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
Dr. Pasi Ojala, University of Oulu, Finland
Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
Prof. Jesuk Ko, Gwangju University, Korea
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 ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
Dr. Noor Zaman, King Faisal University, Al Ahsa Hofuf, Saudi Arabia
Dr. Srinivas Mantha, SASTRA University, Thanjavur, India
Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
Prof. Sangho Choe, The Catholic University of Korea, korea
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
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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 ⓒ 2014 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 ⓒ 2014 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume. 4 Issue. 2

1 A Remote User Interface Framework for Collaborative Services Using Globally

5J

581

Internetworked Smart Appliances
Bongjin Oh*, Jongyoul Park ** *Bigdata S/W Platform, ETRI, Korea

2 Enhancing Routing Energy Efficiency of Wireless Sensor Networks

587

Noor Zaman*, Tung Jang Low**, Turki Alghamdi****,** Department of Computer Science & Information Science, Universiti Tecknologi PETRONAS,
Malaysia,***College of Computer and Information System, Umm Al-Qura University, Saudi Arabia

3 Optimization of Convenience Stores Distribution System with Web Scraping and Google
API Service
Thai Quang LE*, Davar PISHVA***International Management, Ritsumeikan Asia Pacific University, Japan**Asia Pacific Studies, Ritsumeikan Asia Pacific University, Japan

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

581

A Remote User Interface Framework for
Collaborative Services Using Globally
Internetworked Smart Appliances
Bongjin Oh*, Jongyoul Park **Bigdata S/W Platform, ETRI, 218 Gajeong-ro, Yuseong-gu, Daejeon, 305-700, Korea
bjoh@etri.re.kr, jongyoul@etri.re.kr
Abstract—This paper introduces a remote user interface
framework which supports devices to share the UI of theirapplications with multiple smart devices. The smart devices areinternetworked globally through RUI server. Besides that, avirtual IO function is provided to use mobile devices as remotecontroller. By thus, users can control home networked devices
and applications by their smart devices with intuitive UI/UX.
The proposed framework provides collaborative application
model, APIs of sharing application view and virtual IO
emulator.
Keyword— RUI framework, home network, collaborative
application, UI migration, Virtual IO

I. INTRODUCTION

S

mart devices, smart phones and smart pads, have become
the most familiar appliances with users since thosedevices were introduced with multi-touch based intuitive userinterface. TV is known for the friendliest consumer device topeople. It also has evolved to smart device from a typicalpassive device.
The current TV provides users with various interactive
contents augmented from linear services and downloaded
from application servers. Moreover, it is not awkward forpeople to interoperate their smart devices with smart TV for
collaboration services [2][5][7][8].
Recently, many researches have been introduced tointeroperate the smart devices with other devices such as TV,
information appliances and various sensors in home network
area [1][3][4][6]. Many RUI (Remote User Interface)standards such as MIRACAST [11], DLNA-RVU [12] andAirplay [13] use streaming protocols to provide remotedevice control or collaborative services. They have someproblems of using too a lot of bandwidth and supporting only
sharing of main graphic user interface because they transmit
video streams using the sequences of images captured fromframe buffer.
———————————————————————
Manuscript received Dec. 31, 2014. This work was supported by the IT
R&D program of MKE/KEIT, [KI10039202, Development of SmartTV
Device Collaborated Open Middleware and Remote User Interface
Technology for N-Screen Service]. Bongjin Oh is with the Electronics andTelecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu,
Daejeon, 305-700, Korea (corresponding author to provide phone:+82-42-860-6384; fax: +82-42-860-5885; e-mail: bjoh@ etri.re.kr).
Jongyoul Park was with the Electronics and Telecommunications
Research Institute, Daejeon, 305-700, Korea. (e-mail: jongyoul@etri.re.kr).

An HTML5 based collaborative application platform isprovided by MOVL UI [14]. It is independent of device
platform and based on a cloud server for collaboration
services. But it is time consuming for users to connect client
applications with host applications. Multiple applications
should be installed on smart devices, and users shouldinterconnect the devices by logging into allocated room withroom number displayed on TV screen by host applications.
This paper proposes a RUI framework based on sharable
GUI to support collaborative services among interconnected
smart devices. Virtual IO emulator is also provided to control
remote devices using virtualized device controllers.
The rest of this paper is organized as follows. Chapter II
describes the overview of the proposed RUI architecture, anda reference implementation of the RUI framework with
exemplary RUI services is shown in chapter III. Lastly, we
conclude our research briefly in chapter IV.
II. THE PROPOSED RUI FRAMEWORK
A. Network Configuration of the RUI Framework

Fig. 1. The proposed RUI network configuration. There is a RUI server tointerconnect all of home networked devices.

The RUI network is configured as Fig. 1. One SG (Service
Gateway) and several fixed or mobile smart devices areinternetworked in each home network. Smart devices control
and share UIs with other devices using RUI framework. SGs
are interconnected with each other through RUI server tosupport smart devices to control and share UIs with remote
Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015devices located at different home networks.
Service gateways manage devices and services of eachhome networks. Each SG also makes a channel in RUI server
to allow remote devices to access local devices managed bythem. The channel is set up with SG ID, login and password.
The smart devices can’t send RUI messages directly totarget devices because the most of them are networked with
VPN (Virtual Private Network). Therefore, they require local
SGs connected with them by same home network to forward
their requests, formed as RUI messages, to remote devices.
SGs also forward the requests to remote SGs connected withthe target devices with the help of RUI server.
RUI server manages channels with ID, login and password
as well as IP address for SGs, and forward the received RUI
messages to SG designated with SG ID after verifying login
and password included into RUI messages. For this, RUI
server also support asynchronous HTTP based
communication protocol for SGs to send and receive
forwarded RUI messages asynchronously.
Below diagram describes the procedure of making asession and exchanging RUI messages among RUI devices
through RUI server.

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each service gateways and RA (RUI Agent) and RV (RUI
Viewer) can run on every smart device.
The RM scans the RUI devices using the SSDP (Service
Discovery Protocol) of UPnP. Whenever a RUI device turns
on, The RA also find the RM using SSDP. If two devices areconnected, then the RM collects device profile from the RUI
device. The device profile includes device name, service list,
address and device mode, and it is stored to RUI Status Table.
The RM monitors the status of RUI devices. Client devices
get the global information for RUI services such as RUIapplication list, RUI devices, from the RM. The status ofclient devices including current running RUI application andconnection mode is stored to RUI Status Table together with
device profile s. The RM also supports service session
management among client devices. If a client device is gone,then the RM notifies it to another client device bound todisappeared client device.
The RV is implemented by extension of the WebKit torender HTML5 based UIs, and it handles user events invoked
locally or remotely. The user events are transmitted to the
RV’s event queue by the RA whenever users input events withlocal input device or remote virtual input devices. The RV is
launched automatically to render initial RUI Page by the RA.
The RA plays the most important role of RUI framework toshare UIs for remote control of devices and applications
among smart devices. The RA manages Top Window, Device
Page, Virtual IO Page, RUI applications and local repositoryand so on.
The Top Window is displayed as an overlay window on thescreen for interaction between users and RUI components.
Users can request RUI operations of the RA by long touch on
the top window for about 3 seconds. The RA shows thefunctions such as virtual IO on the Top Window, and thenusers select one of them to process.

Fig. 2. The procedure of communication among RUI devices located at
different home networks. RUI server manages channels of SGs to forward
the input RUI messages to target devices.

B. Components of the RUI framework
Three kinds of RUI components are launched on the home
networked devices such as SG and client devices. Their
TABLE ICOMPONENTS OF THE PROPOSED RUI FRAMEWORK
Components

Function

RUI Manager- manages the home networked devices and RUI
services- provides local devices with method tocommunicate with devices outside home- manages the UIs of launched applications andvirtual IO page
- collaborates with other devices for sharing of UI
and IO functions- displays application UI and virtual IO UI- handles the user eventsRUI Agent

RUI Viewer

functions are described at Table I.
RM (RUI Manager) is the only component which runs on

Fig. 3. The protocol stack of the RUI Agent.

The software elements of RUI Framework except the RV
interoperate with each other using HTTP based messages.
The RA provides the software elements with RUICommunicator APIs based on JSON message system. On thecontrary, The RV is tightly coupled with the RA on every
client devices, and they interoperate with each other using
local procedure calls to invoke user’s input events and to
render application’s or RUI initial UI page.
Virtual IO Page is also described as an HTML5 document
including the key map of physical controllers such as remocon,
mouse and keyboard provided locally. Virtual IO Page istransmitted to other Remote Agents for virtual IO mode. The
Remote Agent shows the received Virtual IO Page to users by

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

the RV to control remote devices using the similar userinterface of physical controller.
The Device Page is changed according to current mode ofclient’s device. When the RUI framework is launched, thepage is set with the RUI Initial Page. If the device is bound toother device as virtual IO mode, then the page is set with the
Virtual IO Page. Lastly, the page can be set with UIs of RUIapplications launched locally or remotely. If the RUIapplications are running on a remote client, then the UI to be
set is moved to local device from the remote client.
The RA installs RUI applications to the repository of client
devices, and manages their life-cycles. It also request remote
RAs to launch RUI Applications installed on the remotedevices. The UIs of RUI Applications are able to be migrated
into other devices for remote control. The UIs and Virtual IO
Pages are distributed to remote RUI Agents using OSGI’s
core APIs. In this paper, the UIs and Virtual IO Pages arehandled as sub-apps included in a service bundle.
C. Collaboration Model of RUI Application

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1) Mirror
The UI of the original application which runs on the remotedevice is duplicated, and the UIs are transmitted to multiple
devices. If an input event is invoked in the original UI, then all
the duplicated UIs also receive the invoked event at the sametime.
2) Migration
The original UI of local device is moved to selected devices.
This mode is needed to display the local UI on the bigger
screen. The local UI is automatically changed into virtual IO
mode to control the migrated UI with local device.
3) Segmentation
The parts of Remote UIs are pulled and rendered on thedisplay of local device. The UIs may be displayed on multiple
devices according to the requests of several users at the sametime.
As described in Fig. 2, Virtual IO page can be transmitted
to globally networked devices for remote control through RUIserver. The RUI messages of Virtual IO Page and invokeduser events are routed to remote devices through local SG,
RUI server and remote SG. Because of the problem ofsynchronization, the UI of application can’t be shared
between two devices located at different home until now.
The migrated UIs and logic communicated to each otherwith RUI messages which have the format as Fig. 6.

Fig. 4. The concept diagram of the proposed collaboration model. HTML5
based UI is composed of serveral parts to be shared by serveral devices.

RUI based services are installed on one of inter-connected
smart devices by users. Users can browse the service list
regardless of local service or remote service by RUI service
browser. When users select a service, the selected service will
be launched on the device which it is installed on. Users cancontrol the remote service by the proposed RUI protocol
based on migratable UI as Figure 5.

Fig. 5. The collabroation model of RUI applications. Migrated UI segments
will communicate to the logic installed in local device through RA.

The Sub-description of each UI segments includes
attributes such as segment ID, sharing mode, URL and input
event model etc. Three kinds of the UI sharing modes areprovided as follows.

Fig. 6. The Structure of RUI Message. SE address contains ID to distinguishwhich target is local devices or remote devices and target software element.

The address of RUI message consists of SG ID, device
UUID, software element ID and segment ID to distinguish
software elements. The kinds of software elements areclassified into RUI applications (UI segments, logic apps) andRUI components (RM, RAs).
The IP address of RUI message is decided by the related
information stored to the RUI Status Table such as thecomposition of device ID, application ID and UI segment ID(application ID is allocated per RUI service and included inservice descriptions).
A RA figures out the SG ID is local or not before
transmission of RUI messages. If SG ID is for local SG, then
the RA transmits RUI messages to target elements directly.
Otherwise, the messages are redirected to target elements ofglobally networked devices via RUI server. First, RA tosses
RUI messages to local RM to forward those messages totarget elements. Then, Local RM transmits RUI messages to
RUI server to be verified and forwarded to target RM. lastly,
the remote RM transmits the received messages to target
device using IP address mapped to device ID contains in themessages.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

D. Virtualized Input Devices
The proposed RUI framework provides two virtual IO
modes for users to utilize smart devices as remote controllers
as shown in Figure 7.

