Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
Prof. Yeonseung Ryu, Myongji University, Korea
Dr. Kyuchang Kang, ETRI, Korea
Prof. Plamena Zlateva, BAS(Bulgarian Academy of Sciences), Bulgaria
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Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
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Dr. Saba Mahmood, Air University Islamabad Pakistan, Pakistan
Prof. Zhiming Cai, Macao University of Science and Technology, Macau
Prof. Man Soo Han, Mokpo National Univ., Korea
Mr. Jose Gutierrez, Aalborg University, Denmark

Copyright ⓒ 2015 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
Dr. Noor Zaman, King Faisal University, Al Ahsa Hofuf, Saudi Arabia
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Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
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Dr. Gyanendra Prasad Joshi, Yeungnam University, korea
Dr. Tae-Gyu Lee, Korea Institue of Industrial Technology(KITECH), korea
Prof. Ilkyeun Ra, University of Colorado Denver, USA
Dr. Yong Sun, Beijing University of Posts and Telecommunications, China
Dr. Yulei Wu, Chinese Academy of Sciences, China
Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
Dr. Harish Kumar, Bhagwant institute of technology, India
Dr. Jin REN, North china university of technology, China
Dr. Joseph Kandath, Electronics & Commn Engg, India
Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
Dr. Mostafa Zaman Chowdhury, Kookmin University, Korea
Prof. Francis C.M. Lau, Hong Kong Polytechnic University, Hong Kong
Prof. Ju Bin Song, Kyung Hee University, korea
Prof. KyungHi Chang, Inha University, Korea
Prof. Sherif Welsen Shaker, Kuang-Chi Institute of Advanced Technology, China
Prof. Seung-Hoon Hwang, Dongguk University, Korea
Prof. Dal-Hwan Yoon, Semyung University, korea
Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2015 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2015 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

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Copyright ⓒ 2015 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2015 GiRI (Global IT Research Institute)Anonymity:
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Copyright ⓒ 2015 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2015 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2015 GiRI (Global IT Research Institute)Volume. 4 Issue. 3

1 Open IPTV Convergence Service Creation and Management using Service Delivery Platform

6J

607

Changwoo YoonETRI (Electronics and Telecommunications Research Institute), Yusung, Daejeon, Korea

2 Structure Safety Inspection System Using Multiple Sensors and PTZ Camer

614

Mi-jeong Park*, Eung-kon Kim*
*Department of Computer Science, Sunchon National University, Korea
SunChon National University, 255 Jungang-ro Suncheon Jeollanan-do, Republic of Korea

3 An Adaptive Cooperative Protocol for Multi-Hop Wireless Networks

620

Smich Butcharoen, Chaiyod Pirak
The Sirindhorn International Thai-German Graduate School of Engineering (TGGS),King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

607

Open IPTV Convergence Service Creation andManagement using Service Delivery Platform
Changwoo YoonETRI (Electronics and Telecommunications Research Institute), Yusung, Daejeon, Korea
cwyoon@etri.re.kr
Abstract—IPTV brought a new digital era in the broadcastingdomain. The main change of digital broadcasting is an advent ofa bi-directional interactive service feature. Using thatbi-directionality, IPTV provides various kinds of convergence
service such as information providing, advertisement, ande-commerce. But the number of services required to be
developed and to be provided to users geometrically increases
and the lifetime of services is shortened requiring services to be
developed within a short period. To support the market demand
for a massive service delivery, the IPTV platform based onservice delivery platform (SDP) is essential. The SDP is atechnique developed to meet the variation in communication andinformation technology convergence environments, allows
service providers to rapidly create and deliver convergence
services. This paper describes an Open IPTV service delivery
platform structure and method for supporting rapid service
creation by reusing pre-defined IPTV service components. We
describe key components and functionalities of SDP, IPTV
Enablers and its combination for service convergence. We show
customer targeted mobile advertisement for Open IPTV
convergence service example.

easily develop those telco-dependent ACAP applications.
The characteristic of IPTV convergence service is adependency with broadcasting content. The convergence
services provided during channel service are tightly coupledwith the contents of channel service. For example, channel
content related information is shown on TV for the
information providing service. For the advertisement, content
related advertisements are selected and shown on TV.

Keyword— IPTV, SDP, SOA, ACAP, Open IPTV Platform

I. INTRODUCTIONThe main environmental change brought on by digital
broadcasting such as IPTV, Smart TV and digital cable
TV is an advent of a bi-directional broadcasting business thatuses a telecommunication return channel. This is a change
from ‘TV as a medium of transaction’, which is one way
watching of analogue TV, to ‘TV as a marketplace’, whichconnects the viewer directly, collects the viewer’s opinion
bi-directionally, and reapplies the viewer’s reaction to service.
Figure 1 shows various examples of bi-directional
convergence
services
combiningbroadcasting,telecommunication and related information [1, 2, and 3].
Using those bi-directional characteristic, IPTV provides
various kinds of convergence service such as information
providing, advertisement, and e-commerce. But developmentof new convergence service is difficult because most of IPTV
broadcasting system in Korea uses ACAP (Advanced
Common Application Platform) as a base middleware
platform [4]. The ACAP API has different shape according to
IPTV service providers. General Service developer cannot———————————————————————
Manuscript received June 1, 2014.
Changwoo Yoon is a principle researcher with Electronics andTelecommunications Research Institute, 161 Kajung, Yusung, Daejeon,
Korea (phone: +82-42-860-6543; fax +82-42-860-5611; e-mail:cwyoon@etri.re.kr).

Fig. 1. Example of IPTV convergence service

The number of services required to be developed and to beprovided to users geometrically increases and the lifetime ofservices is shortened requiring services to be developed
within a short period.
Furthermore, with the rapid
development of information technology such as web
technology, there is a need to integrate information,communication and broadcasting technologies to create newconvergence services. Moreover, users want to receive
services only for themselves and act not only as service
consumers but also service prosumers with the spread ofvarious personal devices. [5] The ACAP is not sufficient tosupport those requirements.
In the enterprise domain, the SOA (Service Oriented
Architecture) has been used for integrating various kinds ofbusiness services. In SOA, systems are divided into basic
service components. These services are distinct units that canbe independently reusable. The Service Delivery Platform(SDP) is one of implementations of the SOA concept in anenterprise domain [6].
A service delivery platform (SDP) is a technique developed
to meet this variation in communication and information
technology environments, allows common carriers to rapidly
create and deliver services efficiently to provide the services
and assists third party service providers or personal
Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

information providers in efficiently participating in service
business. [5, 6]
Conventional techniques relating to the SDP mostly define
the SDP as a set of enablers corresponding to abstract formsof physical devices of a network or the Internet. These
conventional techniques provide general SDP structures inwhich service common functions such as an operation support
system (OSS)/business support system (BSS) are connectedwith the enablers of the SDP to enable rapid service creationand third party service providers and users can abstract
functions to create and provide services even if the third party
service providers and the users are not network operators. [7,8, 9]Fig.2. Major characteristics of SDP

This paper describes an Open IPTV service delivery
platform structure and method for supporting circumstancesin which users can be a service provider. We describe key
components and functionalities of SDP, IPTV Enablers andits combination for service convergence. We show customer
targeted mobile advertisement for Open IPTV convergence
service example.

608

Web Service with application’s context.
B. Enablers
The original concept of enablers came from Open Mobile
Alliance (OMA). The enablers are the function that abstracts
network functions and provides them to third party
application developers through application programming
interface (API). Enablers interact with each other and create
other services. The OMA does not specify a protocol for theinterfaces of enablers or a specific mechanism for combining
these enablers. [6] Enabler is a building block that
encapsulates reusable functionality.
We used Web services specification for the implementationof enablers. Web Service Description Language (WSDL) is
used to describe the interface of services provided by enablers.
Simple Object Access Protocol (SOAP) protocol is used tocommunicate between services.
C. Server Configuration
Figure 4 shows SDP server configuration. The virtual
service and service distribution will be explained at chapter
III. The Session Manager is used to create a new session in
SDP. The service creation environment is used for making
convergence service primary scenario. It provides graphicaluser interface to drag and drop service enabler functions tomake the scenario. Service registry saves service informationand portal server reveals the interface of service enablers to be
used to make convergence service.

II. KEY COMPONENTS OF SDP
A. Common BUS

Fig.4. SDP server configuration

III. KEY FUNCTIONALITIES OF SDP
A. Service Virtualization

Fig.3. Architecture of SDP

Figure 3 is a common architecture of SDP providing
convergence services. The concept of SDP consists of twoparts: Common BUS and Enablers. The Common BUS is an
execution environment of Web services and web service
directories. The common BUS has a similar role with that of
the Enterprise Service Bus (ESB). The ESB is a mediating
hub at the time of Web Service coordination. It acts as amessage broker between services. When an application tries
to find Web Service in its routine, ESB finds the appropriate

Fig.5. Concept of Service Virtualization

Service virtualization is a recent trend of SOA focusing on
providing common infrastructure to create and manage
complex service eco-system. By using service virtualization,
service developer can focus on service feature developing
without worrying on how functions are provided, consumed,
and managed.
Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

Service virtualization does not modify general service code,
rather makes virtual service providing the API functions
before executing service. The key idea of service
virtualization is service brokering residing between service
client and service implementation.
Service brokering is needed for a separation of client andgeneral service. In this architecture, client cannot connect
directly with general service. It can only communicate
through brokering service. At the position of service
brokering, virtualized service is hosted and exported toservice user.
Service broker reads information for DB and decides how
to host virtualized service. This approach exports several
virtualized service for one general service, we can adopt
easily to various customer scenarios.
All communications are transmitted through service broker.
We can provide various kinds of brokering services: version
control, protocol transfer, monitoring, and real time policy
decision. Service virtualization does not change client andgeneral service code.
Service virtualization has several advantages. It provides
independent ownership of SOA’s each layer. For example,
service provider develops service interfaces and logics.
Operator describes endpoint policy and message standards.
Architect professionals define service level policy andexecution patterns. Business owner describes business policy
and requirements. All of above can cooperate at the centre
points.
Eventually, service virtualization reduces time-to-market
for a new service and provides more flexible and substantial
methods for managing service environment.

609

C. Service Authorization
The SDP service delivery control function assists
managing and selling of the value-added services. Delivery
control may include user and partner authentication,authorization, credit control, and SLA enforcement. [12]For services requiring a user limitation, certification is
granted an access to only authorized users. Also the SDP
authorization function provides WS-Security standard that is
Web service authentication capability

Fig.8. Concept of Service Authorization

D. Service Protocol Conversion
To easy the creation of convergence service, an ESB used
as a transitional gateway to convert protocols. Today withintroduction of WEB 2.0, there are a lot of de-fecto standard
protocol like XML/HTTP RPC, REST,JSON etc. Sometimes
SOA systems have to support these kinds of protocols. The
generic proxy pattern provides a protocol converter.

Fig.9. Concept of Service Protocol Translation
Fig.6. Architecture of Service Virtualization

Figure 6 shows implementation model of service
virtualization. It parses general service’s WSDL, analyses
information, and then registers into service registry.
B. Location Transparency

Fig.7. Concept of Location Transparency

For a client, there must be no distinction between the usageof local and remote SOA services. They should be accessed in
the same manner as if they were present in the local SOA
framework. To provide location transparency, the SDP
manages service location at registry and mediate it. With thisfeature, service execution is not affected by the change ofservice location.

There are several types of proxy listed below: [13] Edge Proxy (Inbound): This is entry point of our ESB.
It covers protocol conversion to internal SOA protocol.
(Web services) Common Proxy: This is point to handle cross cutting
concerns for system authorization, authentication,
logging, billing etc. And route the service request to
appropriate local proxy.
 Local Proxy: It is one-to-one mapping to Businessservice. It can just forward request to business service
or it contains mediation logics. (Intermediary logic –Transformation, Routing, Function adding, etc.)
 Edge Proxy (Outbound/Optional): This proxy is
another edge proxy for outbound protocol conversion.
This is optional. In our architecture internal protocols
are integrated with web service. This proxy is used forlegacy integration.
E. Message Format Conversion
Along with the protocol conversion, the message format
conversion is needed because the number and types ofarguments are different.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

610

prosumer support. The Open IPTV platform is prosumer
concept next generation platform technology that anyone caneasily create, process and edit services.

Fig.10. Concept of Message Format Change

F. Message Routing
Based on the type of individual message, the message
routing function sends the message to one of the many
services available out-of-the-box on the platform.

Fig.11. Concept of Message Routing

G. Monitoring
Service delivery platform should have total functions tocreate, manage, and providing convergence service. The
monitoring function is mandatory for managing andprovisioning of convergence services.

Fig.13. Concept of Open IPTV Platform

Typically, IPTV platform means middleware. The IPTV
middleware uses client/server architecture and the client often
resides at the STB. The middleware controls the customer
interaction with the service and, therefore, plays a very
important role in controlling the customer’s overall
experience. The definition of IPTV middleware is a layer ofsoftware between applications and resources, which consistsof a set of service enablers that allow multiple functionalities
running on one or more devices in an IPTV system to interact
across a network. Figure 14 shows a concept of IPTV
middleware.

