitive radio technology planners
can draw a long term plan for efficient use of them inpublic benefit. Use of commercially stable technologies
with minimum changes at Operator Transceiver
equipment and user mobile equipment for adaptation to
Cognitive Radio technology shall lead to development ofa economically deployable system.

VIII. REFERENCES[1] Ying Chang Liang, Kwang Cheng Chen, Geoffrey Le Li and Petri
Mahonen, “Coginitive Radio Networking andCommunications: An
Overview”, in Proc. IEEE Transactions in on Vehicular Technology, 2011.
[2] Beibei Wang and K J Ray Liu, “Advances in Cognitive Radio
Networks: A Survey”, IEEE, February 2011, 1932-4553.
[3] Antonio De Domenico, Emilo Calvanese Strinati and Marie Gabriella
Di Benedetto, “A survey on MAC strategies for Cognitive Radio
Networks”, IEEE 2012, 1553-877X/12.

394
394

[8] Miguel Lopez-Benitez, Anna Umbert and Fernando Casadevall,“Evaluation of Spectrum Occupancy in Spain for Cognitive Radio
Applications”, IEEE, 2009, vol.no. 978-1-4244-2517-4/09, IEEE Xplore.
[9] Md Habibul Islam, Choo Leng Koh, Ser Wah Oh, Xianming Qing,
Yoke Yong Lai, Cavin Wang, Ting-Chang Liang, Bee Eng Toh, Francois
Chin, Geok Leng Tan and William Toh, “Spectrum Survey in Singapore:Occupancy Measurements and Analysis”, IEEE Xplore.
[10] Tugba Erpek, Mark Lofquist and Ken Patton, “Spectrum Occupancy
Measurements Loring Commerce Center Limestone Maine”, September
2007, www. sharedspectrum.com.

Neeta Nathani was born in India in 1981. She
received the B.E. and M.Tech. degrees from
Rajiv Gandhi Proudyogiki
Vishwavidyalaya,
India, in 2003,and 2009, respectively. She joined
Sri Ram Institute of Technology, Jabalpur, India,
in 2003 and worked there for few years. Since
2008, she has been with Gyan Ganga Institute ofTechnology & Sciences, Jabalpur, India where
she is currently an Assistant Professor. Her mainareas of research interest are WirelessCommunication, Quality of service of Cognitive
Radio.
Dr. Gopal Chandra is working as Senior General
Manager (Head Quarters), Inspection Circle, BSNL,
a wholly owned Company under Department of
Telecommunications (DoT), Govt. of India. He hasdeveloped and conducted one week course on
Quality of Service Monitoring at Information andCommunication Technologies Authority, Mauritius
as International Expert through Commonwealth
Telecom Organisation London during August 2010.
He has also deliverd a speech on WiMAX coverage
Evaluation at International Conference on Advanced Communications
Technology 2011 at Seoul, Korea and chaired a session on Network
Management. He had also delivered speech on ADSL at International
Telecommunication Union seminar in 2000 at Bangalore, India. He has
carried out extensive research on coverage issues of GSM,CDMA, WCDMA
and WiMAX radio access. Study of Wireless Traffic and QoS estimation of
Cognitive Radio are his current areas of research. In addition, he has written
several articles on advanced telecommunications which has been published innational and international journals and symposiums.

Prof. Mule has completed his master degree inElectronics (Computer) from Government College ofEngineering, Pune and has six years of industrial
experience..

[4] Matthias Wellens, Jin Wu and Petri Mahonen, Department of Wireless
Networks, RWTH Aachen University Kackertstrasse, Germany, “Evaluationof Spectrum Occupancy in Indoor and Outdoor Scenario in the Context of
Cognitive Radio”.
[5] M. A. McHenry, P. A. Tenhula, D. McCloskey, D. A. Roberson, and C. S.
Hood, “Chicago Spectrum Occupancy Measurements & Analysis and a Longterm Studies Proposal,” in Proc. of Workshop on Technology and Policy for
Accessing Spectrum (TAPAS), Boston, USA, August 2006.
[6] Khalid A. Qaraqe, Hasari Celebi, Ali Gorcin†, Amer El-Saigh‡, Huseyin
Arslan†, Mohamed-slim Alouini , “Empirical Results for Wideband
Multidimensional Spectrum Usage”, supported by Qatar Telecom (Qtel)[7] Alexandru Marţian, Calin Vladeanu, Ioana Marcu, Ion
Marghescu,“Evaluation of Spectrum Occupancy in an Urban Environment in a Cognitive
Radio Context”, International Journal on Advances in Telecommunications,vol 3 no 3 & 4, year 2010, http://www.iariajournals.org/telecommunications.

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395

Filtering Performance of Reducing
the Sampling Rate of Sound Card:
Perspectives on Different Signal-to-Noise Ratios
Bing-Yuh Lu*,**, Li-Man Tseng***, Ling-Yuan Hsu**, Shr-Hong Tang**,
Yu-Lin Weng**, Man-Na Hsueh***, Mei-Ju Su****, Huey-Dong Wu*****, Jin-Shin Lai*******
Department of Electronic Engineering, Tungnan University, Taiwan, ROC
Department of Information Management, Catholic St. Mary’s Junior College of Medicine, Nursing, and Management,
Taiwan, ROC***
Department of Nursing, Catholic St. Mary’s Junior College of Medicine, Nursing, and Management, Taiwan, ROC****
Department of Biomedical Engineering, Yuanpei University, Taiwan, ROC*****
Section of Respiration Therapy, Department of Integrated Diagnostics and Therapeutics, National Taiwan University
Hospital, Taiwan, ROC******
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital and College of Medicine,National Taiwan University, Taiwan, ROC**

Benjaminee777@hotmail.com, liman@smc.edu.tw, lyhsu@smc.edu.tw, shrhongtang@smc.edu.tw, ylweng@smc.edu.tw, helena@smc.edu.tw,merri1024@gmail.com, hdwuntuh@ntu.edu.tw, jslai@ntu.edu.tw
Abstract— This study presented a theoretical analysis to provethat there exists an optimal sampling frequency to reduce the noisein the signal to reduce the processes of acoustic digital signal
processing. The respiration sounds have been recorded by 2 to 44.1
KHz or higher sampling rates, however, the optimal frequency
might be decided by the noise from environment. This study solves
the problem from the point of theoretical view. The findings wereinteresting because the noise in a pure sine wave can be reduced
by lower sampling rates. The sampling frequency of lung soundrecording can be reduced in the range of 2 to 5 KHz. In addition,
the results supported to enhance signal-to-noise ratio in thereceivers of frequency modulation, under-water acoustics, andother communication applications.
Manuscript received Aug 20, 2013.
This work was supported in part by the National Science Council, Taiwan
under Grant 101-2221-E562-004, 101-2627-E-002-006, and 102-2627-E002-005.
B. Y. Lu is with the Tungnan University, Taipei, and St. Mary’s JuniorCollege of Medicine, Nursing and Management, Yilan, Taiwan. (e-mail:benjamin@smc.edu.tw)
L. M. Tseng is with St. Mary’s Junior College of Medicine, Nursing andManagement, Yilan, Taiwan. (e-mail: liman@smc.edu.tw)
L.Y. Hsu is with St. Mary’s Junior College of Medicine, Nursing andManagement, Yilan, Taiwan. (e-mail: helena@smc.edu.tw)
S.H. Tang with St. Mary’s Junior College of Medicine, Nursing andManagement, Yilan, Taiwan. (phone: +886-922-998305; fax: +886 2
39897398; e-mail: shrhongtang@smc.edu.tw)
Y. L. Weng is with St. Mary’s Junior College of Medicine, Nursing andManagement, Yilan, Taiwan. (e-mail: ylweng@smc.edu.tw)
M. N. Hsueh is with St. Mary’s Junior College of Medicine, Nursing andManagement, Yilan, Taiwan. (e-mail: helena@smc.edu.tw)
M. J. Su is with Yuanpei University,HsinChu, Taiwan (Email:merri1024@gmail.com)
H. D. Wu with Section of Respiration Therapy, Department of Integrated
Diagnostics and Therapeutics, National Taiwan University Hospital,
Taiwan (e-mail: hdwuntuh@ntu.edu.tw)
J. S. Lai with Health Science and Wellness Center, National Taiwan
University, Taiwan (e-mail: jslai@ntu.edu.tw)