584

emulators. If an event model contains keyboard and mouse,
then the icon of keyboard and mouse is displayed in the virtual
IO menu to be selected by users.
If a virtual mouse emulator is selected then user’s inputs aretransferred to remote RA through local RA as Mouse Events.
Then the remote RA forwards the Mouse Events to RV, andRUI Apps (UI Segments) consume the Mouse Events
generated by RV. This procedure is different with that ofphysical remocon.
III. REFERENCE IMPLEMENTATION(a) Device based virtual IO mode(b) Application based virtual IO mode
Fig. 7. Virtual IO action model. Virtaul IO Page is pulled to local device, sousers can control the remote device as local device.

The first mode is the device virtual IO mode which user
device plays the role as same as physical controller of othersmart devices. Each RA manages Virtual IO Page which
describes control UI of local device’s physical controller suchas remocon or control panels. The Virtual IO Page is
described as HTML5 based application which can berendered by the RVs of other devices. The Virtual IO Page istransmitted to other devices and launched by RVs of thedevices, when users want to control remote devices by virtual
IO mode.
User inputs are transmitted to remote RA as remocon
control codes, and the codes will be consumed by system
event handler. The system handler forwards codes to user
event handler after converting them into key events. This
procedure is also processed when user press a button ofphysical remocon. If any smart appliances, such as
refrigerator, boiler and washing machine as well as TV, canbe accessed from internet, then user can remotely control
them using device based virtual IO mode at any home.
The second mode is the application virtual IO mode whichuser can select necessary type of virtual controller
dynamically. RA manages embedded virtual IO emulators to
be launched by only local RV, not by remote RVs. Virtual IO
emulators are predefined in the repository of the RA
according to the capabilities of local devices.
RUI applications should notify the type of input model forcontrol their UI segments to RAs which launch the UI
segments according to user’s request, and RAs will provide
users with selectable IO emulators among embedded

Fig. 8. Network configuration of a reference implementation. All devices areinterconnected with one home network except contents servers.

A reference platform is implemented to show thefunctionalities of the proposed RUI framework together withan exemplary RUI application. The network configuration of
the reference platform is shown as Figure 8 and Table II.
TABLE IITHE DETAILS OF DEVICE AND SOFTWARE ENVIRONMENT
Item

Details

Hardware- Set-top box, smart phone, smart pad
Android 2.x.x, dual core, RAM:2GB- Contents Server (PC), Windows 7, quad core: i-7,
RAM: 4GB, HDD: 1T, 5400RPM

Networks- AP (WiFi n/g, Ethernet 100Mbps) and switch (1Gbps)

Software- VideoTube RUI application (VOD client)media player, contents guide, media control
functionalities (UPnP AV renderer)- VideoTube server
UPnP AV architecture
Directory service, HTTP based streamer- HTML5 based web apps - handles the user events

Android based a set-top box; a smart phone and a smart pad
are interconnected by an AP connected to a Giga-bit switch. A
VOD server is connected directly to switch as an UPnP AV
server. VOD client is installed the set-top box as an UPnP AV
Renderer. The RM runs on the smart pad to manage the status
of RUI framework. Some HTML5 games found on websites
by the keyword of “HTML5 games” are installed on both of
set-top box and smart pad. The smart phone is only used tocontrol other devices as a virtual IO or remote UI sharing
mode.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

585

Some HTML5 based web apps deployed on websites areused to verify our proposed virtual IO functionalities. Some ofthem are developed for PC, and the others are developed formobile devices. Therefore, user’s smart device should be
virtualized into keyboard for PC version and virtualized into
mouse for mobile device version according to user’s selection.
Fig. 10 shows an example of virtual IO service collaborated
with TV and two smart devices.
An Entanglement game [15] is launched in PAD. Whenuser requests the game migrate to TV, the UI of user’s PAD ischanged to the virtual Keyboard automatically. The phone isalso bound to TV as a virtual mouse mode. Two users canplay the game together simultaneously.
(a) Segmenting RUI mode

IV. CONCLUSION
The proposed RUI framework supports collaborative
services using decomposable and sharable UIs amonginterconnected smart devices. The framework consumes lessnetwork bandwidths than the typical streaming based RUIprotocols because the RUI framework uses HTML5 based UI
and message driven interoperation between multiple devices,
Moreover, users can use local devices as intuitive remotecontrollers of other devices using virtual IO emulators. Thosefunctionalities can be provided to devices interconnected withdifferent home networks through the RUI server using SG
channels.
TABLE IIITHE COMPARISON OF RUI PROTOCOLS(b) Mirroring RUI mode
Fig. 9. A Exemplary RUI service (Segmenting & Mirroring).

VideoTube RUI service was implemented for remote UI
sharing functionality among home-networked devices as
Figure 9. VideoTube is composed of a contents server and amedia player to provide users with a VOD-like service.
The UI of media player is able to be fragmented into Media
Display UI, Control Panel UI and Contents Navigation UI. In
the (a) of Figure 9, a user pulled the Contents Navigation UI
together with Control Panel UI from TV. Only Media Play UI
remains on the screen of TV with full screen mode
automatically. There are 8 kinds of layout templates areprovided for various status of RUI sharing for VideoTube.
The mirroring RUI mode is shown in the (b) of Figure 9.
The UIs of VideoTube are duplicated to smart pad, and users
can control the VideoTube by local smart pad or remote TV.
The user input events are multicast to all of mirrored devices
as well as TV which launches the VideoTube.

Proposed

MIRACAST

Airplay

MOVL UI

Low
OK
OK
OK
Easy

High
OK
NO
NO
Easy

High
OK
OK
NO
Easy

Low
NO
OK
NO
Difficult

OK

OK

NO

OK

Table Ⅲ shows that the proposed RUI framework is better
than other RUI standards for various RUI functionalities.

REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]
[7]
Fig. 10. An Exemplary Virtual IO service (Mouse & Keyboard)

Function
Bandwidth
Mirroring
Collaboration
Virtual IO
Device Paring
Platform
Independent

Bong-Jin Oh, Jong-Youl Park, “A Remote User Interface Framework
for Collaborative Services Using Interconnected Smart Devices,” inProc. ICACT2014, Pyunchang, 2014, pp.630-634.
Moon Soo Lee, Seung-Joon Kwon, “Lightweight Inter-device Interface
Control System for the Machine-to-Machine (M2M) Interaction in theInternet of Things,” in Proc. ICCC2014, Jeju Island, 2014,
pp.481-482.
Bumsuk Choi, Junghak Kim, Soonchoul Kim, Youngho Jeong, Jin
Woo Hong, and Won Don Lee , “A Metadata Design for Augmented
Broadcasting and Testbed System Implementation,” ETRI Journal,
vol.35, No.2, pp. 292–300, Apr. 2013.
Kyoung Ill Kim, Su Young Bae, Dong Chun Lee, Chang Sik Cho, Hun
Joo Lee, and Kyu Chul Lee, “Cloud-Based Gaming Platform
Supporting Multiple Devices,” ETRI Journal, vol.35, No.6, pp.
960-968, Dec. 2013.
L. Bassbouss, M. Tritschler, S. Steglich, K. Tanaka, and Y. Miyazaki,"Towards a Multi-Screen Application Model for the Web," in Proc. the
1st IEEE International Workshop on Consumer Devices and Systems(CDS 2013), Kyoto, 2013, pp.528-533.
Steven Morris, Anthony Smith-Chaigneau, Interactive TV Standards.
Elsevier Inc., 2005.
Moon Soo Lee, Min Jung Kim, Sun Joong Kim, Cho Kee Seong,“Remote Collaboration Screen Control Using Mobile Multi-Touch
Interface,” in Proc. ICTC2012, Jeju Island, 2012, pp. 272-273.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

586

Yusoek Bae and Jongyoul Park, “A Seamless Remote User Interface
System Supporting Multi-Screen Services in Smart Devices,” in Proc.
ICCE2013, Lasvegas, 2013, pp. 462–463.
[9] Bong-Jin Oh and Jong-Youl Park, “Design and Implementation of
HTML5 based Collaborative N-Screen Contents Platform for Smart
TV,” in Proc. ICCC2013, Okinawa, 2013, pp.225–226.
[10] JeaWon Moon, Tae-Beom Lim, Kyung Won Kim, Seok Pil Lee,
SeWoom Lee, “Advanced Responsive Web Framework based on
MPEG-21,” in Proc. ICCE-Berlin2012, Berlin, 2012, pp.192–194.
[11] Miracast. [Online]. Available: https://www.wi-fi.org/[12] RVU Alliance. [Online]. Available: http://www.rvualliance.org/[13] Apple AirPlay. [Online]. Available: http://www.apple.com/airplay/[14] MOVL UI. [Online]. Available: http://connect.movl.com/[15] Entanglement HTML5 based web app. [Online]. Available:
http://entanglement.gopherwoodstudios.com[8]

Bong-Jin Oh received B.S. and M.S. degrees incomputer science from Pusan National University,
Busan, Korea in 1993 and 1995 respectively, and the
Ph.D. degree from Chungnam National University,
Daejeon, Korea in February 2012. Since 1995, he has
been with the Electronics and Telecommunications
Research Institute (ETRI), where he develops home
network middleware and data broadcasting middleware. His researchinterests are home network middleware, data broadcasting middleware,
IPTV, pervasive computing, and big data analytics.
Jongyoul Park received the B.S. degree in computerengineering from Chungnam National University, Korea,
in 1996, the M.S. and Ph.D. degrees in information andcommunication engineering from the Gwangju Instituteof Science and Technology (GIST), Korea, in 1999 and2004, respectively. From 2001 to 2002, he was a visiting
researcher at the school of computing, University of Utah.
Since 2004, he has been a Research Staff and Director of Analytics SW
Research Section of Electronics and Telecommunications Research Institute(ETRI), Korea. His research interest includes IP broadcasting, software
middleware, mobile code, distributed computing, big data and analytics
platform.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

587

Enhancing Routing Energy Efficiency of WirelessSensor Networks
Noor Zaman*, Tung Jang Low**, Turki Alghamdi****,** Department of Computer Science & Information Science, Universiti Tecknologi PETRONAS, Malaysia, ***College ofComputer and Information System, Umm Al-Qura University, Saudi Arabia
Noorzaman650@hotmail.com, lowtanjung@petronas.com.my, taghamdi@uqu.edu.sa

Abstract—Nowadays Wireless Sensor Networks WSNs areplaying a vital role in several application areas ranging healthto battle field. Wireless sensor networks are easy to deploy due
to its unique characteristics of size and self-organizing
networks. Wireless sensor nodes contain small unchangeable
and not chargeable batteries. It is a resource constraint typenetwork. Routing in WSN is most expensive task as it utilizes
more power resources. This paper is intended to introduce
energy efficient routing protocol, known as Position Responsive
Routing Protocol (PRRP) to enhance energy efficiency of WSN.
Position responsive routing protocol differs in several ways
than other existing routing techniques. Position responserouting protocol approach allows fair distribution of
gateway\cluster head selection, maximum possible distanceminimization among nodes and gateways\cluster heads toutilize less energy. Position responsive routing protocol shows
significant improvement of 45% in energy efficiency of wirelesssensor network life time as a whole by increasing battery life ofindividual nodes. Furthermore PRRP shows drastic increases
for data throughput and provide better solution to routing
energy hole due to it fair distributed approach of gateway
selection. This work is the extension of Energy efficient routing
protocol for wireless sensor network published in IEEEICACT 2014.