Fig.12. Concept of Monitoring
Fig.14. IPTV middleware concept

IV. OPEN IPTV PLATFORM
IPTV is defined as multimedia services such as television/video/audio/text/graphics/data delivered over IP based
networks managed to provide the required level of QoS/QoE,
security, interactivity and reliability by ITU-T Focus Group.
If we focus convergence, we can define IPTV as
multimedia services delivered over IP based wire & wirelessconverged networks managed to provide the required level of
QoS/QoE, security, interactivity, reliability and openness, andfor any contents to be optimally consumed and generated byusers anywhere, anytime, and with any device.
The main features of IPTV are bi-directionality andopenness. IPTV platform has an importance to support thesefeatures.
We developed open IPTV platform with fourcharacteristics: openness, personalization, convergence and

IPTV middleware also defines and manages subscribers,
the services available to them, the business rules and thebillable transactions associated with their use of the system. It
also oversees or directly manages content assets, physicalassets, and to an extent, oversees and manages many of thesubsystems of the end-to-end IPTV deployment ecosystem.
As such, it is extremely complex, not to mention the fact thatthe operators deploying it are generally not familiar with someof its core enabling technologies, nor with the services it
enables.
Previously, when only wired and wireless telephones
existed in the communication environment, network operatorsor service providers generated and provided services, and thenumber of generated services was small. A collective systemfor executing a service, such as a service providing system ora service/user management system was constructed for the
Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015service whenever needed. [11]In the current service environment in which circuit-based
communication is changing to Internet-centric packet-based
communication and into which wired and wirelesscommunications are integrated, the number of services
required to be developed and provided to users geometricallyincreases and the lifespan of services is shortened to allow forservices being developed within a short period. Furthermore,with the rapid development of information technology such as
web technology, there is a need to integrate information,communication and broadcasting technologies to create newconvergence services. Moreover, users want to receive
services only for themselves and act not only as service
consumers but also service prosumers with the growing
popularity of various personal devices. [11]611

Fig.16. IPTV platform Enablers

Figure 17 shows how the IPTV service reuses platform
enablers. For example, telemedicine service uses group
casting, messaging, CoD, presence, and video phone
primitive enablers. And it uses screen sharing, simultaneous
viewing, conference screen sharing, and conference
simultaneous viewing convergence enablers.
Other services such as distance education and smart work
shares defined enablers of IPTV platform.

Fig.15. Comparison with Smart Device platform and Service Platform

To meet these changing environments, there are several
attempts to include service platform technologies into IPTV
such as Open API and SDP (Service Delivery Platform).
The Platform makes it possible to develop services network
provider independently. It shortens the development period
by recycling existing services, which makes it easy to develop
various convergence and personalized services. The shorter
development time gives CAPEX/OPEX (Capital Expenditure,
Operational Expenses) advantages to service provider: fastcommercialization, extending business opportunities.
Sometimes, we confuse the service platform to device
platform. The device platform has SDK and Open API to
execute applications in device. The SDK has libraries andinterfaces for OS and applications shown in figure 15.
The service platform resides in server not in terminal
device providing service components and interfaces.
Figure 16 shows Open IPTV platform SDP enablers. It hashierarchical structure. At the very low level of the platform,
network support enablers control network capability
according to service request such as bandwidth.
Primitive service enablers are grouped as broadcast control,
telecommunication service control, web content control, andinformation control. In each group, unit enablers are definedthat has an atomic function. Combining these unit enablers,
the platform consist convergence service control group layer.
The combined enablers defined in highest layer looks like
service. The application server (AS) executes customer
service that calls platform enablers as a reusable function.

Fig.17. Reuse of Enablers

Figure 18 shows service enabler hierarchy. The primitive
service enablers are composed of unit functions, which can beused for creating convergence service shown as grey box infigure 18. For example, service C uses function 1, 2, and 5 in
CoD (Contents on Demand) enabler.

Fig.18. Service Enabler Hierarchy

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

612

Figure 19 shows targeted advertisement service createdusing IPTV platform. While customer are watching VOD orreal-time channel service, advertisement is sent to user’ssmartphone that is prepared according to contents andcustomer properties such as VOD’s actor, user’s sex, age, etc.

terminals. The convergence enabler then inserts
advertisement metadata into golf VOD. A mobile user withsplit EPG terminal requests to view golf contents to open
service platform. As soon as golf moving picture is delivered
to an IPTV user, advertisement request event is generated.
We made other convergence service combining web API
and personal data.

Fig.19. Tatgeted advertisement service

Fig.21. Smart Content Recommendation Service

The split EPG terminal has a function controlling IPTV
service via home AP. A mobile user watches golf sports
channel in VOD service. He already gave his preferences tothe profile enabler via open service platform. For instance,since the profile enabler knows that he enjoys golf as outdoor
sports, when he detects golf driver on watching golf contents,
the mobile advertisement process enabler may give him
advertisement moving picture about newly released golf
driver. Mobile advertisement process enabler may give
helpful information to him as well as connect to the purchasestep by the mobile device.

Figure 21 shows smart content recommendation service
that recommends related content based on user’s personal
history. For the content generation, the enablers search web
space in real-time based on Web open API. The platform
automatically aware the device used, then provide appropriate
contents according to the device’s capability such as CPU
clock speed, screen size, etc.
The SDP has service creation environment (SCE) shown on
figure 22. The SCE has graphical user interface that makes it
possible to edit convergence services using service
components. It also has a creation function of composite
component combining unit components.

Open Service Platform
Split EPG
Terminal(IPTVUsr01)

IPTV Screen

Media Enabler(VODSrv01)

Profile Enabler(IPTVProf01)

Mobile AD
Process
Enabler(ADSrv01)

Mobile AD
Agent(ADProv01)

Deliver Golf Moving Picture

Request User
Preference

Search Golf Ads
Return Ad

Give User
Information
Request Split EPG
User Info.
Acquire Split EPG
User Info.
Insert AD
Metadata Into
Golf VOD

Request Golf VOD
Deliver Golf
Moving Picture

Request AD
Event
Event timeFig.22. Service creation environment and deploy of service

Deliver Golf Moving picture

V. CONCLUSIONS
Fig.20. Service flows of Targeted Advertising Service

Fig. 20 shows example of targeted advertisement service
using SDP. Mobile advertisement agent may be person or
sub-system of an advertisement service provider. He also
watches a golf VOD service via media enabler within open
service platform. The mobile advertisement process enabler
searches a golf advertisement among many advertisement
contents of a mobile advertisement agent. That golf
advertisement is related with revealed product in golf moving
picture. The mobile advertisement process enabler acquires
user preference and check if mobile users own split EPG

Service Delivery Platform (SDP) used for a creation andmanagement of convergence services. Service virtualization
is a key concept of SDP for a service convergence because it
provides service feature independence among unit services(components) of convergence services.
Service provider can create service components using
standard interface without worrying how to integrate each unit
services. After creation of service components, the service
provider can create convergence service combining thosereusable components. Using the SDP’s feature, the efforts ofservice creation can be reduced more than half.
The easy service creation feature will brought a business

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

market chance: creating various business models such as
prosumers, service provider, hosting provider, MVNO, andTelco shown in figure 23.

Fig.23. Business model brought by using SDP

IPTV service is an example of convergence service
between telecommunication and broadcasting services. SDP
will have an essential role on convergence service creationand management because of its SOA concepts.
We developed several service component groups for SDP
based IPTV service platform such as broadcasting service
control, telecommunication service control, web contents
control, network support, and information control. For
example, the telecommunication service control components
group consists of video telephone, group casting, messaging.
With those components, IPTV service provider can create
convergence IPTV service providing telephone call notice or
PIP video-phone screen while watching channel
broadcasting.
Using open platform, we can make social service bymaking social service components shown in figure 24.

613

[4]

ATSC Standard: Advanced Common Application Platform (ACAP),
ww.atsc.org/cms/standards/a_101a.pdf, 2009[5] Changwoo Yoon, Shinmo Kim, Hyunwoo Lee, “Convergence Service
Implementation based on Service Delivery Platform and Research
Issues,” International Technical Conference on Circuits/Systems,
Computers and Communications, pp. 1080-1083, July 2009.
[6] H. Ohnishi, Y. Yamato, M. Kaneko, T. Moriya, M. Hirano, and H.
Sunaga, “Service delivery platform for teleco-enterprise-Internet
combined services”, IEEE GLOBECOM, pp.108-112, 2007.
[7] Sun Young Lee, Jong Yun Lee, and Byung Il Lee, “Service
Composition Techniques Using Data Mining for Ubiquitous
Computing Environments”, International Journal of Computer
Science and Network Security, Vol 6., No 9B, September 2006.
[8] Nilanjan Banerjee, et. Al., “Telecom Telecom mashups: enabling web
2.0 for telecom services”, International Conference on UbiquitousInformation Management and Communication, pp.146-150, 2008.
[9] Moriya, T.; Ohnishi, H.; Yoshida, M.; Hirano, M., “Dataflow
Generation for Service Composition to Incorporate Web andTelecommunication”, GLOBECOM 2007, pp.26-30, Nov. 2007.
[10] Soonchul Jung Mi-Kyoung Kang Dae-Woo Choi, “Call/Messaging
Open API for Telecommunication Services”, 10th International
Conference on Advanced Communication Technology, pp.1139-1143,
Feb. 2008.
[11] Changwoo Yoon, et al., “Convergence Service Implementation basedon Service Delivery Platform and Research Issues,” ITC-CSCC, 2009[12] Johnston, Andy, et al. "Evolution of service delivery platforms."
Ericsson Review 1 (2007): 19-25.
[13] Terry Cho, “ESB Design Pattern – Generic Proxy Pattern,”,
http://javamaster.wordpress.com/page/2/, 2009

Changwoo Yoon received the B.S. degree from
Sogang University, Seoul, Korea, in 1990. He
received M.S. degree from POSTECH, Pohang,
Korea, in 1992. He received Ph.D. degree in
Computer & Information Science &Engineering from University of Florida, US, in2005. Currently he is principal researcher inConsilience-based Research section, ETRI andadjunct professor at University of Science andTechnology. His current research interests include Future Services, Brain
Science, N-Screen, IPTV, Cloud computing, SOA, Service
creation/delivery technology and information retrieval

Fig.24. Social service enablers

The SDP expected to be a key part for M2M (Machine tomachine) or IoT (Internet of Things) service platform becauseof its easy way of creating convergence services.
REFERENCES
[1]

[2]

[3]

Changwoo Yoon, H. Lee, S.H.Jeon, H. Lee, “Mobile Digital Signage
System based on Service Delivery Platform,” ICT Convergence (ICTC),
2011, pp. 582-586, Sep. 2011.
Changwoo Yoon, H. Lee, W. Lyu, B. Kim, “IPTV Service andEvolution direction of technology,” KICS magazine, pp. 3-11, Aug.
2008.
Changwoo Yoon, H.Lee, W.Ryu, “Next Generation IPTV Platform,”
Optical Internet(COIN) 2010 9th international conference, pp.1-3, Jul.
2010.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

614

Structure Safety Inspection System Using
Multiple Sensors and PTZ Camera
Mi-jeong Park*, Eung-kon Kim*
*Department of Computer Science, Sunchon National University, Korea
SunChon National University, 255 Jungang-ro Suncheon Jeollanan-do, Republic of Korea
mj21@sunchon.ac.kr, kek@sunchon.ac.kr
Abstract—In recent years, those aging buildings such as thehigh-rise buildings and the chemical plant structures generatemany social and economic issues due to crack, fire and collapse.
Furthermore, these social and economic issues will lead tonational loss. The aforementioned problems cannot be resolved
through human eye or human power. This thesis proposes the
structure safety inspection system using a small aircraft
equipped with multiple sensors in order to address these
problems. This small aircraft saves and manages the dataobtained through the structure inspection and also provides
efficient information. The purposes of this thesis are tosynchronize, save and manage multiple sensor data, video andthermos graphical data that are transmitted from a small
aircraft and also to implement the safety inspection integrated
management system that provides inspection results and history
information.
Keyword— Multi-Sensor, Small Flying Vehicles, Sensor
Fusion, Monitoring System, PTZ

I. INTRODUCTION

transmission, control technique for sensor data including
sensing space of sensed data, data accumulation and its
transmission.
This study intends to design integrated management system
for safety test that stores and controls multiple sensor data,
image, thermal infrared image, and data being transmitted
from small flying vehicle through its synchronization andprovides test result and historical data information.
II. PROCEDURE FOR PAPER SUBMISSION
Various sensors, imaging equipment and communicationequipment are mounted on unmanned small flying vehicle.
Data obtained from mounted various sensors and devices will
be controlled and information provision service will beresearched [1,2].
A. Review Stage Type of sensor
TABLE 1. Kinds of sensors

Temperature sensor is a device used for recording orRecently, as outdated high-rise buildings, bridge piers,
wind power generators, petro-chemical plant structures
are deteriorated, a lot of problems for the dangerous situationsuch as internal/external cracking, fire and disasters are takenplace and create a social issue. In order to detect dangerous
situations and cracking, there is a small flying vehicle that
does the work that is hard to be inspected by the workers as aproxy. This small flying vehicle mounted with various
sensors provides a function of integrated management
services from structure monitoring to safety inspection. A
process of developing small flying vehicle and verifying its
performance for safety inspection of structures is continuity ofcomplicated process from its design to test flight and a lot of
cost and research time are required. In order to perform safety
inspection for the structures, mounted various sensors are
required to be measured and measured result is required to betransmitted. This study intends to suggest efficient wireless———————————————————————
Manuscript received October 31, 2014 This work was financially
supported by the Ministry of Education (MOE) and National Research
Foundation of Korea(NRF) through the Human Resource Training Projectfor Regional Innovation (No. 2014H1B8A2032217).
E. Kim is with Department of Computer Science, Sunchon National
University, 255 Jungang-ro Suncheon Jeollanan-do, Republic of Korea(phone : 82-61-750-3627, fax : 061-755-7999, email: kek@ sunchon.ac.kr).
M. Park. is with Department of Computer Science, Sunchon National
University, 255 Jungang-ro Suncheon Jeollanan-do, Republic of Korea
email : mj21@ sunchon.ac.kr).