Keyword— noise reduction, sampling frequency, acoustic signal,lung sound, sound card, sonar,SNR.

I.

INTRODUCTIONT

HE linear interpolation technology for recovery of thesignal is a common method, but the precision of theinterpolation is not clear. In lung sound recording, thesuggestive sampling rates are 44.1, 22.05, 11.025, 5.5125, and2.75625 KHz which are based on the sampling rate of
traditional recorders. The sampling is the basic process of thesignal processing. The modern lung sound and heart soundrecorders, biomedical sensors, digital image systems, storage
devices, digital communication systems get the acquisition of
raw data from the humans by sampling [1,2]. The computing
speeds of the micro-processors are enhanced by thesemiconductor manufacturing technology which allowed thewere 10 MHz 30 years ago, but that of the advanced
communication signal converters are several GHz now [16].
The Computerized Respiratory Sound Analysis (CORSA) [3]
pointed out the survey of respiratory sounds research, and very
high speed internal clock to control the units of the processors.
Therefore, the sampling rate of the electronic devices has been
accelerated in the past decades. For example, the limitation ofsampling rate of analog-to-digital converters the sampling rate
was listed as a parameter of the method of digitalization [4].
The word lengths, and sampling rates were considered to record
the lung sound perfectly by using the sound blaster cards orcommercial multi-channel signal acquisition cards. The
simplest process of the acquired raw data is moving average asa low pass filter to reduce the noise. The algorithm of adaptive
filters acquired the environment noise to cancel the noise in thesignals [5]. Furthermore, in the computation of the spectrogram,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014

the overlapped data decided its resolution [6]. Therefore, thetotal error in a specific duration of lung sound can be adominant factor of precision.
The adjustable sampling rate has been employed to reducethe volume in data storage and communication. Some
literatures presented the adjustable sampling rate can enhance
the signal-to-noise ratio [7,8]. In some cases, the weak signal
with lower sampling rate, and the strong signal with higher
sampling rate are the rules to reduce the data volume, andenhance the SNR. Therefore, the experimental results can be
discussed by the theoretical analysis. The following proof
proposed a direct solution to the optimal sampling rate for
reducing the noise.


me 

k N
k 1

i



In this study, the proof gives a solution to reduce noise bytaking an optimal sampling frequency.
Taking the Fourier transform [9], we have:(2)
Yp  j    X  j   N  j  gP  j Generally, a low-pass filter with the transfer function ofH  j  is always designed as be a pre-processing device tocancel the noise. Therefore, the noise can be optimal reduced
by some specific sampling rate in the condition ofd(3)
Y  j   X  j  P  j  H  j    0dsThat is:d 1 (4)N  j   ks     0


k 
ds  TThen
 jk 
(5)k  N '  j   ks   0
TThe optimal ωs depends on the solution of the differentialof  N '  j   ks   0 . It exists the solution of ω s tok 

reduce the noise.
To verify the proof, we design a computer simulation toshow the statistical view of the reduction of noise. Ideally, therespiration can be simplified as a sine wave whose half cycle
for inspiration, and the other for expiration. The noise was the
additive white Gaussian noise (AWGN) which is the common
noise in the communication system [14]. The parameters ofcomputer simulation were listed in Table 1.
The average absolute errors of the sampling points werecomputed to evaluate the performance of the method of
reducing the sampling rate. The sampling rates were formulized
as:
(6)

(7)

linear interpolation. Consequently, the percentage of error
reduction (  ) can be described as:mei
 100%
me1(8)

III. RESULTS
The ideally pure sine wave and that with the AWGN andrandom noise (RN) of signal to noise ratio (SNR) =40 dB wereshown in Fig. 1 (a) and (b). The sampling rate was 44.1 KHz.
The root-mean-square (RMS) error was computed by different
sampling rates of 44.1(Group 1), 22.05(Group 2),
11.025(Group 3), 5.5125(Group 4), and 2.76125(Group 5)
KHz, respectively. We run 100 times of computer simulations
by the same parameters in Table 1 to compute the mean
absolute errors (MAE). The box plots were presented in Fig. 2.
TABLE I THE PARAMETERS of the SIMULATED RESPIRATION
SIGNAL.
Variables
Value
Shape
Sine wave
Period3 sec.
Signal to noise ratio
20, 30, 40, and 50 dB(SNR) of AWGN
Sampling rate
44.1(Group 1), 22.05(Group 2), 11.025(Group3), 5.5125(Group 4), and 2.76125(Group5)
KHz

(a)
1

Amplitude

where p  t    t  nT nyˆi  x1Nŷi was the reduced noised signal and recovered bywhere % METHODIf the signal x(t) with noise n(t), and the sampling rate is2 , where T is the period. The sampling pulse train iss Tdenoted as p(t). Therefore, the sampled signal is:(1)
y p  t    x  t   n  t  gp  t 

44.1
f si  i 1 KHz
2

The pure signal in group 1 was denoted as x1[n], and thenoised signal was y1[n] whose numbers of points are N. We
reduce the sampling rate by dividing 2i-1 where i>1. The
numbers of samples were also reduced to be N/2 i-1. However,the number of the samples can be recovered by well-known
linear interpolation technology. Therefore,

0.5
0
-0.5
-1

0

0.5

1

1.5
Time (sec.)
(b)

2

2.5

3

0

0.5

1

1.5
Time (sec.)

2

2.5

3

1

Amplitude

II.