Keywords— CELRP, Energy Efficient, WSN, PRRP position
responsive routing protocol, Routing protocol,

I. INTRODUCTION
SN is a popular and have capability to high penetratewith several applications areas. It consists of small
nodes having limited sensing, computation, andwireless communications capabilities [1-2]. Sensor nodes
normally sensed data and forward sensed data to the basestation such as temperature, sound, vibration, pressure,motion or pollutants [3].

W———————————————————————
Manuscript received December 30, 2014.
Dr. Noor Zaman is associated with King Faisal University, Saudi
Arabia, (phone: +966-566117159; e-mail: nzaman@kfu.edu.sa).
Dr. Tung Jung Low, is associated with University Teknoloigi Petronas,
Malaysia, (e-mail: lowtanjung@petronas.com.my )Dr. Turki Alghamdi is associated with Umm Al Qura University, Saudi
Arabia. (e-mail: taghamdi@uqu.edu.sa).

Sensor nodes normally sensed data and forward sensed data
to the base station such as temperature, sound, vibration,pressure, motion or pollutants [3]. Sensor nodes are resource
constraint type of network and contain very tiny size of
irreplaceable and not chargeable batteries. Network life is
the highly concern, energy become reason to dyes of nodes
and network partially\ fully stop working [4-6]. Energy
consumed more during path finding and data transmission
operations terms as routing. Routing is the most challenging
issue and direct concern to energy in WSN comparable withad hoc and cellular network [7-8]. Clustering technique forrouting in WSN is considered most suited based on its
characteristics such as energy-efficient, scalable, lower
latency, etc. In clustering, WSN network is divided into sub
networks\clusters and each cluster has cluster head which is
responsible to collect the sensed data from his cluster andforward it to the base station [9]. Cluster heads consumed
more energy due to collecting and forwarding data fromcluster while remaining nodes in the clusters still have moreenergy of 90% of their initial energy [10-12]. This situation
normally happened due to unbalanced energy assumption
which causes more drain of energy from nodes far fromcluster heads [13] in random fashion from sensor nodes. To
address this issue, several energy efficient routing
algorithms and protocols have been propose recently,
including cluster based protocols [14-19], power-awarerouting [20-22] and multi-level transmission radii routing[23]. The minimum energy routing problem has beenaddressed in [24]. If sensor nodes consume energy more
equitably, they continue to provide connectivity for longer
and the network lifetime increases. [25-36].
As sensor networks have specific requirements onenergy saving, data-oriented communication, and interconnection between non-IP and IP, therefore sensor
network-dedicated routing protocols may be required, for
Energy efficient routing scheme.
II. PROBLEM STATEMENT
Sensor network has primary role to sense and forward
data to destination or base station BS, resulting of any
physical event occurrence. Routing plays a key role toidentify path and transfer data in energy constraint sensor
network. Initially routs defined by the nodes then nodes

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

become able to send or receive the data by using thoserouting paths. In case if sensed data is available to somesegments of network but network not able to transfer it to
the destination due to the energy deplete of sensor nodes forsome segments.
III. RELATED WORK
Several routing protocols have been developed recently
to address the energy efficiency issue. WSNs routing
protocols normally specified in following types.
1. Flat routing Protocols
2. Hierarchal routing Protocols
3. Location based routing Protocols
Hierarchal routing protocols work in cluster formation andconsidered to be more energy efficient due to their uniquecharacteristics. Recently proposed Cluster based Energy
Efficient Location Routing Protocol (CELRP), also belongs
to Hierarchal type. In CELRP sensor nodes are normally
distributed into clusters and divided into different quadrants.
Each quadrant contains two clustering and sensor nodes thattransmit data with two hops data transmission [37]. CH is
selected based on the node with maximum residual energy
and minimum distance to the base station in each cluster.
While the CH which has the highest energy residual ischosen as the CH Leader between all the other CHs. CELRP
applies Greedy algorithm among cluster heads and forward
data to the sink. The collection works as nodes sends data to
CH and then CH forward their data to the sink through CH
leader by minimizing number of hops. CELRP assumed thatthe Base Station has all the information including the sensor
nodes, the residual energy and the distance of node fromsink. Sink is place far from the sensor node area.
TheCELRP based on three phases, in first phase it works for theformation of clusters, network divided into four quadrants
and then it forms clusters. In second phase it selects CH andCH leader on the basis of its energy and finally with third
phase it transfer data to the sink. CELRP has main
limitations like its number of children nodes is high in theclusters and secondly its CHleader choosing mechanism
works on the basis of energy level, in most of the cases
CHleader is not the closest to CH, hence it causes moreenergy drain because of longer distance. At the same time ituses Greedy approach for data transmission which also
causes loss of energy efficiency.
Hence it is highly needed to design an energy efficientrouting protocol with assumptions closer to the real, we areposition responsive routing protocol (PRRP) WSN routing
protocol which is more energy efficient than the existing
protocols.
IV. SYSTEM MODEL
Our assumptions for sensor network are such that,
sensor nodes are randomly distributed over an area of 90 x
90 meters with following network properties.

588

1. Network is static and nodes are distributed in random
format, while area is divided in equal square grid format.
2. There is exists only one base station, which is deployed in
the center of the area.
3. The energy of sensor nodes cannot be recharged.
4. Sensor nodes are location aware.
5. The radio power can be controlled, i.e., a node can vary
its transmission power.
Above all assumption are on wide scope, assumption no.
5, is becoming the cause of energy saving, as nodes will beaware about their location and sink too, hence the amount ofenergy which normally network always use to find out theinitial location will be saved.
A.

Network Model

The entire simulation tests were conducted by using avery well-known simulator by the research community NS2,
by applying different topologies and approaches. The few of
those topologies are shown below. In the entire topologies
sensor nodes were distributed within an area of 80*80
meters and then tested their routing capabilities in two ways,
initially it was tested with a normal distribution, while insecond phase it was tested through position responsive (PR)
Algorithm, in which each node was aware about its
neighbors’ location. The second approach for the routing
was tested more excellent in terms of its power saving or interms of nodes life optimization, which we discussed withcoming section of results with the help of simulation graphfig. 1. The following is one of the screen shot scenario for
distributed sensor nodes in Fig. 1

Fig. 1 Sensor nodes distributed with different topology

B.

Energy Model

The power control model, used in this research in which theenergy is consumed, during the transmission will depend on
the transmission distance [38]. The energy consumed by anode (ETx) during transmission of k bits to another node at a
distance of d meters, and the energy consumed (ERx) toreceive k bits are calculated as:

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

𝐸𝑇𝑥 = 𝑘𝐸𝑒𝑙𝑒𝑐 + 𝑘 𝐸𝑎𝑚𝑝 𝑑2(A)

𝐸𝑅𝑥 = 𝑘𝐸𝑒𝑙𝑒𝑐(B)

where,

589

the energy needed by one cycle of one data transmission
period by 5 or 10, respectively. Of course, in reality actuallyenergy used will be less as the first three phases of theprocess are only done once. On the other hand, it is assumed
that the isolated nodes are dead nodes. Isolated nodes are thenodes that do not receive a broadcast message to join the
tree.
C.

Energy Analysis

Eelec: represents the electronics energy
Eamp: represents the amplifier energy
Eelec represents the electronics energy and is determined byseveral factors such as digital coding, modulation, filtering,and spreading of the signal. On the other hand, Eamp
represents the amplifier energy. In our experiments, it isassumed that Eelec =50 nJ/bit and Eamp =100 pJ/bit/m2,
which are the same values used in [37]. The initial energy
stored in each node is 2 J. Since it is quite interesting tocompare different protocols in terms of the energy
consumed in transmission and receiving states, we ignore
the energy consumed in sensing the environment as it will
be same for the evaluation of all protocols. Furthermore, thenode will be considered a dead node, and will not participate
in the coming round if its energy becomes less than athreshold value (Ethreshold).
For example, a node, in the worst case, will be a non-leaf
node and will have eight (08) children in the coming round.
For a single transmission period, it will receive a maximumof eight (8) data packets and transmit one data packet. Then,the minimum energy for this node to be able to participate in
the coming round Ethresholdmin can be computed as follows:

Concerning energy efficiency of PRRP, This section
presents an analysis of energy utilization in terms of energy
consumed by cluster head nodes and leaf nodes. Consider awireless sensor network comprising 𝑁 wireless sensor
nodes that are uniformly distributed within a grid of size m
× m; within each cell one of node acts as the cluster head
(CH), so the number of cluster heads will be same as thenumber of cells, i.e. 𝑚2 .
We shall use the following symbols to denote quantities
mentioned against each:𝑘: Number of leaf nodes in a cell; i.e. k =

𝑁
𝑚2

− 1;

(not counting the CH),

𝐸𝑡 : Energy to transmit one sample,
𝐸𝑟 : Energy to receive one sample,
𝐸𝑖𝑛 : Initial energy provided to each node at the time of
deployment,
𝐸𝑡ℎ : Energy Threshold below which a node cannot act
as a cluster head
𝐸𝑙𝑛 : Energy consumed by Leaf node

𝐸𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑𝑚𝑖𝑛 = 𝑘𝐸𝑒𝑙𝑒𝑐 + 𝑘𝐸𝑎𝑚𝑝 𝑑2 + 8𝑘𝐸𝑒𝑙𝑒𝑐 (C)𝑃𝑠 : Power consumed while a node is asleep,
𝑇𝑡 : Time to transmit one sample,

Assuming that the size of data packets is 100 bytes and the
maximum distance is √2 x 5 m, and using the values ofEelec, Eamp as in 3.3, Ethresholdmin for a single data transmission
period will be (376 μJ). Taking into account the energy
needed for selecting gateway, building the tree and TDMA
schedule, it is empirically found from extensive simulation
results that (400 μJ) is a good estimate for Ethresholdmin for asingle data transmission period. A reasonable good estimate
means that if the node has a residual energy equal to thisEthresholdmin, this energy will be sufficient for the node toparticipate for a round with a single data transmission period
and the node will stay alive until the end of the one period in
the round.
Now, for five and ten data transmission periods, Ethresholdmin
will approximately be 2000 μJ and 4000 μJ, respectively.
This assumption is a pessimistic one as it simply multiplies

𝑇𝑠 : Sampling interval,
Energy Consumed by leaf nodes
For leaf nodes, there are two modes of energy
consumption namely: energy consumed in sleep state andenergy consumed during transmission. During eachsampling interval 𝑇𝑠 , a leaf node spends 𝑇𝑡 time fortransmission and the remaining 𝑇𝑠 − 𝑇𝑡 time in sleep mode.
Thus energy consumed by a leaf node, 𝐸𝑙𝑛 can becalculated as|:𝐸𝑙𝑛 = 𝐸𝑡 + (𝑇𝑠 − 𝑇𝑡 )𝑃𝑠(A)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

Energy Consumed by cluster heads
As cluster head spends its life in three modes, namely:
receiving samples from leaf nodes, transmitting its data andsleep mode. Since, the energy consumed to receive a sample
is 𝐸𝑟 and there are 𝑘 leaf nodes in each cell; thus, totalenergy consumed by CH in receiving 𝑘 samples is 𝑘 𝐸𝑟 ,while energy consumed by CH during transmission is 𝐸𝑡 .
During each sampling interval, a CH spends 𝑘 𝑇𝑡 time forreceiving samples, 𝑇𝑡 time for transmission and remaining
𝑇𝑠 − (𝑘+1)𝑇𝑡time in sleep mode; thus energy
consumed in sleep mode is 𝑃𝑠 × (𝑇𝑠 − (𝑘+1)𝑇𝑡 ). The
total energy consumed by a CH during one samplinginterval,𝐸𝑐ℎ is given by:

𝐸𝑐ℎ = 𝐸𝑡 + 𝑘 𝐸𝑟 + (𝑇𝑠 − (𝑘+1)𝑇𝑡 )𝑃𝑠(B)

Next, we calculate life of a CH in terms of number ofsampling intervals, 𝑁𝑠 while its remaining energy is morethan the threshold 𝐸𝑡ℎ . Thus,
𝐸 −𝐸