CO Sensor

VOC Sensor

Dust Sensor

Ozone Sensor

CO2 Sensor

Humidity sensor

controlling temperature by detecting temperature of fluid suchas air or water or wall. Detected temperature is converted toelectric signal and transmitted [3].
Humidity sensor is a device that detects humidity andoutputs such humidity as electric signal. This sensor uses amechanism that electric resistance and its capacity of porous
ceramics and polymer electrolyte are changed due to physicalabsorption of moisture.
CO sensor is a sensor used for detecting carbon monoxide.
In our living environment, dangerous gases of numerous types
exist and recently, gas-related accident in ordinary homes,
business places and construction sites, explosion accident inpetrochemical combine, mines, chemical plants and pollution,
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

contamination are taken place in a row. As it is almost
impossible for human sensory system to quantify
concentration of dangerous gas or discriminate its type, gassensor that uses physical, chemical properties of substances isused for detecting gas leakage, measuring its concentration
and warning dangerous situation.
CO2 sensor is a device for measuring carbon dioxide. Most
common principle of CO2 sensor is NDIR and chemical gas
sensor. Measuring carbon dioxide is important for monitoring
indoor air quality and various industrial processes.
Ozone sensor is a device for measuring ozone
concentration in atmosphere. Dust sensor is a device fordetecting floating dust or number of condensation nucleus.
VOC sensor is a device for evaluating air quality based on
the amount of volatile organic compounds –cigarette smoke,
harmful smoke and odour being generated at the time ofcooking, outside pollution source, pollution source of livingorganisms [4].
B. Final Stage Structures that require safety test by small
flying vehicle
TABLE 2. Diversified structures that require safety test

a. High-rise building

b. Building insidec. High smoke stackd. Chemical plantf. Cultural property

g. Wind power generator

615

and emission gas in internal/external walls of plant smoke
stack and chemical plants in real time, an advantage of being
able to prevent an accident in advance is provided. In addition,site application for external cracks inspection throughcinematography and internal cracks inspection throughthermal infrared camera for the blades and tower of wind
power generator that is popular as a renewable energy source
will be performed and safety inspection manual for wind
power generator is under the process of R & D.
C. PTZ(Pan Tilt, Zoom) Camera
Format and save your graphic images using a suitablegraphics processing program that will allow you to create theimages as PostScript (PS), Encapsulated PostScript (EPS), or
Tagged Image File Format (TIFF), sizes them, and adjusts theresolution settings. If you created your source files in one of
the following you will be able to submit the graphics without
converting to a PS, EPS, or TIFF file: Microsoft Word,
Microsoft PowerPoint, Microsoft Excel, or Portable
Document Format (PDF). GWP is a scale of evaluating how
much heating effect of gas of 1kg is for a certain period oftime after greenhouse gas is emitted in the atmosphere whencomparing it with carbon dioxide of 1kg. In case of assuming
CO2 as 1 based on 100 years, CH4 is 21, NO2 310, HFCs
1,300, PFCs 7,000 and SF6 23,900, respectively [3]. The
location information of camera consists of the coordinates torepresent the current location of moving objects andstructures, the coordinates to represent the location of camera
and the distance, height difference and angle between theobjects.

Fig. 1. Location Information of Camera

Table 2 is an illustration of diversified structures [5]. a. At
the time of constructing high-rise building, safety problem isrequired to be inspected and for outdated high-rise buildings,
efficiency of service life extension is provided by detecting
cracking and abnormal temperature by its deterioration, b. By
detecting cracks of building inside, building safety is
increased, c. By monitoring cracking and dangerous elementsof cultural property, national waste could be reduced.
Recently, due to frequent fire in chemical plants, safety isrequired in reality, d. f. By analysing, investigating cracking

Fig. 1 is the location information of camera.
The location information of an object consists of thecoordinates to represent the current location of a moving
object, the coordinates to represent the location of a camera
and the distance, height difference and angle between theobjects. The location value of camera will not be changed
once it is installed. The location of a moving object continues
to change; thus, the distance, height difference and angle
value between a camera and a moving object shall becalculated based on the pre-defined formula for the twocoordinates.
and angle determines the
The moving object
horizontal rotational directionof a camera, whereas the
distance (d) between objects determines the enlargement andreduction magnification of a camera. In addition, the height
difference of a camera determines the vertical rotational angleof a camera [6].
It is required to control the operation of horizontal
rotational angle, the vertical rotational angle and the

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

enlargement/reduction value for PTZ camera. [7]The operation signal of a camera requires a conversion
between those coordinates and Cartesian coordinates. It isalso necessary to have the coordinatesof a moving
object calculated on the orthogonal coordinate plane and thelocation coordinates of a camera. It is possible toobtain the distance difference between two points
Formula (1) by utilizing the two coordinates.

through616

Fig 3. Scope of vertical rotation

The scope that the vertical rotational angleof a camera.
can have is
The reliability of operation control value of PTZ camera
for the operation control of a camera will increase with ahigher degree of accuracy for the process of obtaining thelocation of an object.
III. CONTENTS AND METHOD OF STUDY(1)
It is required to calculate the horizontal rotational angle of360 by using Formula (2) for 360 degree system by
converting the reference coordinates into the polar
coordinates that uses the location coordinates of a camera as
the starting point.

There are various sizes, heights and shapes in relation to thestructures. It is difficult for a small aircraft to approach atshort ranges in terms of detecting crack or risk factors whenaccessing to a structure. PTZ camera is useful as a camera to
be installed on a small aircraft to detect the location of cracks
in a structure because it can be enlarged and also it is able tomove to left and right. Moreover, such functions of PTZ
camera as pan, tilt and zoom can be utilized in order to detectcrack and risk factors when a subject is at a remote location.
A. PTZ Module of Video Camera
It is possible to examine in more detail a structure by
conducting PTZ control of a video camera in order to conductvisual inspection of a structure that is hard to access from ashort distance.
It is possible to shoot a structure in a wide area with 355° atleft and right and 120° at top and bottom of PTZ camera. It isalso possible to recognize an object at up to 30M in dark
nature or at night with the function of IR camera.
The screen of PTZ camera is set at 200 million pixels. The
problem associated with blind spot when conducting
horizontal and vertical rotation and enlargement/reduction
operation can be solved.

(2)

Fig. 2. Scope of horizontal rotation

The scope of horizontal rotational angleof a camera is.
As for the vertical rotational angle of a camera , theheight relative to the surface from the installation point of acamera is , whereas the height of a moving object is
They can be obtained through Formula (3).

.

Fig. 3. PTZ camera of video camera

Fig. 3. shows PTZ camera that is able to detect high-risebuildings and places that cannot be detected by human eyes
because it is equipped with a small aircraft.

(3)

Fig. 4. Small aircraft equipped with multiple sensors

Fig. 4. Shows a small aircraft equipped with multiple

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

sensors. It examines the safety inside and outside a structure
through multiple sensors and video sensors.

Fig. 5. Multiple sensor module embedded system

Fig. 5. is the embedded module that processes and analyses
the sensors of various types installed in a small aircraft.
Currently, the research project that focuses on thetransmission protocol and sensor data monitoring system toreceive various signals is in progress based on the embedded
system to receive the signals of sensors. At present, it is themonitoring system that measure carbon dioxide in the air
using CO2 sensor and sends signals when the amount andproportion of carbon dioxide are high.

617

sensor and embedded system receives data transmitted fromsmall flying vehicle and processes received data in real timeand at the time of risk occurrence, informs users of dangerous
situation through smart phone.
As embedded system that is data processing system isrequired to process data collected by heterogeneous multiple
sensors based on processing module that may process data inreal time with high speed and perform a function ofprocessing images in real time at the same time, it was
designed based on excellent performance. In addition, as
sensors being used for multiple sensor monitoring system use
diversified sensors including PTZ camera having a functionof infrared ray, temperature, humidity sensors, it is required touse and provide diversified interfaces.
Embedded system provides a function of acquiring,processing and transmitting various sensor data andcontrolling sensors by analysing instructions after receiving it
and provides monitoring information of sensors so that status
information of each sensor could be observed remotely. At thesame time, in order to synchronize data collected by eachsensor, it provides a separate function and a process of storage,transmission processing of sensor data is required.

C. Structure of Multiple Sensor Monitoring System
The whole configuration diagram of the system proposed inthis paper is shown on figure 7.

Fig. 6. Structure safety inspection integrated management system basedon a small aircraft

Fig. 6. shows the structure safety inspection integrated
management system based on a small aircraft. It consists ofpower supply unit, web server and Wi-Fi based wireless video
transmission system to perform the function of storage server.
It is the low-power video management server system basedon an embedded system that combines, saves and manages
multiple sensors and PTZ camera. As for videos, those videos
transmitted in motion JPEG encoding are synchronized inaccordance with the time. Upon request of users, the sensing
data and videos are also synchronized and transmitted.
B. Design of multiple monitoring system
Multiple sensor monitoring system is a system forpreventing occurrence of cracking and various dangerous
situations due to diversified causes including deterioration or
defective work of structures in advance. In addition, thissystem inspects data in the air in real time using unmanned
flying vehicle and by using obtained data, monitors subject
structure in real time.
Multiple sensor monitoring system is mainly divided into
small flying vehicle and embedded system. Small flying
vehicle is an unmanned flight system that performs datadetection and transmission in real time by using multiple

Fig. 7. System Configuration Diagram

As main function of multiple sensor monitoring system isthat the functions such as high speed input/output processingof various sensors, real time synchronization of sensing data,transmission of processed data are required to be performed at
the same time, real time monitoring is required. By flight of

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

small flying vehicle, each sensor starts to be operated and at
this time, storage of sensing data and its transmission module
are required. In addition, this system was designed to transmit
and store collected data in big data storage device and process
stored data after the flight is finished.
Each module of multiple sensor monitoring system wasdesigned to perform each function that is required to beprovided by data processing system. In order to process image
data of PTZ camera, compression module is provided and fordata synchronization of various sensors, synchronized moduleand transmission module for transmitting data in real time areprovided. In addition, in order to process input/output ofdiversified sensors being connected through each interface,temporary storage space was provided. These modules
provide informing and monitoring system function throughsmart phone by it being implemented in parallel.

Fig. 8. Structure inspection of small aircraft

Fig. 8. shows how a small aircraft inspects a structure.

IV. SENSOR CONTROL AND DATA PROCESSING
A. Sensor Control
Multiple sensor monitoring system provides real time
control function to multiple sensors. As this system uses
operating small flying vehicle and commanding embedded
system, multiple sensor monitoring system provides control
function to diversified sensors. This system controls sensors
by transmitting instructions to sensor that is desired to becontrolled and using data processing system that analyses
transmitted instructions.
B. Data Processing
In multiple sensors monitoring system, sensing datacollected by each sensor is processed in real time andtransmitted to embedded system. However, as transmission
rate of transmission route being used at the time oftransmission by embedded system is limited, in order totransmit big data, a process of compressing sensing data isrequired. In particular, as image data being collected bycamera is big data, its compression process is essential and ascompressed image data is also unable to be transmitted at one
time, it is required to be transmitted to embedded system bydividing it into transmissible packet by using a separatetransmission format. In addition, as data of each sensor isrequired to be transmitted by division at the time oftransmitting sensing data, transmission format is required to
be indicated with tag matching with each sensor. At the sametime, tag of sensing data based on time synchronization isrequired to be included.
C. Video of PTZ Camera
It is required to fly a small aircraft and transmit theoperation of multiple sensors and the data of PTZ camera to
the integrated management system. The transmitted data canbe verified through the monitor screen. It is possible toconfirm the location immediately through GPS sensor anddetect the crack and risk factors of a structure.

618

a

b

c

d

Fig. 9. Cracked wall surface taken by a small aircraft

Fig. 9. is the conventional figure taken by PTZ camera of asmall aircraft. b is the enlarged version by 2x zoom. c and d
are the enlarged versions by 3x zoom. PTZ camera allows us
to take pictures of wall surfaces located at a high-rise building
and check them accurately and specifically.

V. EXPERIMENTAL RESULT
It is difficult to visually confirm the crack and risk of highrise buildings and high structures. They can be identified
using PTZ camera equipped on a small flying object. In theexisting method, the person may face with dangerous situation
as the person should climb directly to the high rise building.
With a small flying object, however, the location of cracks
can be safely identified.
The places where the experiment was performed with asmall flying object are the inside and outside of building 3 atthe department of engineering, Sunchon University. And theexperiments were performed 6 times. Even though the video
was out of focuses by the shake during video shooting,relatively stable video could be acquired. 4 videos out of 6
were accurate and the location of crack were accurately
identified with GIS sensor
Table 3 shows the result of experiment with a small flying
objectTABLE 3 result of experiment with a small flying object

Number of experimentVideo acquired

Video accuracy

6 times

4

88%

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

619

VI. CONCLUSION
At present, a lot of small flying vehicles are applied,
researched and developed in various fields including shootingsite of TV program such as entertainment and documentary,
patrol purpose for safety control, exploration purpose forroving Mars and other purpose of diversified specificities.
This study is being progressed by using small flying
vehicle for safety inspection of structures. By using small
flying vehicle for safety inspection of structures, development
process that may collect, store, transmit and process multiple
sensor data of multiple sensor monitoring system was
explained. In multiple sensors monitoring system, modules
for high speed input/output processing, real time sensor datasynchronization, compression, and transmission of data were
designed.
This thesis focused on the use of a small aircraft for thesafety inspection of a structure. This thesis described themultiple sensors and PTZ camera for the structure safety
inspection. Also, it described the structure safety inspection
integrated management system based on a small aircraft,which can collect, save, transmit and process data. As for themultiple sensors monitoring system, such modules as
high-speed input/output processing, real-time sensor datasynchronization, data compression and transmission were
designed.
In the future study, a system that may increase utilization
accuracy of various sensors through diversified direct tests byusing various sensors and prevent structural cracking anddangerous situations in advance through analysis of image
data being transmitted from PTZ and thermal infrared camera
is scheduled to be researched.
It is expected that based on this system, various structures
would be free from the danger of fire, disaster anddeterioration and its high-levelled safety would be maintainedwith reasonable coast.