396

0.5
0
-0.5
-1

Fig. 1. Computer simulation: (a) the ideally pure sine wave and (b) that with
AWGN.

where i=1 to 5, are also the group indexes, i.e. the sampling
frequency of group 1 was 44.1 KHz, and that of group 2 was
22.05 KHz.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014

SNR

AWGN

397

RN

0.055

0.068

0.05
0.066
0.045
0.064

Error(V)

(a)
20 dB

Error(V)

0.04
0.035

0.062

0.03
0.025

0.06
0.02
0.015

0.058
2

3
Group

4

5

0.018

0.023

0.016

0.0225

0.014

0.022

0.012

0.0215

Error(V)

(b)
30 dB

Error(V)

1

0.01

1

2

3
Group

4

5

1

2

3
Group

4

5

1

2

3
Group

4

5

1

2

3
Group

4

5

0.021
0.0205

0.008

0.02

0.006

0.0195

0.004
1

2

3
Group

4

5

-3

-3

x 10

x 10

5.5

7.4

5
7.2
4.5
7

Error(V)

(c)
40 dB

Error(V)

4
3.5

6.8

3
6.6

2.5
2

6.4

1.5
6.2
1

2

3
Group

4

5

-3

-3

x 10

x 10
1.8

2.35
1.6

2.3
2.25

1.2

Error(V)

(d)
50 dB

Error(V)

1.4

1

2.2
2.15
2.1

0.8
2.05
0.6
2
0.4
1

2

3
Group

4

5

Fig. 2. The box plots of the MAE of Groups 1, 2, 3, 4, and 5 of 100 times. The AWGN level were (a) SNR=20, (b) SNR=30, (c) SNR=40, and (d) SNR=50.
(The sampling rates: 44.1(Group 1), 22.05(Group 2), 11.025(Group 3), 5.5125(Group 4), and 2.76125(Group 5) KHz)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014

RN

100

100

90

90

80

80

70

70

Error(%)

(a)
20 dB

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Fig. 3. The box plots of the percentage of error reduction ( ) of Groups 1, 2, 3, 4, and 5 of 100 times. The AWGN level were (a) SNR=20, (b) SNR=30, (c)SNR=40, and (d) SNR=50. (The sampling rates: 44.1(Group 1), 22.05(Group 2), 11.025(Group 3), 5.5125(Group 4), and 2.76125(Group 5) KHz)B

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014

Figure 2 presented the statistical results. Each box contained
100 trials of MAE. In (a), the SNR of AWGN is 20 dB. The
box plots displayed that Group 5 (sampling rate =2.76125 KHz)
has gotten the minimum MAE errors. Compared with theoriginal signal (Group 1), the operation of reducing sampling
rate cancelled about 1/5 MAE errors from the point of statisticalview. In (b), (c), and (d), the SNRs were 30, 40, and 50 dB. All
results show that the smallest sampling rate (Group 5) gets thesmallest RMS errors. Based on the proof, there exists a optimal
sampling rate to reduce the noise. From the box plots, we findthat the lower sampling rate got the lower MAE errors bystatistical analysis.
The percentage of error reduction by decreasing the sampling
rate was obviously. Figure 3 presented the performance of the
reducing sampling frequency. All plots displayed that group 5
achieved the best effect of noise cancellation for both AWGN
and RN. The  s were in the range of 15 to 25%. Therefore, thefiltering performance of the easy method for pure sine waves
was excellent.
From the observation of Figs. 2, and 3, the lower sampling
rate did reduce both AWGN and RN. The lower frequency
signal can be filtered the noises by the lower sampling rate ofsound card.
IV. DISCUSSION
The pure sine waves usually play as the carrier in thecommunication systems [17]. In the detection systems, thereflections of the transmitting pure sine waves are widely
employed in sonar, RADAR, and other systems. The sampling
rate of receiver can be reduced based on the results of our study,
especially in the real-time computing system. Fourier series
described that a periodic signal is the linear combination of thefundamental signal and its harmonics. Therefore, the simulated
ideally pure sine wave is significant, because of the theory of
the linear time invariant (LTI) system [1]. The box plots can be
expanded to explain the geniality of the operation of the
reducing sampling rate. Chang and Lai [11] have studied on theperformance evaluation and enhancement of lung soundrecognition system in two real noisy environments. The resultspresented that the frequencies of noises were higher than thatthe main episodes in the spectrogram. Therefore, if thesampling rate of the experiment reduced to a specific frequency
and processed the raw data with moving average, most of theenvironmental noise can be much cancelled. However, theexperienced medical staff usually records the lung in silent
environments whose noises compose with the sounds from air
conditioners, fans, computer systems and so on. Based on thesampling theory, the energy of noise would be reduced by thelower sampling rate that shows the possibility of the optimal
sampling rate can be found in some specific environments.
Furthermore, the processing of the digital data can be
accelerated by decreasing the data. The benefits of selecting abetter sampling rate can achieve the better performance of
signal processing. CORSA indicated that the wheeze wascontained in the domain frequency at 400 Hz, but a number ofinvestigators have suggested that the range is actually between
80–1,600 Hz and 350–950 Hz by filter theory [12,13]. Lu et al[14] have synthesized the normal breath and wheezing sounds
whose main components were under 2 KHz. All the papers
provided that the evidence of the sampling rate can be reduced399

to cancel the noise. However, the crackle includes the higher
frequency components, the optimal sampling rate can be higher
than normal breath and wheezing sounds. This is the limitationof using the lower sampling rate for lung sound recording.
Furthermore, the standardized sampling rates applied in manyindustry standard sound facilities are 44.1, 22.05, 11.025 or
5.5125 KHz as standards. Most modern research groups
selected 44.1 or 22.05 KHz to record the lung sound. The
filtering performances of AWGN and RN were close.
Therefore, the filtering method can be employed in thereceivers of digital systems to cancel the noises caused fromdifferent reasons.
Some electronic stethoscope use the lower frequency ofsampling rate [18]. It supports the examination of this study.
We give the better choice of sampling rates to the researchersof respiration sounds.
V. CONCLUSION
The theoretical analysis of reducing sampling rate filter
was solved in this study. The proof can be employed to simplify
the filter design, if we get the smallest error from the simulated
signal by the optimal sampling frequency. In addition thereasons of noises in physiologic signals are usually bandlimited, therefore, the optimal sampling frequency can be used
to simplify the system design. In the detection systems, thereflections of the transmitting pure sine waves are widely
employed in sonar, RADAR, and other systems. The sampling
rate of receiver can be reduced based on the results of our study,
especially in the real-time computing system.
ACKNOWLEDGEMENTS
The authors thank for the valuable comments from thereviewers and the supports from the project numbers of
NSC101-2221-E-562-004, NSC101-2627-E-002-006, and
NSC102-2627-E-002-005, National Science Council, Taiwan,
Republic of China.
REFERENCES[1] S. Haykin and B. Van Veen, Signals and systems, New York: Wiley, 1998.
[2] J. S. Beasley and G.M. Miller, Modern electronic communication. New
York: Pearson Education, 2005.
[3] A. R. A. Sovijarvi, J. Vanderschoot and J. R. Eavis, Standard ofcomputerized respiratory sound analysis. Eur Respir Rev 2000;10:585.
[4] J. E. Earis and B. M. G. Cheetham, “Current methods used forcomputerized respiratory,” Eur Respir Rev, vol. 10, 2000 pp. 586-590.
[5] B. S. Lin, H. D. Wu, F. C. Chong and S. J. Chen, “Wheeze recognition
based on 2D bilateral filtering of spectrogram,” Biomedical EngineeringApplications, Basis & Communications, vol 18, 2006, pp. 128-137.
[6] A. R. A. Sovijärvi, L. P. Malmberg, G. Charbonneau, J. Vanderschoot, F.
Dalmasso, C. Sacco, M. Rossi and J. E. Earis. “Characteristics of breath sounds
and adventitious respiratory sounds,” Eur Respir Rev, vol. 10, 2000, pp. 591–
596.
[7] J. P. McKay and Y. Rahmat-Samii, “Compact range reflector analysis
using the plane wave spectrum approach with an adjustable sampling rate,”
IEEE Trans. on Ant and Propaga, vol. 39, 1991, pp.746-753.
[8] A. Hart and S. P. Voinigescu, “A 1GHz bandwidth low-pass ΔΣ ADC
with 20GHz to 50GHz adjustable sampling rate,” IEEE Radio Frequency
Integrated Circuits Symposium, vol. 2, 2008, pp. 181–184.
[9] F. G. Stremler, Communication systems, New York: Pearson Education,
2005.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014[10] L. Vannuccini, J. E. Earis, P. Helistö, B. M. G. Cheetham, M. Rossi, A.
R. A. Sovijärvi and J. Vanderschoot, “Capturing and preprocessing of
respiratory sounds,” Eur Respir Rev, vol. 10, 2000, pp. 616–620.
[11] G. C. Chang and Y. F. Lai, “Performance evaluation and enhancement oflung sound recognition system in two real noisy environments,” Computer
Methods and Programs in Biomedicine, vol. 97, 2010, pp. 141-150.
[12] N. Gavriely, Y. Palti, G. Alroy and J. B. Grotberg. “Measurement andtheory of wheezing breath sounds,” J Appl Physiol, vol 57, 1984, pp. 481–492.
[13] H. Pasterkamp, A. Tal, F. Leahy, R. Fenton and V. Chernick. “The effectof anticholinergic treatment on postexertional wheezing in asthma studied byphonopneumography and spirometry,” Am Rev Respir Dis, vol. 132, 1985, pp.
16–21[14] B. Y. Lu, H. D. Wu, S. R. Shih, F. C. Chong, M. L. Hsueh and Y. L. Chen ,“Combination of frequency and amplitude modulated Model for the synthesisof normal and wheezing sounds,” Austra Phy Eng Sci Med, vol.34, 2011, pp.
449-457.
[15] V. M. Rossi, A. R. A. Sovijärvi, P. Piirilä, L. Vannuccini, F. Dalmassoand J. Vanderschoot, “Environmental and subject conditions and breathing
manoeuvres for respiratory sound recording,” Eur Respir Rev, vol. 10, 2000,
pp. 611–615.
[16] PICO 900 Series Scope. Available:
http://www.picotech.com/picoscope9000.html[17] T. Wheel, Electronic communication systems for technicians, New
York: Pearson Education, 2006.
[18] User manual, 3M Littman electronic stethoscope, Model 3200.