𝑡ℎ
𝑖𝑛
𝑁𝑠 = 𝐸 +𝑘 𝐸 +(𝑇−(𝑘+1)𝑇 )𝑃

Total life of a CH,

𝑡

𝑟

𝑠

𝑡

𝑠

(C)
Life of a leaf node
Once a CH dies, a new round of cluster head selection is
initiated. In the following paragraphs, we show that leaf
nodes still have enough energy left to participate as cluster
heads in future rounds.
The initial round leading to exhaustion of a CH consistsof 𝑁𝑠 sampling intervals as shown above. Therefore, energy
consumed by a leaf node during the first round, 𝐸𝑙𝑛1 , isgiven by 𝑁𝑠 × 𝐸𝑙𝑛 ; i.e.,

𝐸𝑙𝑛1 = 𝑁𝑠 × 𝐸𝑙𝑛(D)
Using Eqs A and C, Eq D becomes:
𝐸 −𝐸

𝑡ℎ
𝑖𝑛
𝐸𝑙𝑛1 = 𝐸 +𝑘 𝐸 +(𝑇−(𝑘+1)𝑇 )𝑃
𝑡

𝑟

𝑠

𝑡

𝑠

*

(𝐸𝑡 +

(𝑇𝑠 − 𝑇𝑡 )𝑃𝑠 )Assume that 𝑃𝑠 ≅ 0 and divide equation D with 𝐸𝑡 wewill get𝐸𝑙𝑛1 ≅

𝐸𝑡 (𝐸𝑖𝑛 − 𝐸𝑡ℎ )1 + 𝑘 𝐸𝑟 / 𝐸𝑡

590

Typically, energy consumed during transmission of anumber of bits is about four times as much as energy
consumed for receiving the same number of bits[39], that is
to say that 𝐸𝑡 = 4 𝐸𝑟 .
So

𝐸𝑙𝑛1 ≅

𝐸𝑖𝑛 − 𝐸𝑡ℎ
1+𝑘 /4(E)

It is obvious from Equation (E) that at the end of firstround, for a sufficiently large cluster size, leaf nodes
consume only a fraction of their energy reserve. Thus, thenetwork will last for many rounds after the death of firstcluster head.
V. RESULTS & DISCUSSION
In this section, it is attempted to compare our proposed
PRRP protocol with CELRP. In CELRP sensor nodes arenormally distributed into clusters and divided into different
quadrants. Each quadrant contains two clustering and sensor
nodes that transmit data with two hops data transmission[36]. CH is selected based on the node with maximum
residual energy and minimum distance to the base station ineach cluster. While the CH which has the highest energy
residual is chosen as the CH Leader between all the otherCHs. CELRP applies Greedy algorithm among cluster heads
and forward data to the sink. The collection works as nodes
sends data to CH and then CH forward their data to the sinkthrough CH leader by minimizing number of hops. CELRP
assumed that the Base Station has all the information
including the sensor nodes, the residual energy and the
distance of node from sink. Sink is place far from the sensor
node area.
CELRP have main limitations such as the number ofchildren nodes is higher in the clusters like LEACH andsecondly its CHleader choosing mechanism works on thebasis of energy level, in most of the cases CHleader is not
the closest to CHleader, hence it causes more energy drain
due to longer distance. CELRP consider the placement ofsink far from the sensing area and at the same time it uses
Greedy approach for data transmission which both also
caused loss of energy efficiency. Hence above statedlimitations causes major sources of energy drain. PRRP
becomes more energy efficient by using different techniques
for stated limitations besides of other considerations. Thissection is aimed to discuss the performance evaluation of
PRRP and CELRP and to compare them on the basis ofnumber of live nodes vs time, total consumed energy andnetwork throughput one by one and collectively for different
data transmission periods.
A:

Comparison Analysis on the basis of Live Nodes
This section describes comparison analysis of results
for live nodes among CELRP and CELRP for different datatransmission periods individually and collectively. Fig. 01
shows a comparison between PRRP and CELRP in terms ofnumber of live nodes initially for single period oftransmission. Here, our network is tested with different

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

periods and rounds of data transmission such as 1,5 and 10.
After discussing all one by one, all of them finally andcollectively shows for a better understanding of thedifference between CELRP and PRRP. The following Fig.
01 presents the comparison for the single round only.
Our observation shows significant results of PRRP over
CELRP for different periods and rounds. Initially its resultshows little differences but finally through the increase in
the number of the periods, it brings more significant
differences as shown in Fig. 02. PRRP shows a more time
network operational with single period, which is almost up
to 140 seconds, yet CELRP goes up to 120 seconds only.

591

In the third test, we compare CELRP and PRRP for 10
numbers of periods as shown in Fig. 03. It shows that PRRP
has a more significant performance than CELRP for all
scenarios. For example, considering the scenario of 10 datatransmission periods, the network life is significant
improved for PRRP with an excellent improvement insensor network lifetime, which is almost 400 seconds, while
for LEACH it is about 300 seconds. The improvement in
PRRP is because of the efficient use of the energy for all
nodes is up to its last limit and also has an opportunity toparticipate all nodes in the network without having any
network isolated part with it. Secondly, the energy
enhancement in PRRP is because of minimizeed energy
consumption with each node by decreasing the distance
between the gateway and the non-leaf nodes and between
the sink at the same time.

Fig. 02 Number of Live Nodes vs. Time, (PRRP &CELRP with one Period)
Furthermore, we observe the same difference in anotable improvement when testing our network for the 5
periods as shown in Fig. 03 below. The results then showthat PRRP goes up 300 seconds of time, while CELRP
reaches up to 200 seconds. This difference will increase
whenever we test the network for the different and morenumber of rounds. This positive difference comes due to theefficient mechanism of the gateways selection andfunctioning in PRRP. The area of operation of the neighborsof the PRRP is always close to its gateway for saving its
energy level as shown in several different cases of different
rounds of data transmissions and at the same time CH haveless distance from the sink as it is placed in the center.

Fig. 03 Number of Live Nodes vs. Time, (PRRP &CELRP with Five Periods)

Fig. 04 Number of Live Nodes vs. Time, (PRRP &CELRP with Ten Period)
By comparing the energy consumed by a node in
CELRP and PRRP, a difference in status of the nodes will
be found in CELRP and PRRP. It should be considered that
there are three different types of the nodes in PRRP; whilein CELRP it is only two types of nodes such as normal nodeor non-head node and Cluster head or head node, while theCH leader is to be chosen from one of the CH on the basisof its energy level . In PRRP nodes on the other hand has
three following different types such as non-leaf node, leaf
node and gateway nodes. Energy can be compared on thebasis of energy consumed through all nodes duringtransmitting, receiving and idle-listening state.
When we compare PRRP with CELRP for different
periods of data transmission, PRRP always shows an
excellent improvement with energy saving of the network,
as collectively shown in Fig. 05. Simulation results shows asignificant result with an increase in number of the roundsof PRRP, as PRRP uses a mechanism in which if the samenode has some residual energy level, each node can be a
gateway until the end. This approach enables PRRP to savemore energy and use all nodes energy up to its last
maximum limit. The difference can be easily monitored inFig. 05 presenting the collective results of different datatransmission rounds.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015592

of energy decrease. For this, the total consumed energy in
PRRP will be smaller in comparison to CELRP as shown in
the following Fig. 06 for different data transmission periods.

Fig. 05 Number of Live Nodes vs. Time, (PRRP &CELRP Collectively)
B:
Comparison Analysis on the basis of Total Energy
Consumed
As mentioned in earlier sections, in tree building phasein PRRP, the nodes can transmit signals with minimum
energy level. Whilst, the parent can select node which is
closer to the node on very short distance which saves its
energy level. In comparison with CELRP, parent of node
can be any node, CELRP do not consider the distanceamong nodes and CHs. If we compare the energy
consumption by leaf node in PRRP and normal node in
CELRP, it can be noted that both types of nodes will not
lose energy due to receiving data or idle listening. As a
TDMA transmission scheme is implemented, they will not
lose any energy and node will be only ON at its schedule
time only.
The transmission distance is very short in PRRP, as theparent node is near leaf node. While in CELRP the mainpart of the energy is consumed during the data transmission
phase in particular when the normal node is at a distancefrom the cluster head and mostly CH leader is on longer
distance from the CH. The second main reason for theenergy loss in CELRP is the sink is place on long distancehence overheads becomes more for nodes which are far
from the sink. Thus, there is a chance that the CH leader andcluster head itself might be so far from the normal nodes andbecomes the factor of energy loss during the datatransmission phase. Furhermore, by comparing non-leaf
node and the cluster head node of CELRP, it can be foundthat non-leaf node in PRRP is not losing its energy due tothe idle listing as it schedules to receive and transmit data at
the specific time slots only. It is also found that in CELRP
cluster head should remain ON to transmit its data to thesink and cluster head uses CSMA technique. Therefore, itloses the energy.
The number of the children is less related to any
gateway in PRRP. It is due to the gateway, compared to thehead in CELRP, has less number of children, thus making it
consume less energy in receiving data. This also comes to bea factor for a deep tree building in which neighbor nodes
remain closer to the gateway. In CELRP any cluster head,
conversely, might have a more number of children nodes atdifferent distances. This in CELRP also becomes the factor

Fig. 06 Total Consumed energy vs. Time, (CELRP &PRRP)As an illustration, if we compare to 10 datatransmission periods at t=150 seconds, the total consumed
energy is about 230 J in CELRP and about 140 J in PRRP.
The consumed energy is dropped by a significant difference
almost 100%. The overall energy consumed by the end of
the network lifetime is efficient by both the protocols.
However, the important concern is that which protocol canachieve a longer lifetime and higher throughput for the same
initial energy level.

Fig. 07 Energy Consumptions over the number of hop.
Fig. 07 shows total energy consumption based on
simulations for different hops, where the number of nodes
were considered as of 200 with having energy level of 2 J
for each and overall energy of 400 J. The author has shown
one more aspect of the result that if the sink is placed far
from the sensing field, in that case the number of hopes will
increase from the node to the sink that increases the loss ofenergy as distance increases from the node to the sink. In
PRRP, the sink is placed in the center of the sensing field todecrease the possible number of hops among nodes and thesink and at the same time to decrease the distance among thenodes and CHs.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

593

linked with overall energy efficiency of sensor network
which can be achieved through our proposed PRRP.
C:

Comparison Analysis on the basis Throughput
This sub section describes about network throughput
which shows a substantial growth because of increase in
overall network energy efficiency. In PRRP protocol, themain energy is saved during the data transmission phasewith multiple data transmission periods by utilizing samebuild tree for multiple periods. The network survives longer
because of less distance between nodes and gateways. The
following Table 01 is showing the comparison between
PRRP and CELRP protocols in term of the data packets
delivery to the sink for different periods.
TABLE I
A comparison between PRRP and CELRP in terms of throughput

Periods

PRRP

CELRP

Improvementwith PRRP

1 DataTransmission
period

16733

5950

2.81
times

5 DataTransmission
periods

54555

19985

2.72
times

10 DataTransmission
periods

65305

35710

1.82
times

Overall, the performance of the PRRP is moresignificant than the one in CELRP. PRRP, in this case byenhancing the life of the sensor nodes and by utilizing eachsensor nodes energy resource up to the maximum level,
enhances the overall life time and energy efficiency of the
network. As seen in Table 01, the comparison of PRRP and
CELRP with different aspects is presented. The overallsignificant throughput is improved in PRRP through anapplication of it with multiple rounds of data transmission.
This improvement in fact is determined by two major
factors. The first factor is related to the increase in the
network lifetime in PRRP entirely that ensures more influxof data packet, directly giving an effect on the throughput of
the network. The second one is about our assumption thatany node in PRRP can become a gateway on the basis ofdifferent parameters, allowing the maximum nodes to be thegateways. However, gateway selection will be based on
different parameters including energy efficiency, Position
from the sink and number of neighboring nodes. Hence, theincrease in number of gateways based on stated parametersin fact, comparable with CELRP, can decrease the depth of
the tree, and also cause a good impact for the throughput of
the network. This increase of data throughput is directly

VI.