Mi-Jeong Park
Mi-Jeong Park received the B.S. degree from
Korea , Gwangju University, Gwangju, Korea, in
2004, She is M.S degree from department ofcomputer science, Sunchon National University,
Korea, in 2012, She is currently a Ph.D. student incomputer science at the Sunchon National
University, Korea, Her current research interests
include augmented reality, image processing,computer graphics.
Eung-kon Kim
Eung-kon Kim received the B.S. degree from
Chosun University, Gwangju,, Korea, in 1980, his
M.S degree from department of electronics,
Hanyang University, Seoul, Korea, in 1987, his
Ph.D. degree from Chosun University, Gwangju,
Korea, in 1992. His current research interests arecomputer vision, virtual/augmented reality, image
processing, and computer graphics. Currently he isa professor in department of computer engineering,
Sunchon National University, Korea.

ACKNOWLEDGMENT“This research was financially supported by the Ministryof Education (MOE) and National Research Foundation of
Korea (NRF) through the Human Resource Training Projectfor Regional Innovation (No. 2014H1B8A2032217).”
REFERENCES
[1]

[2]

[3]

[4]
[5]
[6]
[7]

Y. S. Bae1, J. S. Kim1, #J. S. Bae. Development of Measuring
Multiple Sensor Modules for Yellow Dust & Co.”, Korean Society for
Precision Engineering, Spring Leaders` Forum 2011, pp. 487-488, may,
2011
Jonghyuk LeeO , Hyunwoo Joe, Euteum Jo, Hyungshin Kim,
“Development of Multi-Sensor Data Processing System for Unmanned
Aerial Vehicle”, Korea Computger Congress 2009., Vol 36, No. 1(B).,502-506, 2009
Chang-Keun Ryu, Chan-Bang Park A Novel Clustering Method withTime Interval for Context Inference based on the Multi-sensor Data
Fusion , The journal of The Korea institute of Electronic
Communication Sciences2013. 3.22 #9, 3
ko.wikipedia.org
https://www.google.co.kr/imghp?hl=ko&tab=wi&ei=9IrPUrijJYOAl
QXjzoGgAQ&ved=0CAQQqi4oAg
Korea Electronics Technology Institute, “CCTV Market Trends
and Outlook”, 2011.
C . Micheloni and G. L. Foresti, “Zoom on Target While Tracking”,
IEEE International Conference on Image Processing, 2005.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

620

An Adaptive Cooperative Protocol
for Multi-Hop Wireless Networks
Smich Butcharoen, Chaiyod Pirak
The Sirindhorn International Thai-German Graduate School of Engineering (TGGS),King Mongkut's University of Technology North Bangkok, Bangkok, Thailand
smichb@tot.co.th, chaiyod.p.ce@tggs-bangkok.org
Abstract— A Multi-hop relay communication system has
gained tremendous attention recently as it reduces capital
investment for expansion of coverage area. In this work, an
adaptive cooperative decode-and-forward (DF) protocol wasdeveloped to enhance the performance of the system. In particular,a ‘best destination relay selection’ approach (BDRS) was proposedto achieve high diversity gain. The approach starts by selecting adestination node with the maximum channel gain first, and thenselecting a relay node with the minimum outage probability
afterwards. Closed-form expressions for the outage probability,
which is a measure of system performance, were derived. Theirvalidity was checked by comparing the calculated results with thesimulated results from MATLAB software. Both results were
found to be almost identical. Simulated outage probability of the
proposed protocol was also found to be many folds lower thanthose of conventional and modified multi-hop communication
protocols. Lastly, an optimum power allocation scheme wasapplied to further minimize outage probability.
Keyword—Cooperative Communications, Multi-hop, Relay
Selection, and Routing Strategy.

I. INTRODUCTIONM

ULTI-HOP relay communication has become more andmore important for the new generation of wirelesscommunication system. It helps expanding service areas,
increasing system capacity, and reducing the investment budget
for base station expansion. The basic idea of radio relaying [1]is to use a relay node to process base station signal and send
them out to another relay or destination node. Multi-hop
relaying can expand cell coverage and reduce deadcommunication spots. Relays can take up some of the trafficload when the full capacity of the base station has been reached.

———————————————————————
Manuscript received October 29, 2014. This work was financially supported
by TOT Public Company Ltd., Thailand.
Smich Butcharoen is a PhD. candidate at The Sirindhorn International ThaiGerman Graduate School of Engineering, King Mongkut’s University ofTechnology North Bangkok (smichb@tot.co.th).
Assistant Professor Dr.Chaiyod Pirak is a supervisor at Mobile
Communications and Embedded Systems Laboratory, The Sirindhorn
International Thai-German Graduate School of Engineering, King Mongkut’s
University of Technology North Bangkok (corresponding author phone:+6625552000 ext. 2929 ; fax:+6625552937; e-mail: chaiyod.p.ce@tggsbangkok.org).

Radio relaying saves the terminal’s transmitting power, which
in turn prolong battery life.
The relay-based architecture, its cooperation diversity, andcooperative multipath technology have already been
implemented worldwide. All of the standards for existing andnear future mobile communications systems—e.g., The 3rdGeneration Partnership Project (3GPP) [2] and broadband
wireless network (IEEE 802.16j) [3]—rely on the concept ofrelaying. A new project, the Wireless World Initiative New
Radio (WINNER) project has planned out in detail an
implementation of a ubiquitous broadband mobile radio
relaying system.
For a small relaying system of one relay and one destination,
there is only one possible route from source to destination.
However, in a large relaying network (e.g., wireless sensor
network), there are many relays between the source and thedestination. A key question is which relay node should beselected to forward the information. One of the relay selection
techniques is to simply pick the ‘best’ of all available relays fortwo hops and using that relay to decode and forward theinformation. In [4], the authors present a selection criterion of22𝜂𝑙 = min [|ℎ𝑆,𝑅𝑙 | , |ℎ𝑅𝑙,𝐷 | ] and an alternative ‘smoothed-out’
version of 𝜂𝑙 = 2/ (1|ℎ𝑆,𝑅𝑙 |2+

1|ℎ𝑅𝑙 ,𝐷 |2), where ℎ𝑆,𝑅𝑙 is thechannel impulse response between the source (S) and relay (𝑅𝑙 ),ℎ𝑅𝑙 ,𝐷 is the channel impulse response between relay 𝑅𝑙 anddestination (D), and l = 1, 2,..., L. The relay node with the ‘best’
channel that produces the highest 𝜂𝑙 is selected to retransmit thesignal. This selection criterion helps avoid system bottleneck,but it might not be optimum. Another simple way to find asuitable route is by following the conventional protocol. In thisprotocol, the system selects the best relay that exhibits the
maximum received signal to noise ratio (SNR) for each hop on
a hop-by-hop basis. Yet, there is a problem with the last hop
since there is no alternative route to the final destination. To
solve this problem, the authors of [5] proposed an ad-hoc
routing strategy–joint selection for the last two hops. We callthis technique ‘modified conventional multi-hop strategy’. This
strategy is easy to implement but its performance can still be
improved. We describe it in details in system model B.
In [6], the best relay selection based on the technique ofminimum outage route (MOR) was proposed. The authors
assumed that each relay node can receive information from the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

nodes of two previous hops and combine their signals using acooperative protocol. This assumption corresponds to adiversity of two transmitting nodes. The performance of such asystem is superior to that of the system described in [5].
Nonetheless, the system performance is still not optimum withrespect to the outage probability.
In contrast to the dual-hop transmission in [6], the authors in[7] analyzed transmission by multiple relays, where every relay
retransmits the signal from the source to the destination. They
found that the outage probability was not reduced by much as
the number of relays increased. In fact, in some instances, more
relays increased the outage probability. On the one hand,increasing the number of relay nodes increases the possibility
that a successful relaying can occur, which increases the mutual
information between the source and destination. On the other
hand, increasing the number of relay nodes increases theprobability of outage condition at the relay and destination
nodes. This trade-off is a result of the time-division channel
allocation scheme that allows proportionally less time for thesource to transmit its signal when the number of relay nodes isincreasing, i.e., the source is allowed to transmit only a fractionof 1/(m+1) of the time, where m is the number of relays. Hence,
an optimal number of relay nodes exists, and it could bedetermined based on the SNR and channel conditions.
Similar to the model in [7], in [8], the authors investigated amodel of two-hop transmission with a cooperative protocol andintroduced a strategy of selecting the best relay based on thereceived SNR and channel quality instead of that of using all
relays at a reduced power in [7]. They also derived the model’s
outage probability. Their result showed a better performance
than that of [7]. In [7], the multiple relays could not reduce theoutage probability significantly, but the best relay selection in[8] was very different–a larger number of relays resulted in amuch higher reduction of outage probability.
In [9], for the purpose of further improvement of the system
performance realized in [8], the authors applied a cooperative
protocol with Alamouti-coding scheme but still used the best
relay selection technique proposed in [8] to their system of twohop transmission with one source, one destination, and two
candidate relays. After the best relay was selected from time totime, four time slots were used to transmit two symbols,
following the Alamouti-coding scheme; therefore, the systemlost some bandwidth efficiency. To compensate, the authors
employed Quadrature Phase Shift Keying (QPSK) modulation.
They also derived an expression for the system’s outage
probability. Their simulation results showed that their system
performed better than that in [8] by 1.5 dB at an SNR higher
than 20 dB.
In [10], the authors considered a linear network model of amulti-hop relay communication system consisting of a source,a destination, and several relays equipped with multiple
antennas. They did not focus on the direct link from the source
to the destination within the same cluster. Distributed spacetime coding was used to maximize diversity gain. In their
discussion, the answer to whether to code a multi-hop relay
channel in space or time was that it depends on the available
resources such as availability of feedback, decoding

621

complexity, and various requirements of wireless applicationsuch as rate, latency, etc.
In this paper, we could not compare our results with those in[10] even though we used a linear network model similar totheirs because their relays were equipped with 2 antennas whileours had only one. Similarly, the cooperative protocol in [6],[7], [8], and [9] could not be compared to ours either because
their systems consisted of only two hops while ours consistedof more than two hops, similar to those of [4] and [5]. In reality,
multi-hop communication uses many hops and relays in eachhop. When a cooperative protocol is applied, there are many
candidate relays and candidate destinations in each cluster.
There has never been proposed a selection strategy that gives
the highest priority to the destination. That was the motivation
for this research. Our reason was that the destination of every
two hops is the source of the next hop, and we wanted the bestsource for the next hop, so it was sensible to give the best
destination the highest priority. We named our proposed
protocol the ‘best destination relay selection (BDRS)’
approach. We also derived a closed-form expression of theoutage probability of our system. To validate it, we compared
the calculated results against the simulated ones. We also
evaluated our system performance against the modified
conventional multi-hop strategy employed in [5]. Finally, we
further improved our system performance by employing
optimum power allocation for source (S) and relay (R). The
results were very interesting. The system tried their best to
maintain the S-D link by allocating more power to the source,even when the quality of the transmission link was poor. Thisfinding confirmed that selecting the best destination node firsthas a tremendous impact on the system’s performance.
The rest of this paper is organized as follows: in section II,models of the wireless communication system used in this study
is described; in section III, a closed-form outage probability of
our system is derived; in section IV, the simulated andcalculated results are discussed; in section V, the optimum
power allocation scheme and results are discussed; in section
VI, the expected real-world implementation issues are
considered; and section VII is the conclusion of this paper.

Fig. 1. A multi-hop communication system.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

II. SYSTEM MODEL
In a multi-hop communication where a source nodecommunicates with a destination nose that is out of its coveragearea, relays serve the function of a base station by recovering
the signal received from the source node and retransmitting itto the desired destination. Hence, relays can reduce the cost ofinvestment of a multi-hop communication system installation.
Figure 1 shows a feasible communication area, a number ofhops, and an example of a relay selection strategy. In this paper,
our system employs a dual-hop relay transmission, in which acluster of nodes consists of two hops. All nodes in a cluster canreceive the transmitted signal from the source node.
In this section, we describe our system model of theconventional multi-hop relay selection approach and themodified conventional multi-hop relay selection approach.
For the sake of simplicity, we depicted an idealized linear
network model in Figure 2 with M hops, L relays, and L
destinations in each hop [5]. This routing strategy is called an
Ad-hoc routing strategy.

622

equipped with one antenna employing a Time Division
Multiple Access (TDMA) technique without spatial reuse and adecode-and-forward strategy for signal repeating. A selected
relay decodes and re-encodes a packet fully before it isforwarded to the next destination. A relay can receive the signal
from a neighboring relay only if they are in the same cluster.
The solid line is an example of a route that connects nodes thatexhibit the highest received SNR of each hop.
A. Conventional Multi-hop Relay Selection Approach
Given a source and a destination, relays are selected toprovide the route that is the least error-prone in 2 selection
phases [5]. In the first phase (phase 1), for any cluster k where

Fig. 4. An idealized linear network model with M hops, L relays, and K
clusters, showing direct links in each cluster.

k = 1, ..., K – 1, the relay (𝑅𝑘∗ ) that exhibits the highest received
SNR from S0 is selected. 𝑅𝑘∗ is found by solving 𝑅𝑘∗ =ℎ𝑜𝑝

arg max (𝛾𝑆,𝑅𝑙,2𝑘−1 ),where𝑅𝑙,2𝑘−1

Fig. 2. Idealized linear network model with M hops, L relays, and L
destinations for each hop.