Bing-Yuh Benjamin Lu received his BS
degree in electrical engineering from
National Central University and his MS
and PhD degrees in electrical engineeringfrom National Taiwan University in 1988,
1993, and 2000, respectively.
He was an instructor at the Department ofElectronic
Engineering,
Tungnan
University, New Taipei City, Taiwan,
Republic of China in 1992. In 2000, he became an associate
professor in the same department. He joined the Department ofInformation Management, St. Mary’s Medicine, Nursing, andManagement College, Yi-Lan, Taiwan, Republic of China in
2011, and served as an associate professor and the department
head in the duration of 2011 to 2013. He is currently an
associate professor in Department of Electronic Engineering,
Tungnan University, New Taipei City, Taiwan, Republic ofChina. He is interested in computer simulation, medical
engineering, acoustics, lung sound and educational researches.
He is a member of IEEE.
His academic interests in focus on acoustics, medical
engineering, computer simulation, educational applications ofengineering, and pulmonary signal processing especially about
lung sound and pulmonary mechanism.

Liman Tseng is a lecturer in the Nursing
Department, St. Mary’s Junior College ofMedicine, Nursing and Management, YiLan, Taiwan. She has a MS degree innursing, Tzu Chi University in 2005, who
attended a PhD degree in public health, in
Tzu Chi University in 2009. Her researchand teaching interests include nursing

400

administration, surgical nursing, oncology nursing andpediatrics nursing.

Ling-Yuan Hsu was born in I-Lan, Taiwan,
R.O.C., in 1975. He received the M.S.
degree in computer science and informationengineering from National Dong Hwa
University, Hualien, Taiwan, in 2004, andPh.D. degree in computer science andinformation engineering at National
Taiwan University of Science andTechnology, Taipei, Taiwan, in 2013. Currently, he is anassistant professor in the Department of Information
Management of ST. Mary’s Medicine, Nursing, andManagement College. His research interests include artificial
intelligence, image processing, evolutionary computation, andcomputer networks.

Shr-Hong Tang was born in Taiwan in
1978. He received Ph.D. in Engineeringfrom National Chiao Tung University(Hsin-Chu, Taiwan) in 2007. His major
field of study was applied information
technology in engineering. He iscurrently an assistant profession in St.
Mary’sMedicine,
NursingandManagement College, and pursuing an
advanced degree in computer science from National Taipei
University of Education (Taipei, Taiwan)

Yu-Lin Weng received her M.S. degree inInformation Management from National
Taichung University of Science andTechnology and her MS degrees in
Network Learning technology from
National Central University in 2006 and2008, respectively. Currently, she ispursuing a PhD degree in Information
Technology at National Taiwan University of Science andTechnology .She was an instructor at the Department ofInformation Technology, St. Mary's Medicine, Nursing andManagement College, Yi-Lan, Taiwan, Republic of China from2008. She is currently the head of the Registration Section, St.
Mary’s Medicine, Nursing, and Management College. She isinterested in digital learning, adaptive testing, and databasesystems.

Man-Na Hsueh received her MS degree
in nursing from National Taipei
University of Nursing and Health
Sciences in 2004. Over the past 35 years,
she has been teaching at St. Mary’s
Nursing School (before 2005) and St.
Mary’sMedicine,
Nursing,andManagement (after 2005). She was the
Director of the Department of Students’ Affairs from 1991 to

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 3, Issue 1, January 2014

1997, the Director of the Department of Students’ Internship
Affairs from 2004 to 2005, and the Representative President
and Director of the Department of Academic Affairs in 2005.
She is currently the head of Department of Nursing at St. Mary’s
Junior College of Medicine, Nursing, and Management . She isinterested in elder person’s health care and in community
medicine.

Mei-Ju Su received Ph.D. degree atGraduate Institute of Electronics
Engineering, National Taiwan University
since 2004 to 2010, Master degree at
Department of Electrical Engineering,
Syracuse University since 1996 tp 1997.
Now, her researches focus on biosignal
processing, medical device, telehealthcare system, and sensor design. She had been worked at
Industry for 8 years since 1998~2005 on network IC design and
SiS Company, medical instrument system design at Biomedical
Technology and Device Research Laboratories of Industrial
Technology Research Institute. Now she is the associate
professor of department of Biomedical engineering, Yuanpei
University.

Huey-Dong Wu was graduated with aM.D. degree from Medicine Department of
National Taiwan University in 1984. Hereceived resident training in National
Taiwan University Hospital (NTUH) andtrained as visiting scholar in ULCA later.
He service in the Department of Internal
Medicine and Department of Integrated
Diagnostic & Therapeutics in National Taiwan University
Hospital (NTUH) till now. He is the chief of the division of
Respiratory Care, Department of Integrated Diagnostic &Therapeutics in NTUH. His academic interests in focus on
respiratory care, pulmonary pathophysiology and pulmonary

401

signal processing especially about lung sound and pulmonary
mechanism.