CONCLUSION

Wireless sensor network has important role and use, due
to it’s diversify approach and range of applications. WSN is
the only most suitable and easy way of deployment inremote and hard areas. Routing is the main expensive
operation for nodes energy consumption. This paper
proposed new routing protocol known as Position
Responsive Routing Protocol PRRP. Our proposed PRRP
addresses energy efficiency, data throughput and routing
hole under certain controlled conditions. Our simulation
based research results showed a significant energy
efficiency improvement of 35% to 45% in WSNs by
increasing overall energy efficiency and life time. PRRP
also shows a significant improvement of data throughput
approximately 3 times to existence protocol CELRP. PRRP
also addresses routing hole issue due to distribution
approach of gateway selection and providing chance tomaximum nodes for data transmission.

REFERENCES[1] Jamal N. Al-Karaki, Ahmed E Kamal, “Routing Techniques in WirelessSensor Network: A Survey”, IEEE Wireless Communication December
2004.
[2] Noor Zaman and A. Abdullah, “Energy Efficient Routing in WirelessSensor Network: Research Issues and Challenges” in IEEE International
Conference on Intelligence and Information Technology, ICIIT 2010. pp.
239-243[3] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A
Survey on Sensor Network”, IEEE Communication Magazine, vol. 40,
no.8, pp. 102-116, August 2002.
[4] Akyildiz, I. F., W. Su, Y. Sankarsubramaniam and E. Cayirci, A survey
on sensor networks, IEEE, Communications Magazines, vol.40, no.8,
pp.102-114, 2002.
[5] Bhardwaj, M. and A. Chandrakasan, Bounding the lifetime of sensor
networks via optimal role, assignments, Proc. of the INFOCOM’02,
pp.1587-1596, 2002.
[6] Zussman, G. and A. Segall, Energy efficient routing in Ad Hoc disasterrecovery networks, Proc. Of the INFOCOM’03, pp.682-691, 2003.
[7] Q. Cao, T. Abdelzaher, T. He, and R. Kravets, “Cluster-Based
Forwarding for Reliable End-to-End Delivery in Wireless Sensor
Networks”, IEEE Infocom’07, May 2007.
[8] Charles Pandana and K. J. Ray Liu “Robust Connectivity-Aware
Energy-Efficient Routing for Wireless Sensor Networks,” IEEE
transactions on wireless communications, vol. 7, no. 10, October 2008[9] Younis, O.; Fahmy, S. HEED: A hybrid, energy-efficient, distributed
clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput.
2004, 3, 366–379.
[10] Lian, J.; Naik, K.; Agnew, G.B. Data capacity improvement of
wireless sensor networks using non-uniform sensor distribution. Int. J.
Distrib. Sens. Netw. 2006, 2, 121–145.
[11] Olariu S.; Stojmenovic, I. Design Guidelines for Maximizing Lifetime
and Avoiding Energy Holes in Sensor Networks with Uniform Distribution
and Uniform Reporting. In Proceedings of the 25th Annual Joint
Conference of the IEEE Computer and Communications Societies(INFOCOM 2006), Barcelona, Spain, 23–29 April 2006; pp. 1–12.
[12] Wadaa, A.; Olariu, S.; Wilson, L.; Jones, K.; Eltoweissy, M. Training awireless sensor network. Mob. Netw. Appl. 2005, 10, 151–168.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015[13] Li, X.; Nayak, A.; Stojmenovic, I. Sink mobility in wireless sensor
networks. Int. Fed. Inf. Proc. 2010, 153–184.
[14] Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H. EnergyEfficient Communication Protocol for Wireless Micro-Sensor Networks. In
Proceedings of the 33rd Annual Hawaii International Conference on
System Sciences (HICSS 2000), Maui, HI, USA, 4–7 January 2000; p. 10.
[15] Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H. An
application-specific protocol architecture for wireless sensor networks.
IEEE Trans. Wirel. Commun. 2002, 1, 660–670.
[16] Khalil, E.A.; Attea, B.A. Energy-aware evolutionary routing protocol
for dynamic clustering of wireless sensor networks. Swarm Evol. Comput.
2011, 1, 195–203.
[17] Gautam, N.; Pyun, J.Y. Distance aware intelligent clustering protocol
for wireless sensor networks. J. Commun. Netw. 2010, 12, 122–129.
[18] Li, C.F.; Ye, M.; Chen, G.H.; Wu, J. An Energy-Efficient Unequal
Clustering Mechanism for Wireless Sensor Networks. In Proceedings of theIEEE International Conference of Mobile Ad hoc and Sensor System,
Washington, DC, USA, 7 November 2005; pp. 604–611.
[19] Yu, J.G.; Qi, Y.Y.; Wang, G.H.; Gu, X. A cluster-based routing
protocol for wireless sensor networks with Nonuniform node distribution.
Int. J. Electron. Commun. 2012, 66, 54–61.
[20] Buragohain, C.; Agrawal, D.; Suri, S. Power Aware Routing for
Sensor Databases. In Proceedings of the 24th Annual Joint Conference of
the IEEE Computer and Communications Societies (INFOCOM 2005),
Miami, FL, USA, 13–17 March 2005; pp.1747–1757.
[21] Singh, S.; Woo, M.; Raghavendra, C.S. Power-Aware Routing in
Mobile ad hoc Networks. In Proceedings of the 4th Annual ACM/IEEEInternational Conference on Mobile Computing and Networking(MobiCom 1998), New York, NY, USA, 1998; pp.181–190.
[22] Stojmenovic, I.; Lin, X. Power aware localized routing in wirelessnetworks. IEEE Trans. Parall. Distr. 2001, 12, 1122–1133.
[23] Li, N.; Hou, J.C. Topology Control in Heterogeneous Wireless
Networks: Problems and Solutions. In Proceedings of the 23th Annual Joint
Conference of the IEEE Computer and Communications Societies(INFOCOM 2004), Hong Kong, 7–11 March 2004; pp. 1735–1746.
[24] A. K. Sadek, W. Su, and K. J. R. Liu “Multinode cooperative
communications in wireless networks,” IEEE Trans. Signal Processing, vol.
55, no. 1, pp. 341-355, 2007.
[25] Estrin, D.; Govindan, R.; Heidemann, J. and Kumar, S., “Next Century
Challenges: Scalable Coordination in Sensor Networks ,” Proceedings of
5th annual IEEE/ACM international conference on Mobile Computing and
Networking, pp. 263-270, 1999.
[26] Rentala, P.; Musunnuri, R.;Gandham, S. and Saxena U., “Survey on
Sensor Networks ,” Available online at: http://www.mdpi.net/sensors.
[27] Wood, A. D., and Stankovic, J. A., Security of Distributed,
Ubiquitous, and Embedded Computing Platforms, to appear in Wiley
Handbook of Science and Technology for Homeland Security, Voeller, J.
G. (Ed.), John Wiley & Sons, Hoboken, NJ, 2008.
[28] A. K. Sadek, Z. Han, and K. J. Ray Liu, “A distributed relayassignment algorithm for cooperative communications in wirelessnetworks,” in Proc. IEEE International Conference on Communications(ICC), vol. 4, pp. 1592-1597, June 2006.
[29] Chang, J. H. and L. Tassiulas, Routing for maximum system lifetime
in wireless Ad Hoc networks, Proc. of the ACCCC’99, 1999.
[30] Noor Zaman and A. Abdullah,“ Position Responsive Routing Protocol(PRRP)”. In IEEE The 13th International Conference on Advance
Communication Technology ICACT 2011, Seoul Korea. Pp.644-648[31] Noor Zaman, and A. Abdullah, “Energy Optimization through Position
Responsive Routing Protocol (PRRP) in Wireless Sensor Network (WSN)”.
In International Journal of Information and Electronics Engineering IJIEE,
2012, Vol. 02, No.05, September 2012, pp. 748-751.
[32] Noor Zaman, Low Tang Jung, Fawaz Alsaade, Turki Alghamdi,“Wireless Sensor Network (WSN): Routing Security, Reliability andEnergy Efficiency”. In Journal of Applied Science, Science alert, Vol. 12,
No.16, 2012 USA, pp. 593-597.
[33] Noor Zaman and A. Abdullah, “Different Techniques Towards
Enhancing Wireless Sensor Network (WSN) Routing Energy Efficiency
and Quality of Service (QoS)”. in World Applied Science Journal (WASJ),
Vol.13, no 4, 2011, pp. 798-805.
[34] Noor Zaman and A. Abdullah, Low Tang Jung, “Optimization ofenergy usage in wireless network using Position responsive routing
Protocol” in IEEE Symposium on Computers & Informatics ISCI 2011,
Malaysia, pp. 51-55.

594[35] Ponnusamy, V., Abdullah, A., and Downe, A. G. (2012). Energy
Efficient Routing Protocols in Wireless Sensor Networks: A
Survey. Wireless Sensor Networks and Energy Efficiency: Protocols,
Routing, and Management, 237-261.
[36] Zaman, N, Tung Jang Low, Alghamdi, T: Energy efficient routing
protocol for wireless sensor network in 16th International Conference on
Advanced Communication Technology (ICACT), 2014 2014; pp. 808-814.
[37] Nurhayati, Sung Hee Choi, and Kyung Oh Lee : A Cluster Based
Energy Efficient Location Routing Protocol in Wireless Sensor Network, inInternational Journal of Computers and Communications, issue 2, Volume
5. 2011. Pp, 67- 74.
[38] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “AnApplication-Specific Protocol Architecture for Wireless Microsensor
Networks”, IEEE On Wireless Communications Trans., vol. 1, no. 4, Oct.
2002, pp. 660-670.
[39] Laura Marie Feeney, Martin Nilsson, Article: Investigating the Energy
Consumption of a Wireless Network Interface in an Ad Hoc Networking
Environment. IEEE Infocom, 2001.

Dr. Noor Zaman acquired his degree inEngineering in 1998, and Master’s in Computer
Science at the University of Agriculture at
Faisalabad in 2000. His academic achievements
further extended with a PhD in Information Technology at University
Technology Malaysia (UTP). He has vast experience of 15 years in the fieldof teaching and research. He is currently working as a Faculty member at
the College of Computer Science and Information Technology, King Faisal
University, in Saudi Arabia.
He has contributed well in King Faisal University for achieving ABET
Accreditation, by working as a member and Secretary for Accreditation andQuality cell for more than 07 years. He takes care of versatile operations
including teaching, research activities, leading ERP projects, IT
consultancy and IT management. He headed the department of IT, andadministered the prometric center in the Institute of Business andTechnology (BIZTEK), in Karachi Pakistan. He has worked as a consultant
for Network and Server Management remotely in Apex Canada USA base
Software house and call center.
Dr. Noor Zaman has authored several research papers, and edited fiveinternational reputed Computer Science area books, has many publications
to his credit. He is an associate Editor, Regional Editor and reviewer for
reputed international journals and conferences around the world. He hascompleted several research grants and currently involved with funded
projects in different courtiers. His areas of interest include Wireless Sensor
Network (WSN), Network, Cloud Computing, Big Data, Mobile
Computing, Software Engineering, Unix, and Linux.

Dr. Low obtained his Bachelor of Engineeringin Computer Technology from Teesside
University, UK (1989), MSc IT from National
University of Malaysia (2001), and his PhD in
IT from Universiti Teknologi PETRONAS(UTP), Malaysia (2012). He is currently a
Senior Lecturer in the Computer andInformation Sciences Department of UTP.
He has been in the academic line for two decades already, mainly as
lecturer cum researcher in various public and private institutes of higher
learning. He teaches various engineering and ICT courses. His research

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

595

interest includes wireless/mobile technology, embedded systems, wirelesssensor networking, robotics, and Green IT. His current R&D include: bioinspired energy efficient routing in WSN, sensors location optimization ingreen IT, and green by IT.