In figure 2, it can be seen that there are one source (S), one
destination (D), M – 1 hops, K clusters, and L linearly
distributed nodes. A cluster is defined as a combination of one
source, L relays, and L destination nodes for every twoconsecutive hops, and each node of the linearly distributed
nodes functions as either a relay or a destination. The
relationship between the index of a cluster (k) and the index of𝑦 ∗ = √𝑃𝑆 ℎ ∗ 𝑥 + 𝑛 ∗
𝑅𝑘
𝑆,𝑅𝑘
𝑆,𝑅𝑘
ℎ𝑜𝑝

a hop (m) is m = 2k.
Figure 3 shows an example of a relay selection in the 1stcluster. S0 is the source node, Rl,1 represents the relay l of the
1st hop, and D1,2 represents the destination node l of the 2ndhop, where l = 1, 2,…, L and the distances between hops areequal. The channel impulse response of the system isindependently and identically distributed. Each node is(1)

where Ps is the transmitted power from S to 𝑅𝑘∗ , 𝑥 is transmitted
symbols, ℎ𝑆,𝑅𝑘∗ is the channel impulse response of the S𝑅𝑘∗ link,
and 𝑛𝑅𝑘∗ is the Additive White Gaussian Noise (AWGN) at 𝑅𝑘∗ .
In phase 2, 𝑅𝑘∗ decodes and re-encodes data then forwards
them to a selected destination 𝐷𝑘∗ . 𝐷𝑘∗ is the node 𝐷𝑙,2𝑘 thatexhibits the highest received SNR from 𝑅𝑘∗ . It is determined bysolving

Fig. 3. The first cluster of an idealized linear network model, consisting of 2
consecutive hops, L relays, and L destinations.

2𝛾𝑆,𝑅𝑙,2𝑘−1 (which is equal to 𝑃𝑠 |ℎ𝑆,𝑅𝑙,2𝑘−1 | /𝑁0 ) represents thereceived SNR from S to Rl,2k-1 (where l = 1, 2,…, L and 2k-1 are
the indices of relays and the set of relays in the kth cluster,
respectively). The received signal at a selected relay 𝑅𝑘∗ can becalculated from the following equation,

𝐷𝑘∗ = arg max (𝛾𝑅𝑘∗,𝐷𝑙,2𝑘 ),
𝐷𝑙,2𝑘

where

2

𝛾𝑅𝑘∗ ,𝐷𝑙,2𝑘 (which is equal to 𝑃𝑅 |ℎ𝑅𝑘∗ ,𝐷𝑙,2𝑘 | /𝑁0 ) represents thereceived SNR from 𝑅𝑘∗ to 𝐷𝑙,2𝑘 of hop 2kth (l = 1, 2,…, L and 2k
are the indices of relays and the set of relays in the kth cluster,
respectively). 𝑅𝑘∗ decodes and remodulates data 𝑥̃ before
forwarding it to 𝐷𝑘∗ in the kth cluster. The received signal at aℎ𝑜𝑝selected destination relay, 𝑦𝑅∗ ,𝐷∗ , for multi-hop can be𝑘𝑘calculated from the following equation,

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

̃ +𝑛 ∗𝑦 = √𝑃𝑅∗ ℎ ∗ ∗ 𝑥
𝐷𝑘
𝑘 𝑅𝑘 ,𝐷𝑘(2)

where 𝑃𝑅𝑘∗ is the transmitted power from 𝑅𝑘∗ to 𝐷𝑘∗ , 𝑥̃ areremodulated data to be forwarded, ℎ𝑅𝑘∗ ,𝐷𝑘∗ is the channel impulse
response between 𝑅𝑘∗ 𝐷𝑘∗ , and 𝑛𝐷𝑘∗ is the Additive White
Gaussian Noise (AWGN) at 𝐷𝑘∗ in the kth cluster.
B. Modified Conventional Multi-hop Relay Selection
Approach
For the last cluster, there is only one destination available
which makes 𝐷𝑘∗ = 𝐷0 . In this approach, the only majordifference from the conventional multi-hop relay selection
approach is that the last two hops in the Kth cluster has to behandled jointly in order to achieve a higher diversity gain. For
the hop 2K-1, instead of determining an 𝑅𝑘∗ from the maximum
2

∗
∗
𝛾𝐷𝑀−2
,𝑅𝑙,𝑀−1 | /𝑁0 ),,𝑅𝑙,𝑀−1 (which is equal to |ℎ𝐷𝑀−2

we∗determined it from arg max (min (𝛾𝐷𝑀−2,𝑅𝑙,𝑀−1 , 𝛾𝑅𝑙,𝑀−1 ,𝐷0 )) ,𝑅𝑙,𝑀−12∗∗where 𝛾𝐷𝑀−2,𝑅𝑙,𝑀−1 | /𝑁0 ) is
,𝑅𝑙,𝑀−1 (which is equal to 𝑃𝑠 |ℎ𝐷𝑀−2∗
the received SNR between 𝐷𝑀−2(of hop (M-2)th) and Rl,M-1,
where l = 1, 2, …, L and M-1 are indices of relays and hops intheKthcluster,
respectively,
and
2

𝛾𝑅𝑙,𝑀−1,𝐷0 (which is equal to 𝑃𝑅 |ℎ𝑅𝑙,𝑀−1,𝐷0 | /𝑁0 )isthereceived SNR between 𝑅𝑙,𝑀−1 (of hop (M-1)th) and D0, where l= 1, 2, …, L and M-1 are indices of relays and hops in the Kthcluster. A summary of this routing strategy is shown in Table 1.
TABLE IROUTING STRATEGY FOR MODIFIED CONVENTIONAL MULTI-HOP RELAY
SELECTION

Let l = 1, 2,…, L; m = 1, 2,…, M ; and k = 1, 2, …, K be
the indices of relays, hops, and clusters in a communication
network. Let S denotes the source node; 𝑅𝑘∗ denotes theselected relay node in the kth cluster; 𝐷𝑘∗ denotes theselected destination node in the kth cluster. A hop index m
is always equals to 2k.
Initialization: S = 𝑆0
Recursion:
For k = 1 to K-1
𝑅𝑘∗ = arg max (𝛾𝑆0 ,𝑅𝑙,2𝑘−1 ) , l = 1, 2,…, L
𝑅𝑙,2𝑘−1- Decode and remodulate at 𝑅𝑘∗- Send from 𝑅𝑘∗ to 𝐷𝑘∗
𝐷𝑘∗ = arg max (𝛾𝑅𝑘∗,𝐷𝑙,2𝑘 ) , l = 1, 2,…, L

623

The outage probability of this model is expressed in (3),which was derived for the Ad-hoc routing in [5],𝛾𝐿

(1 − exp(− 2𝑡ℎ )) , 𝑚 = 1, 2, … , 𝑀 − 2
𝛿𝑚 𝛾0
ℎ𝑜𝑝
𝑃𝑜𝑢𝑡,𝑚 = [𝐼 < 𝑡ℎ] = {𝐿2𝛿 2 )𝛾(𝛿𝑀−11 − exp ( 2 𝑀2 𝑡ℎ ) ,𝑚=𝑀−1(𝛿𝑀−1 𝛿𝑀 )𝛾0(3)2
where 𝛿𝑚represents the attenuation with distance andshadowing at hop mth, m = 1, 2,…, M; 𝛾0 is the SNR averaged
over Rayleigh fading, ignoring the path loss and shadowing; 𝛾𝑡ℎrepresents the given SNR threshold; and L is the number ofrelay nodes in each hop l = 1, 2,…, L.
C. The proposed best destination relay selection (BDRS)In this paper, we propose a BDRS strategy for improving theperformance of a multi-hop relay communication system andreducing the system complexity. This strategy employs acooperative protocol–directly transmitted signal from thesource and signal from a selected relay in each cluster are
combined at a destination node–together with an efficient node
selection rule. In this section, the preliminary information of thestrategy is presented first followed by the routing strategy
Figure 4 shows a picture of the idealized linear networkmodel investigated in this study. It has one source (S), one
destination (D), and M – 1 hops where each hop has L nodes
that can function as a relay or a destination node in each cluster.
A cluster can be viewed as a combination of one source, Lrelays, and L destination nodes in every two consecutive hops.
The relationship between the index of cluster (k) and index ofhop (m) is m = 2k. A direct link is a link between S-D, and an
indirect link is a link via a cooperative relay S-R-D. The
following are the characteristics of our model: the variance ofadditive white Gaussian noise (AWGN) is N0/2 per complex
dimension; the channel impulse response in this system isRayleigh slow fading; the channel quality is checked
periodically to enable adaptive feature; all channel links are
statistically independent to each other; and a Time Division
Multiple Access (TDMA) technique was applied in signaltransmission. Specifically, TDMA was applied in each phase of
the two transmission phases of the cooperative protocol asfollows: in phase 1, S broadcasts a signal to Rl,m and Dl,m withina cluster; and in phase 2, Rl,m detects the signal from S andremodulates it before transmitting the remodulated signal toDl,m. Dl,m of a previous cluster is the source (Sl,m) of the next
cluster. The destination node (Dl,m) combines the signals from

𝐷𝑙,2𝑘

S =

𝐷𝑘∗

Decode and remodulate at 𝐷𝑘∗

End∗𝑅𝐾∗ = arg max (min (𝛾𝐷𝑀−2,𝑅𝑙,𝑀−1 , 𝛾𝑅𝑙,𝑀−1 ,𝐷0 )) , l = 1,

𝑅𝑙,𝑀−1

2,…, L∗𝐷𝐾,= 𝐷0
Output the optimal route {𝑆0 , (𝑅𝑘∗ , 𝐷𝑘∗ ), 𝐷0 }, where k = 1,
2,…, K
Fig. 5. The idealized linear network model with M hops and L relays in the
1st cluster for analyzing BDRS routing strategy.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

S and Rl,m using a maximum ratio combining (MRC) technique[11].
As for the BDRS routing strategy, we describe it by showing
an analysis of only the first cluster, depicted in Figure 4. Other
clusters can be analyzed in exactly the same way, up to the K1th cluster. The last cluster (the Kth cluster) has only one finaldestination node; therefore, our protocol does not apply, andinstead, selection of the best relay is performed according to themethod in [7]. We considered only the case of L = 3 and M = 2
for our analysis, but it can be generalized to the cases of l = 1,
2,…, L and M = 2.
Figure 5 shows a BDRS implementation in the first cluster ofour modified conventional multi-hop communication model(illustrated in Figure 1). The model’s cooperative protocol has
an advantage of evenly broadcasting a signal from a source toall nodes in a cluster, including the selected destination node of
the cluster. The proposed BDRS strategy, which is
implemented in 2 phases, can be described as follows. The first
node that transmits a signal is defined as S = 𝑆0 , and thedestination node that receives a signal is D = Dk. Any numberof k clusters, where k = 1,…, K – 1, can be between the 2 nodes.
In phase 1, a destination relay that exhibits the maximum
received SNR is selected (𝐷𝑘∗ ). 𝐷𝑘∗ is found by calculatingarg max(𝛾𝑆,𝐷𝑙,2𝑘 ), where l = 1, 2,…, L and k = 1, 2,…, K-1, and𝐷𝑙,2𝑘(C-MRC) technique presented in [12]. Note that, at high 𝛾𝑆𝑅𝑘∗ ,the node that detects the signal places full confidence on thearriving signals from the relay. At low 𝛾𝑆𝑅𝑘∗ , its confidence isweighted according to the ratio of both hops, which are the 𝑆𝑅𝑘∗
and 𝑅𝑘∗ 𝐷𝑘∗ routes. The received symbols were detected by using
the maximum likelihood (ML) technique. Then, the destination
relay (𝐷𝑘∗ ) functions as the source (Sl,2k-2) for the next
consecutive cluster. The received signal is detected and reencoded before the re-encoded signal is transmitted to a relay
in the next cluster. The received signal can be calculated fromthe following expression,
𝑐𝑜𝑜𝑝

𝑐𝑜𝑜𝑝

𝑦𝑆,𝐷∗ = √𝑃𝑆 ℎ𝑆,𝐷𝑘∗ 𝑥 + 𝑛𝑆𝐷𝑘∗ ,(4)𝑘where 𝑃𝑆 is the transmitted power from S to D, 𝑥 represents
modulated transmitted symbols, ℎ𝑆,𝐷𝑘∗ is the channel impulse
response from S to D, and 𝑛𝑆𝐷𝑘∗ is AWGN from S to D.
In phase 2, a relay 𝑅𝑘∗ is selected that exhibits the minimum
outage probability (𝑃𝑜𝑢𝑡 ), where 𝑅𝑘∗ = arg min (𝑃𝑜𝑢𝑡 ) and𝑅𝑙,2𝑘−1

𝑃𝑜𝑢𝑡 is as expressed in (19). 𝑅𝑘∗ receives a broadcast signal fromthe source, detects 𝑥̃ by using a Maximum-Likelihood (ML)technique, and decodes and re-encodes the 𝑥̃ before
transmitting it to the next destination node 𝐷𝑘∗ . The optimalroute is then the best one in the set of a-node-to-another-noderoutes {R} = {𝑆0 , (𝑅𝑘∗ , 𝐷𝑘∗ ), (𝑅𝐾∗ , 𝐷0 )}, and k = 1, 2,…, K. The
received signal from 𝑅𝑘∗ to 𝐷𝑘∗ can be expressed as follows,𝑐𝑜𝑜𝑝𝑦𝑅∗ ,𝐷∗ = √𝑃𝑅𝑘∗ ℎ𝑅𝑘∗ ,𝐷𝑘∗ 𝑥̃ + 𝑛𝑅𝑘∗ 𝐷𝑘∗ ,
𝑘

𝑘

(5)

where 𝑃𝑅𝑘∗ is the transmitted power from 𝑅𝑘∗ to 𝐷𝑘∗ , 𝑥̃ represents
the decoded transmitted symbols, ℎ𝑅𝑘∗,𝐷𝑘∗ represents the channel
impulse response from the selected 𝑅𝑘∗ to 𝐷𝑘∗ in cluster kth,𝑛𝑅𝑘∗ 𝐷𝑘∗ represents the Additive White Gaussian Noise (AWGN)
from the selected 𝑅𝑘∗ to 𝐷𝑘∗ .
The destination relay (𝐷𝑘∗ ) combines the received signals
from S and 𝑅𝑘∗ using a cooperative maximum ratio combining∗