Jin-Shin Lai was born in Taipei, Taiwan,
Republic of China (R.O.C.) in 1949. Hereceived the M.D. degree from the Medical
School of National Taiwan University,
Taipei, Taiwan in 1974. He completed
residency training in the Department of
Physical Medicine and Rehabilitation,
National Taiwan University Hospital,
Taiwan in 1978.He was a Lecturer, Associate Professor, andProfessor of Medical School, College of Medicine, National
Taiwan University in 1980, 1984 and 1996, respectively.
Now, he is the Director of Health Science and Wellness Center,
National Taiwan University and Director of Sports Medicine
Center, National Taiwan University Hospital. His professional
interests include medical informatics, sports medicine,
rehabilitation medicine, biomechanics, and rehabilitationengineering. He and his research group had published a seriesof studies about Physiological Effects of Tai Chi Chuan
Training.
Prof. Lai has been the Chairman of School of Rehabilitation
Medicine (1988-1992), Chairman of School of Occupational
Therapy (1992-1998), Chairman of Department of Physical
Medicine & Rehabilitation (1993-1999), Chief of Division of
Medical Informatics (2001-2007), College of Medicine,National Taiwan University, respectively. He also served as the
Chairman of Department of Information Technology & Service,
National Taiwan University Hospital (1998-2004), and elected
as the President of Rehabilitation Medicine Association of
Taiwan (R.O.C.) 1993-1999, the President of Sports Medicine
Association of Taiwan (R.O.C.) 1997-2001, the Chairman of
Scientific Commission of Asian Federation of Sports Medicine(AFSM) 1990-2000, the Treasurer of Asian Federation ofSports Medicine (AFSM) 2001-2004 and the Chair of HL-7
Taiwan (2005-2009).

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ICACT Transactions on Advanced Communications Technology(TACT)Technology (TACT)Vol.
Vol.3,
3,Issue
Issue1,
1,January
January2014
2014

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Techniques for System of Systems Engineeringin Construction of a Smart Tourism Industry
Information System
Liming Bai*
*College of Business Administration, Guangxi University of Finance and Economics,100 Mingxiu West Road,
Xixiangtang District, Nanning, Guangxi Zhuang Autonomous Region 530003, P.R.CHINA
putaoren2000@aliyun.com

Abstract—Currently, as many disciplines begin to cultivate a
set of core methodologies, system of systems engineering (SoSE),
which has its root in context of military programming, becomes asignificant research focus and provides a new perspective to solve
the emerging “system of systems” challenges in industrial
analysis. Meanwhile, modern communication and information
technologies
haveprovidedgreaterpossibilities
forsocio-economic sectors to execute smarter decisions, and thesetechnologies may advance the industrial application of SoSE.
Therefore, targeting the rareness of government-oriented
intelligent decision support system (DSS), and guided by theunderlying system of systems thinking, this paper proposes atechnical framework for designing a policy maker-responsive
smart information system which focuses: (1) system of systems
structural architecting; (2) geographical simulation using
time-series remotely sensed data, GIS instrument and simulation
bodies such as cellular automata (CA) and multi-agent systems(MAS); (3) SoS evolution description through network analysis
and intelligent computing; (4) measurement of SoS effectivenesswith two-tier and four-grade method; and (5) SoSE program forindustrial optimization. Its application in tourism analysis will
provide a smarter base for industrial policy-making, planning andforecasting, and will help reduce risk and cost in industrial
restructuring. For this relatively new field of SoSE application,
tools and methods are not perfect, so it is important to draw
together academia, government, industrial organizations andenterprises to collaborate for further valuable achievement.
Keyword—System of systems engineering (SoSE), geographicalsimulation, tourism, smart industry information system,
intelligent computing

I. INTRODUCTIONI

N the information age, as computer communication, internetof things, distributed control, cloud computing and manyother fields of technology advance, originally separated
Manuscript received December 31, 2012. This work was supported in partby the National Natural Science Foundation of China under Grant 71163004,
the Humanities and Social Sciences Foundation of Guangxi under Grant SK
13LX363 and the Research Foundation of Guangxi University of Finance andEconomics under Grant 2012C17.
L. M. Bai is with the Guangxi University of Finance and Economics,
Nanning, Guangxi Zhuang Autonomous Region 530003, China (phone:139-7868-4395; fax: 077-1384-3160; e-mail: putaoren2000@ aliyun.com).

individual systems (or system elements) become capable ofconnecting to each other by information “ties”, forming ameta-system that involves various heterogeneous distributed
systems (including policies, economies or technologies) [1].
Decision makers within government and industry encounter aprevalent problem of increasing complexity, which are ofsystem-of-systems type [1]. This is particularly evident intourism industry. Tourism plays an important role in national
economies of many countries. Therefore, growth of theindustry results in complex networks consisted of expanding
conventional components and newly generated cross-sector
systems. These growing complexities, which is of system ofsystems type, have challenged the system-engineeringprinciples based industrial analysis in investigating the industry
architecture and evolution, making effective decision-support
become unmanageable [1].
This becomes a problem to be solved in construction ofdecision support system (DSS) as well. There are 3 different
types of tourism-related information systems: (1)tourist-oriented information system (TIS). It provides
information on tourist attractions, itineraries, logging andaccommodations and tour guide, ect. for tourists or potential
tourists. For instance, Ren developed a Tourism Information
System for Wuhan City, China based on MapGIS K9, with theaim, principle, overall framework and function module design.
The study emphasized the feasibility and broad prospect of the
GIS technology in construction of such systems [2]; (2)Tourism enterprise-oriented information systems (TES). It is a
tourism management information system aids managerial ormarketing behaviors of individual corporate. Taking the
Sanzhualun National Forest Park as an example, Wang andChen introduced the use of WebGIS technology in forest park
tourism information system development [3]. Wan and Tangestablished a persistent data frame using .NET platform andCastle technologies, and applied it into developing a travel
management system [4]. (3) Policy/industrial decision
maker-oriented system (Tourism industry information system,
TIIS). It mainly provides support for decision making processof governments, industrial organization. Li and Zhan presented
the realizing schemes and process of the object-oriented data
model in the construction of a Tourism Information