Turki Alghamdi graduated with B.Sc. degree (withhonors) in computer science from the King Abdulaziz
University, Jeddah, Saudi Arabia, in 2003. He wasawarded M.Sc. degree in Distributed Systems andNetworks from the University of Hertfordshire,
Hatfield in 2006. In 2011 he received his Ph.D. degree in Computer
Science from the University of Bradford, Bradford, United Kingdom. In
2003,
He joined the Department of Computer Science, University of Umm AlQura, as an Assistant Teacher. Since August 2011 he has been working forthe Department of Computer Science as an Assistant Professor. He is acurrent administrator of recently established smart networks laboratory in
the Computer Science Department. His current research interests include
computer networks and Wireless Sensor Networks.

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596

Optimization of Convenience Stores’
Distribution System withWeb Scraping and Google API Service
Thai Quang LE*, Davar PISHVA***International Management, Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu, Oita 874-8577 Japan**Asia Pacific Studies, Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu, Oita 874-8577 Japan
quanle11@apu.ac.jp, dpishva@apu.ac.jp
Abstract—Vehicle Routing Problem (VRP) has never become
an obsolete research theme in the field of operations researchand supply chain management. Considering that significant
number of researchers have already tried addressing VRPs withmathematical modeling and algorithmic approaches, this paper
focuses on a practical implementation and employs
programming techniques to cope with a particular businessproblem in convenience stores’ distribution system. It optimizes
goods distribution process of convenience stores business, which
involves lorries delivering products from a warehouse to a
network of several convenience stores in a single trip, collecting
their garbage, passing by a gas station for re-fueling whenneeded, and returning back to the warehouse. A mathematical‘network flow model’ is initially developed to examine theproblem. Geographical data of convenience stores, theirassociated warehouses, garbage dumpsites and gas stations aresubsequently retrieved through programming with the ‘webscraping’ technique. A prototype of web-based delivery
navigation system that utilizes Google API service is thendeveloped to solve the optimal convenience stores’ networking
problem. Furthermore, a more general perspective of the
problem is illustrated with cluster-first-route-second heuristic
algorithm and a mobile version of the prototype, which canserve as a real time navigation system for delivery truck drivers,is developed. Validity of obtained results is also examined byother known methods to justify optimality and fast performanceof the approach.
Keyword—Assignment, Google API Service, Maximal
Covering, Modeling, Optimization, Supply Chain, Web
Scraping.

I. INTRODUCTIONI

t is fair to claim that profit maximization is the ultimate goalof any commercial firms. In a retailing business thatcomprises of thousands of stores or stations, even a small
reduction in product cost can have a big impact on not only———————————————————————
Manuscript received on November 21, 2014. This work is a follow up ofan accepted conference paper for the 17th International Conference on
Advanced Communication Technology.
Thai Quang LE is a student at Ritsumeikan Asia Pacific University(APU) in the field of Business Administration focusing on Strategic
Management and candidate for graduation.
Davar Pishva is a professor in ICT at Ritsumeikan Asia Pacific University(APU) Japan (corresponding author: +81-977-78-1000, fax: +81-977-781001, e-mail:dpishva@apu.ac.jp).

the business itself, but also to the nation’s economy and social
well-being as a whole. Over many decades, there have been
many profound researches and proven practices suggesting
various ways to make business more lucrative, but most ofthem focus on effective resources allocation and enhancing
productivity, which then lead to production cost reduction.
Moreover, in our push-button world where everythingchanges so quickly along with the high-pace development oftechnology, machines are gradually replacing the human rolein several aspects, such as manufacturing procedures andbusiness administration. Since potential effects of technology
on business has not fully been exploited and more and moreadvanced and pragmatic technology is being invented by anincreasing flow of capital investment in research anddevelopment (R&D) on a global scale, technology is believed
to be one of the decisive keys in business competition today.
Furthermore, apart from profit maximization, more andmore businesses are presently paying serious attention tocorporate social responsibility (CSR), especially inenvironmental aspects. It has been a common sense to utilize
the perspective of management in different aspects of doing
business, and one argument for explaining this is that mostbusiness factors such as human capitals, production cost,market sales, etc. are measureable by human beings. However,recently technology has also enabled us to measure different
types of environmental factors such as amount ofcarbon-dioxide emission and so on that are resulted fromprolong business process. Data from such measurements
force business managers to take environmental factors into
consideration and lead the business with a more balance
strategy between profit maximization and social
responsibility. The challenge here is how to help business stay
more lucrative, and at the same time remain friendly to thesociety and the environment. This is also the question that thispaper would like to address, particularly in convenience store
retailing business in Japan for which a systematic
technological approach is suggested as an answer.
The remainder of the paper is organized as follows: Section
2 addresses delivery route optimizing perspective of
convenience stores’ distribution problem and some related
previous work. Section 3 then delivers a practical solution toenhance the distribution process of a network of convenience
stores, which includes network flow modeling, data collection
by web-scraping technique, multi-objective route
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

optimization with Google Map API and Evolutionary
Algorithm in Excel, and an easy to follow Graphic User
Interface (GUI) implementation on web-based mobile
platform. Section 4 proceeds with detailed discussion andanalysis of the research result. Sections 5 subsequently
indicate some current limitations and potential future researchand conclude the paper.
II. LITERATURE REVIEW
A. Convenience Store Business in Japan
Considering that Japanese people is famous for tirelessdedication to working-life, convenience store turns out to be
one of the key factors in timesaving for the majority ofJapanese and lead to higher overall working productivity ofthe society. Due to the increasing dependency of Japanesesociety on convenience stores, growth in this retailingbusiness is also a reflection of the Japanese present living
standards [1]. Japan’s convenience stores are well known forcatering not only fruitful types of beverages, safe and healthy
foods [2], but also to a plenty of in-store services, namely
banking, printing, public utility payment (gas, telephone,electricity, etc.), ticket reservation for concerts or sport games
and so on [3]. However, it is also claimed that not just themerchandising factor, but also time beneficial factor of
convenience stores is important to the Japanese daily life [1].
To illustrate this, since most Japanese visit stores on foot [1],a high-density of roughly over five stores per kilometer,
which can be much higher in metropolitan areas, enables
anyone to access a store in less than 10 minutes on foot on
average. Thus, it is reported that approximately sixty percent
of customers visit the same store twice a week or more [2].
Convenience store business is one of the most profitableretailing businesses in Japan, covering 36% of the Japan
Retail Food Market in the year 2012 [4-5]. With theincreasing total of sales from roughly 8.7 trillion yen in theyear 2011 to about 9.4 and 9.8 trillion yen in the year 2012
and 2013 respectively [4]. Regarding the number of stores,
there are over 50,000 convenience stores located throughout
47 prefectures of Japan and the number of new establishments
has been continuously increasing in recent years; from 43,373
stores in 2011 to 47,801 and 50,234 stores in 2012 and 2013
respectively. Most of the convenience stores are establishedin the metropolitan Kanto area, with more than 20,000 storesin the year 2013; and Okinawa has the lowest number ofconvenience stores, about 500 stores in 2013. Since most of
the convenience stores learn the system from supermarket
management experience, with their limitation of the small
store sizes, convenience stores business is adapting many ofstate-of-art technologies, especially Electronic-OrderingSystem (EOS) in collecting and analyzing Point-Of-Sales(POS) data in order to reduce effectively product delivery
time from the wholesalers to each store [1, 6].
B. Convenience Store’s Distribution Problem
The price charged for a similar item being sold in
convenience store is usually more expensive (by about twenty
percent) than in local supermarkets or mass merchandiser [7].
This overhead cost is considered as charges for theconvenience values perceived by the customers. To illustrate,597

one can both purchase foods for dinners and pay the electricbill at a nearby convenience store, instead of passing by both asupermarket and a power utility office, which are not usually
located in the same neighborhood. However, from theperspective of business managers, the overhead cost of
convenience stores is mostly resulted from overnight
operation and expensive merchandise distribution process.
In Japan, convenience stores maintain long business hours,
usually beginning at seven in the morning and stay open until
eleven at night. Because the average salary for a night-work
shift (from six until midnight) is sometimes three times higher
than the normal working hour, the convenient operating hours
raise an additional cost for the business. Additionally,
considering limited available land area in its vicinity,
convenience store in Japan is usually defined as self-service
store with average of 100 to 150 square meter floor space [1].
Because of such small size, convenience stores in Japannormally do not practice inventory, and a well-managed
product distribution system is essential to the businessdevelopment [1], which is called just-in-time delivery.
Particularly, all of the products are distributed by trucks froma distribution center (warehouse) to all of the convenience
stores on a regular basis. For example, 7-eleven convenience
stores implement a combined distribution system bytemperature zones by which rice, chilled products are being
delivered three times per day, frozen and beverage products
are being delivered three to seven times per week [2]. Because
products have to be delivered to thousands of convenience
store on an average 9 times per day (in the case of 7-elevenstores), the cost for hiring truck drivers and gas consumption
rise up. And this expensive distribution process is believed tobe the biggest overhead cost associated with the price.
Due to the limitation of floor space, convenience stores do
not have the capacity to do inventory. Hence, in order to meetcustomer’s needs, many of the convenience stores nowadays
practice just-in-time distribution system, by which the
products are only being delivered when needed. Therefore, ona daily basis, convenience retailers have to deliver goods bytrucks from a distribution center to several stores, fill-up thegasoline when necessary, collect all the garbage, and dump
the collected garbage at a designated dumpsite; and this
distribution patterns have to be repeated many times per day.
Throughout the years, convenience store business has foundmany ways to enhance the situation. For example, 7-elevenconvenience stores pioneered the so-called temperatureindependent distribution system by which mixed categories
are combined before being loaded onto trucks so as toincrease payload efficiency and tremendously decrease thereplenishments of goods per day [2]. Nevertheless, in order toserve millions of people per day, still thousands of delivery
vehicles, mainly trucks and mini vans, at least 3,799 of which
belong to 7-eleven, are operating everyday and raising both
economic and environmental concerns.
Hence, this research approaches the convenience stores’
distribution problem as a vehicle routing optimization
problem, which proposes a systematic way to minimize thetotal truck delivery time duration in networks of convenience
stores, resulting in both financial and environmental savings.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

C. Related Works
There have been a considerable number of research
investigating vehicle routing problems (VRPs) in various
fields, which can be considered as linear optimization withdifferent operational objectives and constrains. In fact, many
real-life VRPs comprise of huge number, sometimes hundreds
or thousands, of nodes in a networks and it is very
time-consuming as well as computationally expensive toobtain an exact optimality in the result. Because of such large
scale and demand for multivariable constraints and decisionparameters, most of the proposed solutions for VRP are basedon heuristics, which can be classified as classical heuristics
and meta-heuristic [8]. Just to cite, some of the classicalheuristics are Clarke and Wright (1964) [9], Sweep, Petal, etc.
Meta-heuristics include evolutionary algorithm [10],Taburoute [11], adaptive memory based tabu search [12], antcolony optimization [13], hybrid cooperating meta-heuristics[14] and so on.
There have also been several researches that implement or
extend those algorithms in empirical businesses that practicerouting optimization. A decision support system proposed by
Lin, Choy, Ho, Lam, Pang, Chin (2014), which also considers
level of service as a cost function and has a prototype on
mobile-client for courier operation to minimize the total
traveling distance [15], is one such example. Unlike
convenience stores distribution problem, this problem ofcourier service involves the continuous alternation in thecustomer orders, making dynamic routing and flexible time
windows as the main concentration of the research. Anotherrecent VRP example is pickup and delivery of customers to
the airport, which is examined by Tang, Yu, Li [16]. The
research makes use of the CPLEX software to implement an
improved label-correcting method to remove infeasible routes
to speed up the route search process. A related work had also
been carried out by Li, Chen, Sivakumar and Wu [17], which
involves optimization of travel time for inventory routing inpetroleum business. Due to the nature of the business, newconstrains such as hours-of-service regulations of the industry,
etc. were considered. Waste collection problem is anothertype of VRP concentrating on balancing the workload overtime by optimizing the decision on container selection androuting, obtaining a cost savings of up to 40% [18]. Due to theuncertainties in traffic congestion and the importance of time
management, several researchers have also incorporated time
windows as constrains in building the model of the VRP. For
example, a research done by Zarandi, Hemmati, Davari, andTurksen (2013) on the location-routing problem with time
windows under uncertainty inspects the fuzzy attributes ofdemands of customers and travel time [19]. Tas, Jabali,
Woensel (2014) also introduced a solution procedure to
resolve the vehicle routing problem with flexible time
windows, in which the customers can be flexibly served
before or after a preset tolerable time frame in order to save
operational cost [20]. In 2010, in order to tackle with driving
hours regulations, a research on departure time optimization
for VRP was also proposed by Kok, Hans, and Schutten(2011), which claim to reduce 15% of duty time of truck
drivers with the newly introduced algorithm [21]. Regarding
convenience store business, Bhusiri, Qureshi, and Taniguchi