𝑦C−MRC = (√𝑃𝑆 ℎ𝑆𝐷∗ 𝑦𝑆𝐷∗

𝑘

𝑘

𝑛 𝐷∗

)

𝑘

+(

𝐦𝐢𝐧(𝛾𝑆𝑅∗ ,𝛾𝑅∗ 𝐷∗ )𝑘𝛾 𝑅 ∗ 𝐷∗

𝑘 𝑘

𝑘 𝑘

∗

)(

√𝑃𝑅 ℎ𝑅∗ 𝐷∗ 𝑦𝑅∗ 𝐷∗
𝑘 𝑘

𝑛 𝐷∗

𝑘 𝑘

)

. (6)

𝑘

For the last cluster, a cooperative relay 𝑅𝐾∗ is selected suchthat it exhibits the minimum outage probability(𝑅𝐾∗ = arg min (𝑃𝑜𝑢𝑡 )). Our proposed protocol is summarized
𝑅𝑙,2𝐾−1

in Table 2 as follows.
TABLE IIROUTING STRATEGY FOR THE PROPOSED BDRS

2𝛾𝑆,𝐷𝑙,2𝑘 (calculated from 𝑃𝑆 |ℎ𝑆,𝐷𝑙,2𝑘 | /𝑁0 ) represents the SNRfrom S to Dl,2k , where l = 1, 2,…, L. The expression for thereceived signal is624

Let l = 1, 2,…, L be the index of relay nodes; m = 1, 2,…,
M be the index of communication hops; and k = 1, 2,…, K
be the index of clusters in a communication network. Let S
denotes the source node; 𝑅𝑘∗ denotes the selected relay nodein the kth cluster; and 𝐷𝑘∗ denote the selected destination
node in the kth cluster; and note that a hop index m = 2k.
Initialization: S = 𝑆0
Recursion:
For k = 1 to K-1
𝐷𝑘∗ = arg max(𝛾𝑆,𝐷𝑙,2𝑘 ) , l = 1, 2,..., L
𝐷𝑙,2𝑘

𝑅𝑘∗ = arg min (𝑃𝑜𝑢𝑡 )𝑅𝑙,2𝑘−1(𝑃𝑜𝑢𝑡 expression is in (19))- Decode at 𝑅𝑘∗ and remodulate- Send from 𝑅𝑘∗ to 𝐷𝑘∗- Combine signals by using C-MRC representedby (6)- Decode at 𝑅𝑘∗ and remodulate at 𝐷𝑘∗
S = 𝐷𝑘∗End
𝐷𝐾∗ = 𝐷0
𝑅𝐾∗ = arg min (𝑃𝑜𝑢𝑡 )(𝑃𝑜𝑢𝑡 is expressed in (19))
𝑅𝑙,𝑀−1∗
Output the optimal route {𝑆0 , (𝑅𝑘∗ , 𝐷𝑘∗ ), (𝑅𝑀−1, 𝐷0 )}, k = 1,
2,…, K

In order to implement a cooperative protocol in a multi-hop
relay system, we have to find two optimum nodes, i.e., one isthe cooperative relay node and the other one is the destination
node. We found that the proposed BDRS can reduce thecomputational complexity by searching only L times comparedto the heuristic strategy that searches all nodes, as illustrated in
Figure 6 (a) and (b). In the latter strategy, the source has to

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

search for the best relay node and the best destination node fromall nodes in order to minimize the outage probability. In
contrast, the proposed BDRS reduces the system complexity
from an exponential function of Lm to a linear function of L,
where L is the number of relays in each hop and m is the numberof hops. In Figure 6, m = 2; the cluster consists of two hops. The
proposed BDRS can reduce the complexity of clusters k =1 to k
= K-1. The complexity of the last cluster (k = K) is, however,the same because there is only one final destination.

625

mutual information between S and D for the proposed BDRS
protocol can be written as
1

𝐼𝐷𝐹 = 𝑙𝑜𝑔2 (1 + max (𝛾𝑆𝐷𝑙,2𝑘 ) + 𝛾𝑅𝑙,2𝑘−1𝐷𝑘∗ ),2

𝑙=1,…,𝐿l = 1,2,…,L and k = 1,2,…,K(8)

where 𝛾𝑆𝐷𝑙,2𝑘 = 𝑃𝑆 |ℎ𝑆𝐷𝑙,2𝑘 | 2 /𝑁0 is the instantaneous SNR
between S and 𝐷𝑙,2𝑘 at hop m = 2k and 𝛾𝑅𝑙,2𝑘−1 𝐷𝑘∗ =𝑃𝑅 |ℎ𝑅𝑙,2𝑘−1𝐷𝑘∗ | 2 /𝑁0 is the instantaneous SNR between 𝑅𝑙,2𝑘−1
and 𝐷𝑘∗ at the cluster kth. In fact, the mutual information in (8)can be determined by the BDRS selection principle that selects
the destination node with the maximum received SNR first thenuses the node to retransmit the signal. Based on the principle of
C-MRC signal combining [12], the total received SNR(SNRtotal) is the summation of the received SNR from both thedirect and indirect links, i.e., SNRtotal = max (𝛾𝑆𝐷𝑙,2 ) +𝑙=1,2,3

𝛾𝑅𝑙,2𝑘−1𝐷𝑘∗ . From a general principle together with theexpression for mutual information in (8), a general form of theoutage probability can be expressed as follows,
𝑃𝑜𝑢𝑡 = Pr(𝐼𝐷𝐹 ≤ 𝑡ℎ)(a) Heuristic Searching Strategy. (b) The proposed BDRS searching strategy.

= Pr ( max (𝛾𝑆𝐷𝑙,2𝑘 ) + 𝛾𝑅𝑙,2𝑘−1𝐷𝑘∗ ≤ 22𝑡ℎ − 1 )
𝑙=1,…,𝐿

Fig. 6. Comparison of complexity for two different searching strategies.

III. PERFORMANCE ANALYSIS
In this section, we derive a closed-form expression of theoutage probability for our BDRS protocol. The number of hops(M) was set to be 2, the number of cooperative relays (L) to be
1, and the number of destination relays (L) to be 3. We call thisconfiguration 1R3D. We picked 1R3D as the best configuration
to show the derivation of our protocol because a configurationof 3 Ds shows sufficiently the complexity of the derivation in a
manageable length. Furthermore, any configuration with ahigher number of Ds can be derived by using the same principle.
We only consider the outage probability of a system with one
relay for the reason that the best relay of a system with more
relays can be selected in the same way by applying thisexpression to each relay, one-by-one, and choosing the relay
with the minimum outage probability among all of the relays.
Our derivation starts by, first, finding the mutual information
between S and 𝑅𝑙,2𝑘−1 , as shown below,
1

𝐼𝑆𝑅𝑙,2𝑘−1 = 𝑙𝑜𝑔2 (1 + 𝛾𝑆𝑅𝑙,2𝑘−1 ), l = 1,2,…,L and k = 1,2,…,K
2(7)
where 𝛾𝑆𝑅𝑙,2𝑘−1 = 𝑃𝑆 |ℎ𝑆𝑅𝑙,2𝑘−1 | 2 /𝑁0 is the instantaneous SNR
between S and 𝑅𝑙,2𝑘−1 and ℎ𝑆𝑅𝑙,2𝑘−1 is the channel impulse
response between S and 𝑅𝑙,2𝑘−1 . If 𝐼𝑆𝑅𝑙,2𝑘−1 is higher than agiven threshold (th) specified by the system designer according
to the type of application, the relay 𝑅𝑙,2𝑘−1 decodes andretransmits the signal to a selected destination relay (𝐷𝑘∗ ). The
reason why we have a factor of 1/2 in (7) is because we use twotime slots (or orthogonal channels) for data transmission. The(9)where l = 1,2,…,L and k = 1,2,…,K.
For the purpose of making the derivation clear and easy tounderstand, only the first cluster of our model is considered. We
present the derivation of the outage probability (𝑃𝑜𝑢𝑡 ) in steps
as follows: 1) determine the distribution of the total received
SNR (SNRtotal) by using a moment generating function (MGF).
Since MGF of SNRtotal is equal to the product of MGF∗ , we have to determineof max (𝛾𝑆𝐷𝑙,2𝑘 ) and MGF of 𝛾𝑅2𝑘−1 𝐷𝑙,2𝑘
𝑙=1,…,𝐿each MGF separately and derive the probability density
function (PDF) by applying an inverse Laplace transform to the
MGF of SNRtotal; 2) determine 𝑃𝑜𝑢𝑡 by calculating a cumulative
distribution function (CDF) with respect to the outage threshold(th) which can be found directly by integrating its PDF from theprevious step.
In order to simplify the analysis in step 1, we introduce a new
random variable 𝑍 which is a sum of two random variables, 𝑍 =𝜒 + 𝛾𝑅1,1𝐷2∗ where 𝜒 = max (𝛾𝑆𝐷𝑙,2 ). Now, the outage𝑙𝜖{1,2,3}

probability can be written as 𝑃𝑜𝑢𝑡 = Pr(𝑍 ≤ 22th − 1). Therefore,
the CDF of the received signal can be obtained directly fromthe PDF of 𝑍 evaluated at 22th – 1 since the two randomvariables 𝜒 and 𝛾𝑅1,1𝐷2∗ are statistically independent, and the
PDF of 𝑍 is just the convolution of the PDFs of 𝜒 and 𝛾𝑅1,1𝐷2∗(its calculation method reported in [13]). The PDF of 𝜒 can befound from the CDF of 𝜒 as follows,𝐹𝜒 (𝑥) = Pr (max(𝛾𝑆𝐷𝑙,2 ) ≤ 𝑥)𝑙𝜖3

= ∏3𝑙=1 Pr(𝛾𝑆𝐷𝑙,2 ≤ 𝑥) = ∏3𝑙=1 𝐹𝛾𝑆𝐷 (𝑥)
𝑙,2

(10)

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015626


  SD   SD2,2     SD   SD3,2  exp   1,2
exp   1,2
x 
x
  SD  SD     SD  SD  
3

x  
1,2
2,2
1,2
2,2




    SD1,2  SD2,2
f  ( x)   exp      SD1,2  SD3,2








l 1
SD1,2 SD2,2
SD1,2 SD3,2
  SDl ,2  

 


 











  SD   SD3,2     SD  SD   SD1,2  SD3,2  SD1,2  SD3,2  exp   2,2
exp   2,2 3,2
x 
x
  SD  SD     SD1,2  SD2,2  SD3,2

2,2
3,2




   SD2,2  SD3,2
    SD1,2  SD2,2  SD3,2   SD2,2  SD3,2






SD2,2 SD3,2
SD1,2 SD2,2 SD3,2

 


 


 














(11)




1
1

  
()
SD1,2  SD2,23 SDl ,2     SD1,2  SD2,2 
M ( f  ( x))   exp   1  



l 1
1
s 
 
 ( SD1,2  SD2,2 )   s 
 
SD

l
,2


    SD1,2  SD2,2



 


 
  



1
1
 
 ( SD2,2  SD3,2 )   
 ( SD1,2  SD3,2 )    SD2,2  SD3,2     SD1,2  SD3,2 



 

 




 
  
11
 ( SD2,2  SD3,2 )   s    
 ( SD1,2  SD3,2 )   s       SD2,2  SD3,2      SD1,2  SD3,2 




 










  


  SD2,2  SD3,2    SD1,2 SD2,2  SD3,2
 
 
 



SD1,2 SD2,2 SD3,2
 
 .


   SD1,2  SD2,2  SD3,2   SD2,2  SD3,2 

s


 SD1,2  SD2,2  SD3,2








𝑓𝛾𝑅

∗
1,1 𝐷2







∗ |𝑅
is off
1,1 𝐷2 1,1(𝑥) Pr(𝑅 is off) + 𝑓𝛾𝑅(𝑥) = 𝑓𝛾𝑅

(12)

∗ |𝑅
is on
1,1 𝐷2 1,1(𝑥) Pr(𝑅 is on)

22𝑡ℎ −1

𝐴 = Pr(𝐼 ≤ 𝑡ℎ) = Pr(𝛾𝑆𝑅1,1 ≤ 22𝑡ℎ − 1) = 1 − exp(− 𝛾
𝑓𝛾𝑅

∗ |𝑅
is on
1,1 𝐷2 1,1

𝑓𝛾𝑅∗
1,1 𝐷2

(𝑥) =

1
̅𝑅
𝛾

∗
1,1 𝐷2(𝑥) = 𝐴𝛿(𝑥) + (1 − 𝐴)

𝑀(𝑓𝛾𝑅

∗
1,1 𝐷2exp (− ̅

𝑥

𝛾𝑅

1
̅𝑅
𝛾

∗
1,1 𝐷2∗
1,1 𝐷2

𝑆𝑅1,1), 𝑥 ≥ 0

exp (− ̅𝑥𝛾𝑅(𝑥)) = 𝐴𝛿(𝑥) + (1 − 𝐴) (𝛾̅∗1,1 𝐷2) , 𝑥 ≥ 0.