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Technology(TACT)Management System (TIMS) [5]. A set of synthetical function
indexes were incorporated into the system so that it can provide
city tourism administration agents several decision making
services such as tourist attraction analysis, regional tourism
planning, tourism product and tourist itinerary design.
The information provided by the 3 types of information
systems is significantly different. Differs from TIS and TES,TIIS has to concern more on macro environment dynamics andcomplex stakeholder networks, and it has more work to with
big data. Therefore, smart or virtual communication is widelyused in micro-management practices (e.g., corporate or
profession administration), but when it comes tomacro-management on industrial or regional level, due to theneed of both large-scale geographical information and diverse
simulation models, government/industry-oriented intelligent
decision support system is relatively rare.
Therefore, inspired by the embedded “system of systems(SoS) thinking” in the text of military programming, this paper
attempts to architect a policy maker-responsive smart tourism
industry information system and proposes the related modelingtechniques and approaches.
II. SYSTEM OF SYSTEMS AND SYSTEM OF SYSTEMSENGINEERING
A. System of Systems
Development of information and communication
technologies have changed the form and frequency of theinteractions among social systems, therefore, at the beginningof the 21st century, the concept of “system of systems” [6-7]began to become an emerging internationally research focus insystem science[8]. Many research centers, such as the National
Centers for Systems of Systems Engineering (NCSoSE) and the
SoS Engineering Center of Excellence (SoSECE), were
established to study complex systems of systems problem
domains in different fields.
Though without a widely accepted definition, the core ofconcept is commonly recognized [9]. Sage and Cuppan
suggested that a SoS has to meet the Maier’s five criteria [10],and Keating defined SoS as a meta-system consists of multiple
individual systems that diversified in operating context,
geographic location, conceptual architecture and function mode[11]. To some extent, SoS is a task-oriented system alliance,
which possess the resources and capabilities of each component
system and performs more productive than the sum of theindividual systems. Therefore, SoS is often designed to solve
the multi-system interaction and /or integration problems[12],dealing with inherent complexity domains [13].
In recent years, differences between SoS and general systemhave been recognized, and architectural design, resource
confrontation and performance evaluation for SoS havebecome increasingly emphasized in national defense, aerospace
exploration, information or communication networks,transportation, energy, healthcare, environment preservation,
management and many other application fields [12].

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403

B. Socio-Technical Features of Systems of Systems
Differentiated from simple general system, system ofsystems has several significant socio-technical features [8]: (1)large in scale, complex in architecture, and composed ofmultiple component systems; (2) the component systems
demonstrate wide geographic distribution, and they haveoperational, functional or managerial independence; (3) having
unfixed targets under a specified purpose, and the SoS
resources can be dynamically configured to meet diversified
needs of multiple tasks; (4) components systems shareinterdependence, interoperability and can run concurrently in
executing a specified plan; (5) developing under centralizedplanning and administration, and producing behavioral orfunctional emergence in process of evolution; (6) it is critical to
explore and coordinate the capabilities/resources of different
component systems or stakeholders to gain collectively
provided upgraded capability.
C. System of Systems Engineering
Definition, abstraction, modeling and analysis of system ofsystems challenges ascribe to system of systems engineering(SoSE), which is a set of developing processes, instruments,
and approaches providing design or re-construction solutions.
System of systems engineering methodology is initialized by
U.S. Department of Defense, and is developed mostly in
military applications in many countries. But in recent years, its
superiority in dealing with emerging complex socio-economicor technical problems is increasingly recognized, driving thenon-defense applications. SoSE is not simply systems
engineering of monolithic and complex systems, as the solution
design emphasizes uncertainties in the mission requirements
and the component systems, and it contains multi-level andmulti-dimensional consistent engineering programs [14-15].
Rather than systems engineering concentrating on building thesystem right, SoSE focuses on configuration of selected
systems and their resources and interactions to meet therequirements of SoS capability.
There is not a single unified consensus for processes
involved in system of systems engineering. DeLaurentis
suggested a three-phase SoSE framework where a SoS problem
is defined, modeled and analyzed [16]. Zhang et al proposed a
two-level and four-grade measure approach focusing on SoS
performance [12].
III. TECHNOLOGIES FOR SMART SYSTEM ARCHITECTING
In recent years, systems science (especially complexity) andgeo-spatial information science are emphasized in tourism
study. complex networks analysis [17], cellular automata (CA)and multi-agent systems (MAS) [18] is widely used.
However, many studies only emphasized the static industrial
architectures, but ignored the dynamic evolution mechanism
towards the expected goal [19]. Yang analyzed the tourism
industry network and its evolution, and pointed out that theformation and evolution of the networks were driven byinternal heterogeneity and external uncertainty [19]. Xue andWeng analyzed regional tourism spatial layout using
economics deductive model combined with agent-based
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calculation. The microscopic background, influence factors anddynamic process of tourism spatial architecture changes could
be “dynamically” observed through the interaction of a large
number of micro agents (enterprises, consumers etc. )[20].
These research applications provide a meaningfulenlightment for constructing a smart tourism industry
information system. Guided by the underlying system ofsystems engineering thinking, and based on an integration of
information and communication technologies, this paper
proposes a technical framework for designing a smart
information system. The phases include: (1) system of systems
structural architecting; (2) geographical simulation using
remotely sensed data, GIS instrument and intelligent
computational agents; (3) SoS evolution description throughnetwork analysis and intelligent computing; (4) measurementof SoS effectiveness with two-tier and four-grade measuremethod; and (5) SoSE program for industrial optimization.
The technical route is shown in Fig. 1, and the detailed
information will be stated in the subsequent sections.
A. System of Systems Structural Architecting
Tourism industrial SoS is a complex network ofgeographically distributed resources, market entities,
consumers, stakeholders and their intensive communications,which evolutes both spatially and temporally in architecture
and component systems. Emergence may be produced in the
dynamic SoS evolution. Therefore, the first phase forconstructing the smart industry information system is SoS
architecting, namely, properly converting the industrial SoS
into virtual properties in the simulation system. The structural
architecting for tourism SoS is demonstrated in Fig. 2.
The basic elements of the tourism SoS to be simulated
include resource entity, SoS architecture, SoS process andenvironment, and the component system itself is a composite
element. Primary task of the study is to identify the basic units
and the consequently assembled component systems, as well as
structural relationships between the tourism SoS and thecomponent systems. The smart information system consists of
tourist geographical system, landscape system, industrial
layout system and social System.
Fig. 2 only provides a simple conceptual model, and further
improvement should be made to elaborate more elements,
component systems and the embedded hierarchy andinteraction networks. Moreover, an enhanced functionalarchitecting is in need to identify the requirements for enhanced
SoS functions.
B. Geographical Simulation: Cellular Automata with
Geographical Multi-agent
In the second phase, the tourism SoS will be geographicallysimulated based on remotely sensed data, GIS modelingplatform, CA and MAS instruments. Time-series simulation
outputs though knowledge discovery (KDD) and data mining
can provide support for the following analysis missions.

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Theories related to SoS, industrial organizationand informatization① SoS Architecting

Trial and Error② Geographical Simulation
Multi-temporal remotely sensed imageries
Data mining

Image processing

sampling

Kernel Fisher Nonlinear Discriminant Cellular
Automata, Geographical Multi-agent systems
auxiliary data

Time-series simulation model③ SoS Architectural Evolution Analysis
Landscape system
Industrial
layout system

SocialsystemTourist geographicalsystemNetwork analysis
Scale-free
network anddynamic Bayesian
network
Intelligent
computing④ SoS Effectiveness Assessment
Two-level and Four-grade
SoS effectiveness analysis
MOP
MOE

SoS robustness andadaptability assessment
Robustness

MOEE
Dynamic adaptability
MOSE

Uncertainty⑤ SoSE Program for Industrial Optimization
Macro-management policies and plans
Fig. 1. Technical route for constructing the smart tourism industrial
information system. The designation focuses 5 critical subsequent processes:
1) system of systems structural architecting; 2) geographical simulation using
time-series remotely sensed data, GIS instrument and simulation bodies suchas cellular automata (CA) and multi-agent systems (MAS); 3) SoS evolution
description through network analysis and intelligent computing; 4)measurement of SoS effectiveness through two-tier and four-grade measuremethod; and 5) SoSE program for industrial optimization. Each of theseprocesses is capable of outputting relevant industrial information.