598(2013) proposed a perspective of convenience stores’
distribution as a “VRP with soft time windows andsimultaneous pickups and deliveries”, which focuses on using
branch-and-price approach and evolutionary algorithm tooptimize the problem with delivery arrival time restriction
and tests with a public benchmark test set [22-23].
Because of the limited real business data availability,
despite the countless investigations in the VRP, many of them
are short of testing the approach on empirical data or realcases. Hence, just few of the related previous works such as[15, 24] can demonstrate a working prototype where theproposed algorithms actually work on practical geographicalinput data. Furthermore, even though most of the proposed
approaches are recognized to have the ability to solve a
specific problem on mathematical model, usually thesolutions are very complex and expensive in terms ofcomputation.
D. Research Objective
This paper wants to put a forward into the field byintroducing a practical methodology to optimize the routingof convenience stores’ distribution process, especially inJapan. By utilizing the web-scraping technique to collectgeographical data of all of the convenience stores and gas
stations in Japan (nearly 100,000 real locations), this researchaims to propose a practical solution and benchmark withwhole population of real data, rather than mathematical theory
and synthetic simulation.
Specifically, the research implements systematic computer
software and a working web-based application so as to havethe postal code of a distribution network entered by a user,
and optimize traveling time of drivers delivering products
from a warehouse to the network that includes up-to-8
convenience stores, pass by a gas stations on the route whenneeded, collect all the garbage and return to the warehouse tocomplete the process. In this problem, we recognize thepractical need of searching for an optimal location of a gas
station for the delivery trucks’ re-fueling along the trip, whichto the best of our knowledge, has not been introduced in theliterature so far.
Moreover, the software is further tested with convenience
stores networks of over 1500 areas (with different postal
codes) in Oita prefecture where the authors reside.
Furthermore, industry-proven Google API service is also
introduced in order to ensure the retrieval of the real-timetraffic data used in the optimization process, as well as toreduce the dependence on high-performing computational
infrastructure. Despite the fact that no time window
constraints are introduced in the optimization process, theoutput result already includes all of the estimation of
travelling duration under real-time traffic condition, hence
facilitating a confident measurement of appropriate departureand arrival point of time.
Additionally, the cluster-first-route-second ideology,
which has been proposed by many researches in optimizing
multi-depot VRP [25-26], can be used to cope up with theconvenience stores’ distribution problem. Because in real-life
practice, prior to optimizing the product delivery routing of acertain convenience stores network, a process of clustering allof the stores located within a region (city or prefecture) into
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

599

small distribution networks in a way that can minimize
comprehensive travelling duration are necessary. Even
though the paper concentrates more on the later one in theworkflow, which is optimizing a certain convenience store
network, a proposed solution using network modeling and
Evolution algorithm on Solver add-in of Microsoft Excel[27-28] is also demonstrated to optimize the convenience
stores clustering in the first procedure.

III. METHODOLOGYThis section examines the distribution problem of
convenience stores as a specific case of vehicle routing
problems with the objective of finding an optimal delivery
route that minimizes gas consumption by reducing thetravelling time of trucks distributing goods from thewarehouse to several convenience stores, passing by a gas
station to refuel and coming back to the warehouse. Different
from method approaching the original problem of theTravelling Salesmen Problem, the approach proposed here
focuses more on the actual travel time, rather than physicaldistance optimization.
A. Network Flow Model
1) Graphical Model of the Problem
The convenience stores’ distribution optimization problem
is examined as a “Network Flow Problem” by assuming eachof physical locations in the distributing routes as a node in the
network, distance and travelling duration as weights of arches.
The network’s optimization objective is therefore to find theflow connecting every node that associates with the optimaltransportation time. A graphical network flow model of asimple convenience store distribution problem involving awarehouse, and 4 convenience stores can be represented asshown in Figures 1, 2 and 3.

Fig. 1. Each location in the problem is demonstrated as a node in the
network flow model.

Fig. 2. The arc connecting two nodes represent the physical distance, or thetravelling time needed to move between the two respective locations.

Fig. 3. Blue lines represent the flow connecting nodes and arcs, showing oneof many possible routes that can help to achieve the distribution process.

2) Linear Programming Model of the Problem
A linear programming model of the stated convenience
stores’ distribution problem of a specific stores network canbe represented as shown in Figure 4:

Fig. 4.
Mathematical Representation of Network Flow Model ofConvenience Store Problem.

In order to solve this Network Flow optimization problem,
collecting information about geographical locations ofconvenience stores, gas stations, warehouses and garbage
dumpsites, as well as the distance and travel time between
these locations is indispensable.
B. Data Collection with Web Scraping
To solve the distribution problem of convenience store, i.e.,
the Network Flow problem above, location data of all of theconvenience stores, gas stations, as well as the physicaldistance and travelling time between those places arenecessary. Thus where to find trustworthy sources and how to
obtain the data are questions that are discussed in this sectionof the paper.
1) Data Source
NAVITIME website is established by the NAVITIME
Japan Company, a Japanese online portal service focusing on
events and places. NAVITIME website presents a friendly
GUI (http://www.navitime.co.jp), enabling user not only
access detailed information but also navigate routes between
thousands of places classified into several categories such as
convenience stores, gas stations, restaurants, parking places,
schools, clinics and so on. Furthermore, the website also
updates newly established spots in Japan, making it morereliable as a references source [29]. As far as data needed tosolve the convenience store business problem, the
NAVITIME website has lists of almost all of the convenience
stores located in Japan, with details such as the store name,address, telephone number, and even the information
regarding whether the store sells alcohol, cigarettes or fresh
vegetables. Moreover, the website also classifies the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

convenience stores based on their location in a prefecture orcity (e.g., Oita, Beppu) and brand names (e.g., 7-eleven,Lawson, etc.), making it very convenient to find the needed
data. Hence, we have selected the NAVITIME website as themain data source for retrieving the geographical informationof all of the convenience stores, gas stations in Japan.
2) Collecting Data from NAVITIME Website by Web
Scraping
Web scraping or web crawling techniques are usuallyprogrammed to interact with a webpage source and extract
data stored in it, and “usually, the extracted data might bepost-processed, converted in the most structured format andstored for further usage” [30]. This research mainly relies on
NAVITIME website for its data source, and web scraping
programming technique as the tool to extract the data from thewebsite. Despite the fact that NAVITIME website contains
the necessary information for the convenience store
distribution problem, the website is only built for normal
users who would utilize web browsers to retrieve information
on a single physical spot, convenience store or gas stations inthis case. In other words, it would require huge amounts ofefforts to manually surf the website and collect all of the data
for nearly 100,000 locations of convenience stores and gas
stations in Japan. Therefore, a systematic and automaticmanner of retrieving the data from the website is
indispensable, and web scraping technique is undeniably themost promising tool that can help us achieve this.
In order to achieve this, we developed a web-scraping
algorithm for crawling data from the NAVITIME website in
Ruby programming language and Figure 5 shows its pseudocode. The website categorizes all the convenience stores into
different prefectures in Japan, and after accessing each of thelink of those prefectures, the website shows all the links, eachof which connects to a specific convenience store’s
information page, sorted into different listing pages.
Therefore, a looping algorithm is used to access thishierarchical structure, which simultaneously extract all theneeded information of convenience store such as the store
name, geographical address, telephone number, and details
regarding the availability of fresh vegetables, alcohol orcigarettes, etc., and store them into a local comma-separated
values (CSV) file.

Fig. 5. Pseudo code of web-scraping algorithm for the NAVITIME website.

C. Multi-objective Route Optimization with Google API
1) Google API Service
Google API is a well-known web service that provides
online tools for developers who utilize Google’s data andplatform. Google API consists of several programming

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libraries serving many areas such as Mapping, Advertising,
Cloud storage, etc. Google Direction API is one such service
that helps retrieve geographical distance and travelling timefrom one physical location to another. Moreover, the freeversion of such service also helps to calculate the optimalroute in terms of time to travel through a maximum of 10
physical locations (including the origin and destination
locations) [31]. Since Google Direction API operates on the
Google server by receiving requests from the users andreturning back the results, we can easily utilize thewell-established platform of Google server.
Due to the academic nature of this research, we utilized thefree version of the Google API service to solve the statedproblem despite its limitations. For commercial application,
however, the Work version of the service is recommended
since in addition to a more generous number of request
permits, it also provides the travelling duration and calculates
optimal route under real-time traffic conditions, somethingwhich can further ensure optimality of the approach.
2) Optimize Convenience Stores Distribution Routing
with Google API
In order to use Google API service, a structural request for
fulfilling parameter requirements need to be sent to theGoogle server through a specific URL, which is designated by
the service. Consequently, Google API will return the result ina response package. A typical request to Google Direction
API to find an optimal travelling route for a trip includes:
 Origin Point: The origin location point from where thetrip starts: e.g., the warehouse or distribution center.
 Waypoints: A list of all geographical points to pass-byduring the trip: e.g., all the convenience stores in the
network and a nearby gas station.
 Destination point: The final destination point where thetrip ends: e.g., the warehouse or distribution center.
In order to find the optimal location of a gas station to
fulfill the possible re-fueling requirement of the delivery
vehicle, an iterative algorithm has been built. Within thecollected data of all gas stations in Japan, we first want to finda list of all gas stations located within the same region as the
distribution network of convenience stores. This task is
obtained by looking up the area name of the distribution
network (e.g. prefecture, city and town name) from the input
postal code through a web API service (i.e.,
http://zipcloud.ibsnet.co.jp), and then the result is used as the
keywords for searching relevant gas stations in the collected
data. After that, each of the returned gas stations is inserted
into the request waypoint parameter of Google API service,
one by one, and from the returned routing results the optimal
one is selected. This route then becomes the optimal one thatpasses through all the convenience stores in a desired network
and at the same time drops by the gas station for re-fueling.
D. Cluster-first-route-second
Cluster-first-route-second is a heuristic method for
optimizing large scale VRP, which has two separate phrases,
namely clustering and routing. Originally, the algorithm wasintended to address problems with vehicle capacity
constraints. However, this idea of classifying all the
waypoints into different clusters before optimizing the routehas been adopted in many practical research questions suchas in VRP with Backhauls [32].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

In convenience stores business, daily-consumed products
have to be delivered to every store on a regular basis. For
example, 7-eleven convenience stores have rice and chilled
products delivered three times per day, and drinks and frozen
foods delivered three to seven times per week [2]. Hence, allof the customers or convenience stores in this case, have thesame orders at similar time intervals during a day or a week.
This implies that distribution network of a distribution
manager who is in charge of hundreds of stores within a city
or a prefecture is usually much larger than a chain of
convenience stores that can be handled by a single delivery
truck. Applying the idea of cluster-first-route-second
approach will allow distribution managers to first cluster all of
the stores that fall within their delivery network into manysmall networks in a way that can both satisfy total number ofavailable delivery trucks and optimize the total travel time
durations of their respective network. In this section we have
utilized free Google Distance Matrix API to obtain the travel
time matrix among convenience stores, a sample result of
which are shown in Figure 6, and used Evolutionary
Algorithm of Solver add-ins on Excel for solution
demonstration. As an example, two networks consisting of all
the 7-eleven and Family-Mart convenience stores in Beppu
City, Oita Prefecture has been selected to illustrate theapproach.