̅𝑅𝛾∗
1,1 𝐷2∗− 𝑆
𝑅1,1 𝐷2

)

)

(13)

(14)

(15)

(16)

(17)

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015





627






x 
1 
x   
1  exp  
1  exp  
 
 
  R D   R D   SD    l ,2 
1,1 l ,2  
1,1 l ,2 l ,2   






x 



x   3  exp     F  x   A 1  exp   SR   1
  SD      1
l 1 
1   SDl ,2  l 1 

R D
  SD
3

1,1



l ,2

1,1

l ,2


x 
exp     SR   SD1,2   SD2,2    SD   SD2,2     SD2,2  
x  
1 
1,1  SD1,2
1  exp   x 1,2
  1  exp   A 1  exp   x  SD  SD    R D*    R D*  SD1,2  SD2,2     SD1,2   SD2,2  SD1,2  SD2,2   
1
1,2
2,2




1,1 2  
1,1 2 

 





  SD  SD   *
R
D
1,2
2,2


1,1 2

x 
exp     SR   SD1,2   SD3,2    SD   SD3,2     SD3,2  
x  
1 
1,1  SD1,21  exp   x 1,2
  1  exp   A 1  exp   x    SD   SD    SD  SD  








* 
* 
SD
SD
SD
SD
1
R1,1 D2 
1,2
3,2
1,2
3,2
1,2
3,2

   1,2


 
3,2

 R1,1 D2    SD  SD   *
1,2
3,2

 R1,1 D2



 


  SD  SD   SD   SD  SD
1,2
2,2
3,2
2,2
3,2
 A 1  exp   x SD1,2  SD2,2  SD3,2





 

 
SD1,2  SD2,2   SD3,2   SD2,2  SD3,2
  SD1,2  SD2,2  SD3,2



x 
exp    SR 
1,1       SD1,2  SD2,2   SD3,2   SD2,2  SD3,2
 SD1,2  SD2,2  SD3,2
     1  exp   x    1 1  exp   x  
SD1,2




  R D*    R D* 
1,1 2  
1,1 2



  R D*
1,1 2


 



  SD3,2   SD2,2  SD3,2      
 SD1,2  SD2,2  SD3,2
  

SD2,2   1(18)

 (22th  1)   3 
 (22th  1)  coop   1  exp  Pout 1  exp   SR1,1   l 1 
 SDl ,2  



 




2 th


 exp   (2  1) 





2 th
2 th




3 





SR1,1


  1 1  exp   (2  1)    1 1  exp   (2  1)       R D*    R D*  SDl ,2       SDl ,2 l 1  
1
1

1,1 2
1,1 2 











  R1,1 Dl ,2  R D* 
1,1 2 
 (22th  1)  
 (22th  1)( SD1,2  SD2,2 )    1  exp    1  exp  

 

 




SR
SD
SD
1,1
1,2
2,2

 

 



 (22th  1)  
 (22th  1)( SD1,2  SD3,2 )    1  exp    1  exp  

 






SR
SD
SD
1,1
1,2
3,2

 

   (22th  1)  
 (22th  1)( SD2,2  SD3,2 )    1  exp    1  exp    SR1,1   SD2,2  SD3,2



 




 (22th  1) 
exp  
 

2 th






 

SR
1,1

   SD1,2   SD2,2  1  exp   (2  1)    1 1  exp  (22th  1)   SD1,2   SD2,2    


 

  R D*    R D*   SD   SD2,2 
1   SD1,2  SD2,2    SD1,2  SD2,2    
1,1 2
1,1 2 



 1,2

  SD  SD   *R1,1 D2
1,2
2,2



Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015628

 (22th  1) 
exp  
 (22th  1)   SR1,1    SD1,2   SD3,2    SD3,2       1 1  exp  (22th  1)  SD1,2
 1  exp    
  SD  SD      R D*    R D*   SD1,2   SD3,2 
1   SD1,2  SD3,2  
1,2
3,2

  
1,1 2
1,1 2   SD  SD   *R1,1 D2
1,2
3,2



 (22th  1) 
exp    (22th  1)    SD2,2   SD3,2    SD2,2   SD3,2    
SR
1 
1,1


2 th






 1  exp   1  exp  (2  1) 






 











  SD2,2   SD3,2 **
1  SD2,2 SD3,2  
R1,1 D2
R1,1 D2  SD2,2 SD3,2    





  SD  SD   *R1,1 D2
2,2
3, 2


 (22th  1) 
exp  
 (22th  1)  3
 SR1,1   3 1 
1 
1   .

2 th
















1
exp
1
exp
(2
1)

3
1
1 

 

  R D*    R D* l 1  SDl ,2

 
1,1 2
1,1 2  l 1     l 1  SDl ,2 
R1,1 D2*

SDl ,2where 𝐹𝛾𝑆𝐷 (𝑥) is the CDF of 𝛾𝑆𝐷𝑙,2 and 𝐹𝛾𝑆𝐷 (𝑥) = 1 −
exp(̅

−x

𝛾𝑆𝐷𝑙,2𝑙,2𝑙,2) . By using the expression for the CDF of 𝛾𝑆𝐷𝑙,2 theexpression for the PDF of 𝜒 can be found by taking thederivative of (10) with respect to 𝑥, and after somemanipulations, f  ( x) can be written as (11). To determine the∗ , we obtain it from the PDF of 𝛾𝑅 𝐷∗ ,MGF of 𝛾𝑅2𝑘−1𝐷𝑙,2𝑘
1,1 2
expressed in (12).
The operation of the relay can be explained as follows. WhenI is equal or less than th, the received SNR at the destination
sent by 𝑅1,1 is 0 since there is no signal retransmission from
𝑅1,1 . Therefore, the conditional PDF 𝑓𝛾𝑅 𝐷∗ |𝑅 is off (𝑥) can be

expressed as 𝑓𝛾𝑅

1,1 2∗ |𝑅 is off
1,1 𝐷2(𝑥) = 𝛿(𝑥) (i.e., when the mutual

information of SR is less than th, 𝐼 ≤ Ith). We define an outage
probability for when 𝑅1,1 is off as A, expressed in (14) where
𝛾𝑆𝑅

1,1

= 𝐄(

(19)

𝑃𝑆 |ℎ𝑆𝑅1,1 |𝑁0

2) stands for the average SNR between 𝑆

and 𝑅1,1 and E(∙) is a statistical expectation operator. When 𝐼 >Ith, the outage probability that 𝑅1,1 is on is (1 − 𝐴) and theconditional PDF 𝑓𝛾𝑅 𝐷∗ |𝑅1,1 is on (𝑥) can be written as (15)where 𝛾𝑅∗
1,1 𝐷2= 𝐄(

𝑃𝑅 |ℎ𝑅∗|
1,1 𝐷2𝑁0

2) stands for the average SNR

between 𝑅1,1 and 𝐷2∗ . Therefore, the PDF of 𝛾𝑅1,1𝐷2∗ can be
written as shown in (16). The MGF of 𝑓𝛾𝑅 𝐷∗ (𝑥) can be
1,1 2

obtained by transforming (16) from time domain to frequency
domain, expressed in (17). Multiplying (12) with (17) and theninverse Laplace transforming the multiplication product to time
domain, we obtain expression (18) for the CDF of Z.
Lastly, in order to find 𝑃𝑜𝑢𝑡 in step 2, we integrate directly the
CDF expression in (18) with respect to the given threshold (th).
Hence, in general, 𝑃𝑜𝑢𝑡 can be expressed as 𝑃𝑜𝑢𝑡 = Pr(𝑍 ≤22𝑡ℎ − 1) = 𝐹𝑍 (22𝑡ℎ − 1). For the 1R3D configuration, 𝑃𝑜𝑢𝑡 ofa cluster becomes (19). The derivation of (19) is shown in the
Appendix.
For comparison, we also derive the outage probability for
1R1D and 1R2D systems in the same manner. In principle, ourderivation procedure can be applied to a system of any numbersof relays and destinations.

1,1 2

The outage probability for the 1R1D configuration is as follows, 22th  1 
exp    SR   1
 22th  1   
 22th  1  
 22th  1 
 22th  1  
1
1,1


coop

  1  exp  
 


Pout  1  exp  
exp  exp  









  SR1,1     SD1,2    1  1    SD1,2
  R1,1 D1,2   R1,1 D1,2
  SD1,2  
  SD 1,2  RD1,2 (20)

The outage probabilities for the 1R2D is as follows,

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015629

 22th  1 
exp  2 th
  SR   1
 22th  1 
 22th  1   2

 2 


 2th


2
1
1,1


coop
  1 exp   2  1       1  exp   1  exp  
Pout
exp  
  SD    SD   l 1  1
  R D*   R D *
l ,21    SDl ,2
  SR1,1   l 1 


l ,2
1,1 2 
1,1 2





  SDl ,2  R D* 
1,1 2 2  22th  1   
1    1  exp    22th  1    1  exp    SR     SD   



l
1
1,1

 
 l ,2    22th  1  
2 
1  
  22th  1  
exp    l 1 
  SR   2  1    22th  1  


SD
1
1,1


 l ,2   1  exp  
exp  
 













*
*
SD2,2
2

1
1  l 1  SDl ,2  


R
D
R
D
1,1
1,1
2
2


 l 1     *  R1,1 D2  SDl ,2  Fig. 7. Simulated and calculated performances of BDRS (first cluster, fixedsource, 1 relay node, and 1-3 destination nodes).

Fig. 8. Simulated performance of BDRS with various numbers of relay anddestination nodes (first cluster, fixed source, 1-3 relays, and 1-3 destinations).

IV. SIMULATION RESULTS
Based on computer simulation with MATLAB software, theperformance of the BDRS system was determined andcompared with that from the closed-form expression (19).
Simulation settings were as follows: binary phase shift keying(21)(BPSK) modulation was used with a total transmitted power of
1 Watt; noise variance was 𝑁0; and bandwidth efficiency was 1
bit/s/Hz. In addition, Jake’s model [14] was employed for
simulating Rayleigh fading channels with normalized Doppler
shift of 5,000 Hz, and all channel link variances in the systemwere assumed to be dependent on the distance between nodes
in an idealized linear system.
Figure 7 shows the simulated and calculated performances ofthe first cluster of 3 different configurations of a system: 1R2D,
1R2D, and 1R3D. It can be seen that the performances werevery close. Note that the BDRS protocol reduced the systemcomplexity from L2 to L, compared to that of searching for thebest case of all cases in the same cluster in a non-augmented
cooperative protocol.
Figure 8 depicts the performances 4 configurations of asystem: 1R1D, 1R2D, 1R3D, 2R2D, and 3R3D. The
performance curves show that when the number of destination
nodes was fixed, more relays resulted in more coding gain.
Moreover, the curves in this figure and in Figure7 indicate thatthe diversity gain was dependent on the number of destination
nodes, and the coding gain was dependent on the number ofcooperative relay nodes.
Figure 9 shows the simulated performances of the proposed
BDRS strategy versus the modified conventional multi-hop
strategy for 3 clusters, as an extension for the cases of only 1cluster above. The result shows that the proposed strategy
outperformed the modified conventional multi-hop
communication strategy dramatically–the SNR differences for
1R1D, 2R2D, 3R3D, 4R4D, and 5R5D configurations were 5,
9, 12, 13 and 13 dB, respectively, at 𝑃𝑜𝑢𝑡 of 10−2. In addition, at
a high SNR regime, as the number of destination relays
increased, the proposed protocol achieved more diversity gain.
Figure 10 shows simulated performances for 3 clusters as thesource was fixed or moving. When the source moved, channel
variances were changed from the distance variation. BDRS will
adaptively search for the new best destination, and then the newbest relay with minimum outage probability. The software was
set to simulate the source moving randomly up and down
vertically in every 10,000 bits transmission. It can be seen that

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

630

moving source deteriorate the performances marginally, butwould not be as much as that of a non-adaptive protocol.

V. PERFORMANCE IMPROVEMENT
This section discusses the general concept of power
allocation, implementation issue, and the results. In principle,
C-MRC alone cannot guarantee maximum total SNR at thedestination, but by varying power allocation according tochannel quality, system performance should be improved. The
objective of power allocation is to selectively allocate certaintransmitted power to source (𝑃S) and relay (𝑃R,) such that the
total transmitted power (𝑃T) is fixed, in order to minimize Pout.
We can write the power allocation condition as follows,
Fig. 9 Simulated performances for three clusters of the proposed BDRS,
conventional multi-hop, and modified conventional multi-hop.

𝑃𝑇 = 𝜆𝑃𝑆 + (1 − 𝜆)𝑃𝑅

at 0 < 𝜆 < 1,(22)

where 𝜆 is the power allocation factor between 𝑃𝑆 and 𝑃𝑅 .
In this study, the optimum power allocation was determinedby replacing 𝑃𝑅 with (1 − 𝜆)𝑃𝑆 and 𝑃S with 𝜆𝑃𝑆 in theexpression for outage probability (19) and then heuristicallyvarying 𝜆 from 0 to 1 until the minimum outage probability was
found. Graphs of 𝑃𝑜𝑢𝑡 as a function of 𝜆 at SNR = 10dB, 20dB,
and 30dB were plotted in Figure 11. To find the minimum
outage probability  min( Pout )  from the curves, normally wetake a derivative of (19) with respect to 𝑃𝑆 and set it equal tozero then algebraically solve the equation. However, thisexpression is very difficult to solve in closed-form, so wedetermined the minimum outage probability by finding thezero-crossing as follows,
coop
dPout
( )
0
d

Fig.10 Simulated performances for 3 clusters with the source fixed or
moving.

Fig. 11. Pout of BDRS (1R3D) with fixed SNR at various source transmitting
powers at SNR =10, 20, and 30 dB.