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ICACTTransactions
Transactionson
onAdvanced
AdvancedCommunications
CommunicationsTechnology
Technology(TACT)

Industrial Layout System
Landscape System
LUCC
TouristAttraction

Dynamics ofEcology andEnvironment

Industry
Composition

SpatialGrowth Pole
Tourist Flow

Marketmodel can be used for analog of the derivative and evolution ofa newborn or a simple component system in the tourism SoS.

Coopetition
ClusterIndustrial Network
Socioculture

Urban Core-periphery
Structure405
405

Destination
Image
Macro Policy
Social System

Tourist Geographical System
Fig. 2. Conceptual model for the smart tourism industrial information system.

The core of geographical simulation is to establish scientificgeospatial systems model using appropriate researchmethodologies, based on application of complexity theory andcombined with the inherent law of geography. Cellular
automata (CA) and multi-agent system (MAS) are suited to
study the complex geographic phenomenon [17].
CA is a grid dynamics model that is discrete in time, spaceand state, and is local in spatial interaction and temporal causal
relationship. It is an important research tool and theoreticalmethod branch of artificial life. Many studies have shown, thekernel Fisher nonlinear discriminant learning machine issuperior to other methods in automatically extracting the CA
transition rules. However, CA cell can not move, having
limitations in dynamic environment and cell interactions, thus
introduction of MAS modeling proves useful. MAS stems froma complex adaptive system, it is composed of multiple
computing units (agent) mutually interacting. Spatial MAS
resolves the macro space pattern formation though theinteraction among microscopic individual (cell) or agent, it isvery close to the real geographical world, and can reflect thecharacteristics of complex space systems in terms ofemergence, chaos and evolution, thus has more analog
advantage. MAS is a SoSE method, which can ensure thecoherence of SoSE applications in tourism geographicalsimulation.
C. SoS Evolution Description: Network Analysis andIntelligent Computing
System network approaches such as small-world network
analysis, scale-free network analysis, and social influence
network theory (SINT) with dynamic Bayesian network, can be
employed to simulate the growth and evolution of the tourism
industry SoS. They are also effective in describing the complex
interactions between the component systems. In this process,intelligent computing will play a critical role in data mining.
Small-world Network Analysis
A network in the real world usually has both certain rules andsome randomness. To describe the transition from a network ofrules to a random one, Watts and Strogatz introduced aninteresting WS Small-world Network Model in 1998 [21]. The

Scale-free Network Analysis
Unlike the degree distribution for the exponent network, thatof many large-scale complex network is a descending curve,
following a power law distribution P(k ) ~ k −γ . As explanationof this power-law distribution, Barabási and Albert proposed
the Scale-free Network Model [22, 23]. The evolution of BA
scale-free network mainly has two aspects, namely growth andpreferential attachment. New nodes are constantly added into
the network, and it is with high probability for the new nodes toconnect to the existing node with larger number of connections.
The generation algorithm is as follows:--Growth: Initially there are m0 nodes in the network, andone new node is added at each step, the node is connected to
one of the existing m ≤ m0 nodes.
--Preferential attachment: When selecting the node towhich the new node attached in the network, choice is madeaccording to Π probability. If the degree of the node i is ki ,then the probability of the existing node to be selected isdefined as:∏ (k ) =
i

ki
∑kj

(1)

j

In this way, after t steps, a network with N = t + m0 nodes
and mt edges is generated. In such a network, there are a few
nodes having a very high degree (i.e., the number of nodes
connected to it), while the majority of the nodes show relatively
low degree. Tourism SoS is open, elements (nodes) areconstantly added and the attachment features as unequal
probability. Therefore, when the tourism SoS and its
component systems have evolved into the near mature or
mature stage of the life cycle, the application of scale-free
network model will produce more realistic descriptions.

Social Influence Network and Dynamic Bayesian networks
To achieve the desired objective for the smart tourism
industry information system, the SoSE program guiding the
SoS evolution should make clear how to configure the SoS andachieve the best SoS configuration through circulated SoS
measurements. The Social Influence Network Theory (SINT),
combined with Dynamic Bayesian Networks (DBN), can fulfill
this dynamic process. The SINT depends on the individual’s
coordination processing on the conflict viewpoints, stances andattitudes. But the output of the conflict processing results
depends on the structural relationship between the individuals,
such post, influence of interaction and one’s sensibility (that is,
to what extent can an individual be influenced) to it. Its
expression is as follows:yi(t +1) = ai ( wi1 y1(t ) + wi 2 y2(t ) + " + wiN y N(t ) ) + (1 − ai ) yi(1)(2)

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Wherein, t is period of time; y represents the viewpoint,
position or attitude of the individual i ( i = 1, 2," , N , N is thenumber of individuals in the group) in time period t , its initialvalue is yi(1) ; wij is the weight for the influence of individualN

i upon individual j ( 0 ≤ wij ≤ 1 ), and ∑ wij = 1 ; individual
j =1

i ’s sensibility ai =Providing preference
record using empirical
judgment on behavior,
making preferred mode
be calculated first

N

∑w

ij

j =1, j ≠ i

One of the methods to estimate wij is DBN. Bayesian
network is a product of the combination of probability theory
and graph theory, and is composed of network structure andconditional probability distribution. Its specific applicativearchitecture is determined based on knowledge principle andthe structure of variable relationship. For a Bayesian networkwith total number of N nodes, its network architecture
depends on the following set of conditional independence
assumptions:P(vi v1 ," vi −1 , vi +1 ," vN ) = p (vi parent (vi ))(3)

Wherein, i = 1, 2," , N ; parent (vi ) is the parent nodecorresponding to node vi . Thus, it can be inferred from thechain rule of probability that, the joint probability distribution
for the node set V is:
N

N

i =1

i =1

P (V ) = ∏ p (vi v1 ," vi −1 , vi +1 ," vN ) = ∏ p (vi parent (vi ))(4)

DBN provides a strong support for the extraction of thecomplex interactions between systems or elements within the
tourism SoS. Therefore, it can be used in overall analysis of the
SoS, producing in the smart tourism industry information
system a resource allocation program conducive to industrial
optimization.
Intelligent Computing in SoSE
Application of intelligent computing may contribute toimproving the system of systems engineering effectiveness inthe smart industrial system’s operation. As an essential streamof artificial intelligence, intelligent computing is an empiricalcomputer thinking program in data processing after sequential
set of steps of intelligent acquisition and recognition. The
computing model is of hierarchical nature, and it generally
features six levels[24], namely, operational simulation,
existential experience, evaluation, inference of change limit,
intelligent computing experience system and multi-link
simulation and behavior capture (Fig. 3).
Intelligent computing body is capable of self-growth andbeing more adaptive with time and record of changes.
Algorithms for intelligent computing includes neural networks,
machine learning, bio-computing, fuzzy logic, pattern etc.

5. Computing
Experience
System

4-A: forecast of the▪第3阶段variation
range of thetarget;4-B: anticipationassessment

4. Inference of
Change Limit

Processing the mostbasic thinking
operations throughroutine programming

1. Operational
Simulation

406
406

6. Simulation
and Behavior
Capture6-A: predicting
cooperation orinterference behaviors
from multiple targets;
6-B: behavior capture

3. Evaluation
A performance
evaluation systemselecting experience
option(s) as the core ofcalculation reference

2. Existential
Experience
Coding the superior
experience andconvenient process bylogical judgment

Fig. 3. Intelligent computing process in system of systems engineering. Sixlevels are included with respect to: operational simulation, existential
experience, evaluation, inference of change limit, intelligent computing
experience system and multi-link simulation and behavior capture.

Intelligent computing frame and algorithms can be used insystem of systems engineering processes including data
mining, complex network analysis, big data processing andpolicy forecast. The effectiveness and functionality of the smart
tourism information system will be significantly improved
through hybrid embedded intelligent computing algorithms andsystems.
D. Emergence and SoS Effectiveness Measurement: Two-level
and Four-grade Method
Measurement and evaluation of a SoS can be conducted on
aspects including SoS effectiveness, robustness andadaptability. The overall performance is the extent of to which
the SoS have a beneficial role in the mission process andoutcome. Corresponds to hierarchical characteristics of SoS
architecture, a two-level and four-grade measure method was
proposed in [12:253-259]. The method mainly includes
measures of performance (MOP), measures of effectiveness(MOE), measures of emergence effectiveness (MOEE) andmeasures of system of systems effectiveness (MOSE). Amongthe four grades, MOP and MOE are oriented to component
subsystems of a SoS, while MOEE, and MOSE are oriented to
the SoS’ top-level mission operations.

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corresponded to a sub-operation of the mission, that is:
Measures of Performance (MOP)
Performance of system unit is the inherent properties orcharacteristics of a given SoS. Suppose a SOS consisted of nsystem unit, namely
SoS = { s1 , s2 ," , sn }Wherein,

pi

(5)

is

the

subsystem

level

performancecorresponding to any system unit si （ i = 1, 2," , n ）. Suppose
the numer of performance metrics for system unit si was ri , pi
will be presented as:{pi = pi ,1 , pi ,2 ," , pi , ri}

(6)Performance metrics of system unit si would be subject tothe restriction of a specific low-end performance threshold(denoted by pi∗ ) as well as the technological constraints forhigh-end performance.
Measures of Effectiveness (MOE)
Effectiveness aggregation of multiple cooperating andmutually administrative system units constitute input intomeasures of effectiveness for emergence-level behavior
characteristics. MOE for each system unit is likely to containmultiple aspects, i.e. for any system unit si ( i = 1, 2," , n ), itscorresponding system effectiveness will be:{ei = ei ,1 , ei ,2 ," , ei ,li}(7)Wherein, li denotes the dimension of system effectivenessei for system unit si ; for each specific system effectivenessdimension, its corresponding MOE is:ei , j = f i , j ( pi ) = f i , j ( pi ,1 , pi ,2 ," , pi , ri ), ∀j ≤ li

(8)

Similar to MOP, there is a minimum threshold ei∗, j for MOE
as well.
Measures of Emergence Effectiveness (MOEE)A SoS comprises a number of hierarchical systems, eachsystem composite has a corresponding emergence grade. while
the overall characteristics and behavior of the SoS or its systemcomposites are an outcome of emergence of all cooperative andinterconnected subsystems within them. Therefore, MOEE
serves as the core of SoS measurement, and also shows certain
hierarchical characteristics.
--MOEE on bottom-line emergence grade: systemcomposites corresponding to the bottom-line emergence grade
are system units. Suppose MOEE for a bottom-line emergence
grade was EEi , and it contained oi aspects, where each aspect{

EEi = EEi ,1 , EEi ,2 ," , EEi ,oi}(9)Therefore,
EEi = Ωi (mi , ek , e p , eq ,")(10)

Where Ωi is MOEE function corresponding to emergence
grade i ; ek , e p , eq indicates the effectiveness values of theinterrelated system units sk , s p , sq , which act in accordancewith homologous emergence levels and complete missions
corresponding to SoS or system composite, while mi indicatesthe quantitative architecture of these interrelated system units--MOEE on non-bottom-line emergence grade: systemcomposite on any non-bottom-line emergence grade contains atleast one lower-leveled system complex. They share the samemeasures and constraint function types with the bottom-line
emergence grade MOEE, just differing on each variable’s
indicating meaning.
Measures of System of Systems Effectiveness (MOSE)
Measures of system of systems effectiveness refers toassessment of SoS goal’s final achievement status, determinedby the effectiveness of the behavioral characteristics on global
emergence grade. Suppose SoS’ overall MOSE was E , and it
contained l aspects, namely
E = { E1 , E2 ," , El }

(11)

Then,
E = E ( N , EE ) = E ( N , EE1 , EE2 ," , EEo )(12)

Wherein, E denotes function of MOSE for SoS; EE iseffectiveness function of behavior characteristics on
homologous emergence grade, and it includes o aspects,
corresponding to o sub-operations of the SoS respectively,
namely EE = { EE1 , EE2 ," , EEo } ; While N indicates thequantitative structure of these sub-operations.
Eventually, industrial optimization projects can be carriedout based on the “hidden order” of the tourism SoS evolutionthat discovered by the smart industrial system. It is the ultimate
aim of constructing such a system.
IV. CONCLUSION
Currently, as many disciplines begin to cultivate a set of core
methodologies [25], SoSE, which has its root in context ofmilitary programming, is proven to be a notable research focus,
providing a new perspective to solve the emerging system ofsystems challenges in industrial analysis. SoS thinking is ofgreat significance in cross-disciplinary study [12], and it may
lead a new research paradigm. Its application in tourism
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analysis will provide a solid foundation for industrial
policy-making, planning and forecasting, and will help reducerisk and cost in industrial restructuring.
Modern communication and information technologies
provide greater possibilities for socio-economic sectors to
execute smarter decisions. These technologies will advance the
SoSE industrial application. Therefore, targeting the rareness ofgovernment oriented intelligent decision support system (DSS),
and inspired by the “system of systems thinking”, this paper
presents a technical route for constructing a policy
maker-responsive smart tourism industry information system.
System of systems architecting, geographical simulation,
network analysis with intelligent computing and SoS
effectiveness evaluation are focused in the smart systemconstruction. The overall design include: 1) system of systems
structural architecting; 2) geographical simulation using
geo-science instruments, CA and MAS; 3) SoS evolution
description through network analysis and intelligent
computing; 4) SoS effectiveness measurement using two-tierand four-grade method; and 5) SoSE program for industrial
optimization.
However, SoS tools and methods are not perfect, so it isimportant to draw together academia, government, industrialorganizations and enterprises to collaborate in the related
studies. According to the 10 hot issues leading the future
development of SoSE [7, 8], future research directions for the
tourism SoSE may include: (1) Flexibility, adaptability, andcapability of rapid recovery; (2) Model-driven SoS
architecture; (3) Multi-view product for SoS architecture; (4)Net-centric vulnerability; (5) Evolution; (6) Guided emergenceand capability engineering.
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Liming Bai (M’13) (Inner Mongolian, China, 1980) wasawarded a doctorate in agriculture at Center of Forestry
Remote Sensing Information Engineering, Central South
University of Forestry and Technology, Changsha, Hunan
Province, China in 2011. Her major field of study isremote sensing.
She is a Lecturer in College of Business Administration,
Guangxi University of Finance and Economics. Her current research interests
are tourism system of systems engineering and smart geographical simulation
with intelligent computing, remote sensing and geographic information system(GIS).

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