(23)

In detail, Figure 12 shows the optimum power allocation in
the first cluster of 1R3D configuration. The crossing point at xaxis is the source power that yields the minimum outage
probability. This figure shows how much power of a total of 1
Watt should be allocated between the source and the relay when
the channel quality of each link changes at a fixed SNR = 20
dB. The results indicate that more power—0.83, 0.93, and 0.81
Watt—should be allocated to the source when the qualities of
the SD link, SR link, and RD link, respectively, were set to bepoor one at a time. The rest of the power (𝑃𝑅 ) should beallocated to the cooperative relay.
In Figure 13, the effects of channel variances (𝛿 2 ) on power
allocation are shown in plots of 𝑃𝑜𝑢𝑡 as a function of λ at SNR= 10 dB (Figure 11a), SNR = 20 dB (Figure 11b), and SNR =22
230 dB (Figure 11c) where (𝛿𝑆𝐷
= 0.1, 𝛿𝑆𝑅= 1, 𝛿𝑅𝐷
= 1),
2
2
2
2
2
2
(𝛿𝑆𝐷 = 1, 𝛿𝑆𝑅 = 0.1, 𝛿𝑅𝐷 = 1), and (𝛿𝑆𝐷 = 1, 𝛿𝑆𝑅 = 1, 𝛿𝑅𝐷=0.1) for each SNR case. The results show that the optimum
power allocation was always with λ more than 0.5 in all cases.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

VI. IMPLEMENTATION ISSUESIn this section, we discuss the following: cases when central
control is available and when control is unavailable, error
detection, channel estimation when source is moving, collision
problem, and an issue regarding odd hops.
Following the BDRS strategy, the best destination for eachcluster is selected first based on received SNR, or equivalently,
measured channel gain. Selection can be implemented in eithera centralized or distributed way. If a central controller isavailable (such as the cases of base station in a cellular networkor an access point in a mesh network), it can collect all channel
state information (CSI) and then assign a transmission. In thecase where a central controller is not available, BDRS strategy
can also be implemented in a distributed way by using a
distributed relay selection algorithm proposed in [15] where
each relay sets a timer based on its measured channel gain. The
larger the channel gain is, the shorter the receiving time is. In
this way, the timer of the destination with the best channel, i.e,,the best destination selected, expires first. That destination thensends a flag signal. All other destinations, while waiting fortheir timer to count down to zero, are in listening mode. As soon
as they hear the flag signal, they back off. This method requiresthat every destination can hear all the others. For error

631

detection, cyclic redundancy check (CRC), commonly used inwireless communication systems, is applied to the selected
node. For estimation of channel impulse response when thesource is moving, a short-term statistical technique reported in[16] that levels out short-term changes can be employed. To
deal with possible hidden-node collisions, each relay canrandomly stop transmitting then wait and see if other relays are
re-transmitting. Finally, it may seem that the proposed BDRS islimited to systems consisting of even hops, however, that is not
the case. When a system consists of odd hops, the BDRS
strategy works by selecting the best relay at hop M-1, from theneighboring nodes in the same hop, which will then cooperatewith the source to retransmit the signal to the final destination.
VII. CONCLUSION
In this paper, we propose an adaptive cooperative protocol
for multi-hop wireless network with a ‘best destination relay
selection’ (BDRS) routing protocol. The proposed routing
protocol was designed to achieve an optimal route while
reducing system complexity. To assess the performance of the
proposed protocol, its operations in a system with different
numbers of hops and clusters were simulated and their results
were compared to those of the conventional multi-hop andmodified conventional multi-hop protocols. It was found thatour proposed protocol outperformed those protocols by several
folds–it achieved a 15 dB and 14 dB received SNR
improvement gains, respectively, at an outage probability ofless than 10-2. Moreover, we found that the simulated results
agreed closely with the calculated results from the exact,
closed-form mathematical expression of the outage probabilityof our proposed protocol. Lastly, we used an optimum power
allocation scheme to further improve the system performanceand found that its operation supported our reasoning that thedirect source to destination link is the most important link.
APPENDIX
A. Derivation for the 1R1D configuration

Fig. 12. Optimum power for the first cluster, found by searching for zerocrossing of (23)Let 𝑢 be the CDF of the received SNR of a direct link and 𝑣
is the CDF of the received SNR of an indirect link as follows,𝑢 = 1−𝑒

𝑥
𝛾𝑆𝐷

−̅

1,2

,

(25)

𝑣 = 𝐴𝛿(𝑥) + (1 − 𝐴) (1 − 𝑒𝑥𝛾𝑅1,1 𝐷
1,2

−̅

).

(26)

The PDF of 𝑢 and 𝑣 can be determined by differentiating theirCDFs,
𝑓(𝑢) =ddx(1 − 𝑒

𝑥
𝛾𝑆𝐷
1,2

−̅

)=

𝑒

𝑥
−𝛾
̅ 𝑆𝐷

1,2

̅𝑆𝐷1,2𝛾,(27)

Fig. 13. Pout of BDRS (1R3D) at various transmitting powers, SNRs, andchannel variances.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015𝑓(𝑣) =

𝑑
𝑑𝑥

𝑒

𝑥
−𝛾
̅𝑅 𝐷
1,1 1,2(𝐴𝛿(𝑥) + (1 − 𝐴) ̅

𝛾𝑅1,1 𝐷1,2

𝑒

𝑥
−𝛾
̅𝑅 𝐷
1,1 1,2) = 𝐴𝛿(𝑥) + (1 − 𝐴) ̅

𝛾𝑅1,1 𝐷1,2

632

.

(28)

Transforming 𝑢 and 𝑣 to the frequency domain by using the moment generating function (M), expressed as

𝑀(𝑢) =

1
̅ 𝑆𝐷
𝛾
1,2
1
̅ 𝑆𝐷
𝛾
1,2

𝑀(𝑣) = 𝐴 + (1 − 𝐴) (

−𝑠

,

(29)

1
̅𝑅 𝐷
𝛾
1,1 1,2
1
̅𝑅 𝐷𝛾
1,1 1,2

−𝑠

).

(30)

The PDF of the total received SNR can be found by multiplying 𝑀(𝑢) with 𝑀(𝑣), expressed asf   x  = 𝑀(𝑢)x𝑀(𝑣) = 𝐴 (

1
̅ 𝑆𝐷
𝛾
1,2
1
̅ 𝑆𝐷
𝛾
1,2

−𝑠

1
̅𝑅 𝐷
𝛾
1,1 1,2
1) + (1 − 𝐴) (̅𝑅 𝐷𝛾
1,1 1,2

−𝑠

)(

1
̅ 𝑆𝐷
𝛾
𝑙,2
1
̅ 𝑆𝐷
𝛾
𝑙,2

−𝑠

).

(31)

Using partial fraction expansion to expand the multiplication product term, we obtainf   x  = 𝑀(𝑢)x𝑀(𝑣) = 𝐴 (

1
̅ 𝑆𝐷
𝛾
1,2
1
̅ 𝑆𝐷
𝛾
1,2

−𝑠

)+

1−𝐴
1
1
−̅
̅ 𝑆𝐷
𝛾
𝛾𝑅1,1 𝐷1,2
1,2

[(

(𝛾
̅

1
𝑆𝐷1,2

)(𝛾
̅

1

𝑅1,1 𝐷1,2

1
−𝑠
̅𝑅 𝐷
𝛾
1,1 1,2

)

)−(

(𝛾
̅

1

𝑆𝐷1,2

)(𝛾
̅

1

𝑅1,1 𝐷1,2

1
)−𝑠
𝛾𝑆𝐷
1,2

)

)].

Determining the CDF by transforming it to the time domain by applying an inverse Laplace transform to F

𝑥
−𝛾
̅ 𝑆𝐷
1,2

F  x  = 𝐴 ( 𝛾̅
𝑒

𝑆𝐷1,2

)+

1−𝐴
1
1
−̅
̅ 𝑆𝐷
𝛾
𝛾𝑅1,1 𝐷1,2
1,2

coop

[(

𝑒

𝑥
−𝛾
̅𝑅 𝐷
1,1 1,2)−(

̅𝑆𝐷1,2 )(𝛾̅𝑅1,1 𝐷1,2 )(𝛾Then, we can calculate the outage probability ( Pout ) by integrating F

(32)

(̅

𝑒

𝑥
−𝛾
̅ 𝑆𝐷
1,2

 x  , we get)].

̅𝑆𝐷1,2 )(𝛾̅𝑅1,1 𝐷1,2 )
(𝛾

(33)

 x  with respect to the given threshold (th), 22th  1 
exp    SR   1
 22th  1   
 22th  1  
 22th  1 
 22th  1   .
1
1,1


coop
  1  exp  
 


Pout  1  exp  
exp  exp    SR     SD    1
 R D  R D
  SD  




SD
1
1,1
1,2
1,2
1,1 1,2 
1,1 1,2
1,2

 

 





  SD


RD
1,2 
 1,2

(34)

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

633

B. Derivation for the 1R2D configuration
The CDF of the received SNR of a direct link (𝑢) and the CDF of the received SNR of an indirect link (𝑣) in the case of 1R2D are
as follows,𝑢 = (1 − 𝑒

𝑥
𝛾𝑆𝐷

−̅

1,2

) (1 − 𝑒

𝑥
𝛾𝑆𝐷
2,2

−̅

𝑣 = 𝐴𝛿(𝑥) + (1 − 𝐴) (1 − 𝑒

),

𝑥
𝛾𝑅 𝐷∗
1,1 2

−̅

(35)

).

(36)

Using the same method in the derivation of the 1R1D configuration, we get the following expression for the outage probability, 22th  1 
exp    SR   1
 22th  1 
 22th  1   2
 22th  1   2 
 2th

1,1


coop
  1 exp   2  1       1  exp  Pout  1  exp  
exp  
  SD    SD   l 1  1
  R D*   R D *
l ,21    SDl ,2
  SR1,1   l 1 


l ,2
1,1 2 
1,1 2





  SDl ,2  R D* 
1,1 2 2  22th  1   
1    1  exp    22th  1    1  exp  









l 1
SR1,1   SDl ,2   
 22th  1  
2 
1  
  22th  1  
exp    l 1 
  SR   2  1   22th  1  


SD
1
1,1


 l ,2    .
  1  exp  
exp     R D*    R D*
 SD2,22 
1 
1  l 1   SDl ,2  


1,1 2
1,1 2


 l 1     *  R1,1 D2  SDl ,2 

 


(37)

C. Derivation for the 1R3D configuration
The CDF of the received SNR of a direct link (𝑢) and the CDF of the received SNR of an indirect link (𝑣) in the case of 1R3D are
as follows,𝑢 = (1 − 𝑒

𝑥
𝛾𝑆𝐷

−̅

1,2

) (1 − 𝑒

𝑥
𝛾𝑆𝐷
2,2

−̅

𝑣 = 𝐴𝛿(𝑥) + (1 − 𝐴) (1 − 𝑒) (1 − 𝑒

𝑥
𝛾𝑅 𝐷∗
1,1 2

−̅

𝑥
𝛾𝑆𝐷

−̅

).

3,2

),

(38)

(39)

Using the same method in the derivation of the 1R1D configuration, we get the following expression for the outage probability, (22th  1)   3 
 (22th  1)  coop   1  exp  Pout 1  exp   SR1,1   l 1 
 SDl ,2  



 


Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015

634



2 th


 exp   (2  1) 



2 th
2 th

 1 


3 





SR
(2

1)
1
(2

1)


1,1


1  exp   1  exp       
  SD 







*
* 
l 1  SD
R1,1 D2
R1,1 D2 l ,21
1   l ,2 

 









R1,1 D2*   R1,1 Dl ,2
 (22th  1)  
 (22th  1)( SD1,2  SD2,2 )    1  exp    1  exp  

 

 




SR
SD
SD
1,1
1,2
2,2

 

 



 (22th  1)  
 (22th  1)( SD1,2  SD3,2 )    1  exp    1  exp    SR1,1   SD1,2  SD3,2









 (22th  1)  
 (22th  1)( SD2,2  SD3,2 )    1  exp    1  exp  

 

 




SR
SD
SD
1,1
2,2
3,2

 

 

 (22th  1) 
exp  
 (22th  1)   SR1,1    SD1,2   SD2,2    SD2,2       1 1  exp  (22th  1)  SD1,2
 1  exp      













  SD1,2   SD2,2 
*
*
SD
SD
SD
SD
1 
R1,1 D2
R1,1 D2 
1,2
2,2
1,2
2,2


    SD  SD   *R1,1 D2
1,2
2,2



 (22th  1) 
exp  
 (22th  1)   SR1,1    SD1,2   SD3,2    SD3,2       1 1  exp  (22th  1)  SD1,2
 1  exp    




 













  SD1,2   SD3,2 **
1  SD1,2 SD3,2  
R1,1 D2
R1,1 D2  SD1,2 SD3,2    





  SD  SD   *R1,1 D2
1,2
3,2



 (22th  1) 
exp  
 (22th  1)   SR1,1    SD2,2   SD3,2    SD3,2       1 1  exp  (22th  1)  SD2,2
 1  exp    
  SD  SD      R D*    R D*   SD2,2   SD3,2 
1   SD2,2  SD3,2  
2,2
3,2

  
1,1 2
1,1 2   SD  SD   *R1,1 D2
2,2
3, 2


 (22th  1) 
exp  

2 th



3

 SR1,1   3 1 

 


1  exp   (2  1)    1 1  exp  (22th  1) 1    .


3
1
1 

 

  R D*    R D* l 1  SDl ,2

 
1,1 2
1,1 2  l 1     l 1  SDl ,2 
SDl ,2

R1,1 D2*

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 3, May 2015635

[16] A. Bletsas, H. Shin, MZ. Win, A. Lippman, A simple cooperative
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Smich Butcharoen received his Bachelor degree intelecommunication engineering from Suranaree
University of Technology. Nakhonratchasima,
Thailand in 1997. Master degree in information
technology from King Mongkut’s University ofTechnology North Bangkok, Thailand in 2004.
Currently, he is a Ph.D. candidate in wirelesscommunication engineering of The Sirindhorn
International Thai-German Graduate School ofEngineering (TGGS), King Mongkut's University ofTechnology North Bangkok, Thailand.
Chaiyod Pirak received his Bachelor degree intelecommunicationengineering
from
King
Mongkut’s Institute of Technology Ladkrabang,
Bangkok, Thailand in 2001 and Ph.D. degree from
University of Maryland, College Park, MD, USA in
2005. He is holding a lecturer position atcommunication engineering department of TGGS.
His research interests include the area of wirelesscommunication.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)