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Fig. 7.
Clustering with 4 groups (black, purple, blue, and red) andnumerical routing order of network consisting of all of fourteen 7-elevenconvenience stores located in Beppu City, Oita Prefecture.

Fig. 6. Travelling time duration matrix of network including a warehouse at
Beppu station, and fourteen 7-eleven convenience stores in Beppu City.

Fig. 8. Clustering with 4 groups (black, blue, green, and purple) andnumerical routing order of network consisting of all of 16 Family-Mart
convenience stores located in Beppu City, Oita Prefecture.

The Evolutionary Algorithm with default parameters
setting is run on linear programming models manually built in
Excel. The optimization objective is set to minimize the total
traveling duration in the whole network with a single
warehouse. Decision variables show the total stores in eachgroup, the classification of each convenience store, as well as
the traveling order. Constraints of the problem are theconstant number of available delivery trucks (or the total
number of groups), and an assumption that each groups must
contain more than two stores. Moreover, the warehouse isassumed to be located at the central station of the city.
For better illustration, visualization Google maps are
generated by an online tool (https://mapbuildr.com) to show
the clustering and routing result of the real-life networks of
7-eleven (14 stores shown on Figure 7) and Family-Mart (16stores shown on Figure 8) convenience stores in Beppu City,
Oita Prefecture. Moreover, each cluster is marked with
distinguish colored dot on the map with the travelingnumerical order within the cluster being placed inside, and the
big balloon-shaped red mark represents the warehouse.

this work is to also build a practical mobile application that
can interact with truck drivers in a programmatic manner andenable them to input any references regarding a specificconvenience store distribution network. The device should
then automatically run the underlying procedures all the way
from finding the necessary data to sending requests to Google
API service and retrieving the calculated optimal delivery
routes through an easy to follow GUI in a real time fashion.
This way, the mobile device of the truck drivers can serve as
navigation system for the delivery network, calculate optimaldelivery route in a continuous fashion and update the truck
drivers about a possible re-route option when deemednecessary. To achieve such an objective, a prototype of
web-based mobile application and local web server have been
established and developed using PHP, HTML5, JavaScript on
Angular Framework, and MySQL database management andthe following subsections highlights its main features.
1) Navigation System Structure
A block diagram of the envisioned system is shown in
Figure 9. It is a large online network consisting of a web

E. Web-based Implementation on Mobile Platform forContinuous Routing Updates
Although at the cluster-first-route-second stage, optimaldelivery sequence of stores assigned to each delivery truck is
calculated, the situation may change during the actual
delivery time due to traffic congestion. The ultimate goal of Fig. 9. Structure of the proposed navigation system.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

server and various clients, which are mobile devices running
the web-based application and available in each delivery truck.
The web server’s responsibilities include managing theretrieved database and sending geographical location of
convenience stores and gas stations located in a requested
distribution network, or geographical region. Because of thecentralization of the server’s database, the system canmaintain consistency of response to every client in case there
are modifications to any of convenience stores’ information.
This way, clients will act as bridge to search for convenience
stores between the user and central sever. Furthermore, themobile devices installed in the delivery vehicles will support
the drivers by displaying an interactive navigation map, which
is based on the familiar Google Map platform, illustrating its
familiar perception of up-to-date optimal route for acustomizable distribution network.
2) Graphical User Interface & Functions of Client
Application
Mobiles are selected as target devices due to their built-in
GPS functionality, which can facilitate real-time navigation
tracking for the equipped delivery trucks. As such, the processof route optimization can be done not just statically before
starting the trip, but also dynamically while the truck ismoving. This way, the remaining route gets continuously
optimized based on the current traffic condition.
The two figures 10 and 11 demonstrate the developed
web-based application running on iOS simulator. Figure 10
shows the page requiring necessary input from the user forsearching a list of convenience stores located within a specificregion, which is specified by postal code. In this GUI,
warehouse location, as well as the intention to re-fuel is also
customizable. Figure 11 gives an example of the search result
page, letting the user to select up-to 8 convenience stores (7 inthe case of passing by a gas station for re-fueling) into theirdistribution network. Then, Figure 12 shows the result of anavigation map, guiding the distribution route with numberedmarkers (Gas station marker has “S” suffixed), representing

Fig. 10. GUI for showing search result of convenience stores located in an
input area (through postal code).

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Fig. 11. GUI for showing search result of convenience stores located in an
input area (through postal code).

the travelling order, which starts from “0” at 1st delivery
point.
As the delivery truck moves, the application automaticallyverifies whether it has reached a predefined place (e.g. Figure11) on the route or not. This way, the application has thecapability to continuously build a new network, which
consists of the current location as new origin, and remaining
stores and a gas station (if the truck has not yet passed by one)as the waypoints, without any manual input from the driver.
Whenever the driver clicks the “Update” button shown on
Figure 12, a new route is also immediately calculated together
with the latest traffic condition from Google API service. As

Fig. 12. GUI showing the navigation map with the optimal route calculated
and shown. The blue dot represents the current location of the truck, andgreen marker labeled “P” indicates if the truck has already passed thelocation.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

Fig. 13. GUI showing the updated navigation map for optimal route startingfrom the current location of the truck and passing by all the remaining
convenience stores from the initial network.

shown in Figure 13, after the driver has passed the firstlocation and is on the way to the next convenience store(location numbered 1 in Figure 12), a new route is calculated.
Please note that in addition to the renumbering of theremaining delivery nodes, optimal gas station location has
been altered from previous location labeled “3S” in Figure 12
to a new location labeled “4S” in Figure 13 due to current
traffic situation.
IV. ANALYSIS AND DISCUSSION
This section provides a thorough analysis of the obtained
results and discusses the advantage of implemented
methodology in some details. Initially, the envisioned positive
impacts of the obtained result are examined from both
economical and environmental perspectives. Then somefigures illustrating the outcome of the data collection
procedure is demonstrated. Several verification test results on
the feasibility of the developed computer software are also
illustrated. Finally, accuracy and performance of the proposed
approach is compared with other well-known methodologies.
A. Commercial and Environmental Perspective
To examine the economical and environmental benefit of
our approach, we collected some relevant data on
convenience stores in Japan (e.g., the total number ofconvenience stores, the average number of deliveries per day,etc.) and made several assumption scenarios. For each of the
scenario, financial cost saving and carbon dioxide emission
prevention were calculated with the aid of online gas
consumption
calculator
toolat
http://www.city-data.com/gas/gas.php, which also considers
the gas consumption differences between city roads andhighways.
Collected Data and Pertinent Assumptions: Number of convenience stores in Japan: 56,925
 Average number of deliveries needed to a single store
per day: 9
 Average number of convenience stores in a single

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distribution network, or average number ofconvenience stores to which a single truck has todeliver a day: 8
 Average hourly salary of a single truck driver: 900
Japanese Yen Average fuel cost per gallon: 115 Japanese Yen Miles per gallon in city: 30
 Miles per gallon in highway: 40
Based on these assumptions, an example network of eight
7-eleven stores in Beppu City, Oita Prefecture was initiallytested with the program. Using the obtained result and bycomparing two possible trips with a maximum and minimum
travel distance, a heuristic estimation of maximum cost saving
and carbon dioxide emission prevention for every
convenience stores network in Japan is estimated. Our findingshows that approximately 2.8 billion Japanese Yen can besaved per year, which is equivalent to 56 million tons ofcarbon dioxide, or roughly 7.8 million homes’ electricity forone year. Even though this estimate may not be precise due tolocation disparity of convenience stores throughout Japan, we
can still see the huge impact of the approach in real life
practice.
B. Data Collection and Program Testing Result
1) Data Collection
Informational on about 57,000 convenience stores androughly 40,000 gas stations was collected using web data
scraping technique on the NAVITIME website(http://navitime.co.jp). Table 1 shows a summary result of thespecific number of physical places, total time needed for data
extraction, and actual size of the CSV files in which theextracted data are stored:
TABLE IEXTRACTION TIME OF CONVENIENCE STORES AND GAS STATIONS’ PERTINENTDATA USING RUBY SCRIPT AND NOKOGIRI LIBRARY
Data
Number of Locations
Extraction TimeCSV File Size

Convenience Store
56,92596 minutes
16.3 MBGas Station
36,66080 minutes
14 MB

Considering that our analytical approach depends on theextracted information on all the available convenience storesand gas stations in Japan (nearly 100,000 physical locations in
total), the data crawling had to be done on nearly 100,000 web
pages. This may give an impression that the above mentioned
processing time for data extraction is quite expensive.
Nonetheless, because all of the extracted information, e.g.,store names, addresses, etc. is almost fixed and does not easilychange; the created CSV file can be subsequently used as
database for optimization for quite sometimes and enable thesystem to operate in real time fashion. Therefore, theextraction time of one and a half hours for a periodical updateof the database can be considered practically acceptable.
Furthermore, despite some limitations of the CSV fileutilization for data storage which is discussed in the limitation
section, the CSV database file size of about 15 MB is feasible
for a typical ruby program to execute any necessary reading or
writing operations.
2) Program Testing
In order to test correct operation and reliability of a newly
developed program, we initially used it to gather various dataon roughly 56,925 convenience stores and 39,660 gas stations

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015that are located throughout Japan. We then focused ouranalytical modeling approach to convenience stores that arelocated in Oita prefecture. Our finding shows that out of 1,765
different districts in Oita prefecture that have a unique postal
code, (a complete list of which can be accessed from the link
http://homepage1.nifty.com/tabotabo/pzips/oita.htm), in 754
or 42% of them there exit convenience stores. We thencreated examples of convenient store networks with up-to 8
stores per network for supply and garbage collection services
and in about 95% of them the program could come up with an
optimal routing solution in less than 1 minute.
C. Accuracy & Performance
In general, routing optimization problem requires muchtime and computational resources in order to come up with afeasible solution. In this particular case, since optimization
problem consisted of a network having a warehouse, up to 8
convenience stores, and a garbage dumpsite, our algorithm
had to examine a total of 40,320 possible routes indetermining an optimal solution that could yield the least
travel duration or distance. Even though there are many
proven algorithms such as Evolutionary or Generic algorithm
to solve the routing optimization problem, their analysis in
general takes longer time.
This was realized when we validated optimality of ourresult with that of Evolutionary algorithm built into Excel’s
Solver Add-in function using its default settings. Althoughboth methods yielded the same result in terms of optimality,
performance of our approach which relies on Google API ismuch faster than that of the Evolutionary algorithm built into
Excel’s Solver Add-in function, as shown in Table 2. Despite
the fact that free version of Google API service is limited to a
network of 10 nodes, its enterprise version can handle a
network of up to 25 nodes per request.
TABLE IIPERFORMANCE COMPARISON BETWEEN GOOGLE API SERVICE AND
EVOLUTIONARY ALGORITHM OF EXCEL SOLVER ADD-IN
Evolutionary
Algorithm in Excel
Testing Network
Google API Service
Solver Add-in(approximately)
Network of 6 locations
42 seconds
Less than 10 seconds
Network of 7 locations
51 seconds
Less than 10 seconds
Network of 8 locations
52 seconds
Less than 10 seconds
Network of 9 locations
53 seconds
Less than 10 seconds
Network of 10 locations
58 seconds
Less than 10 seconds

D. Cluster-first-route-second Performance
There have been many different ways approaching thecluster-first-route-second heuristic in solving VRP. However,many of them failed to consider the traveling time duration
based on real-traffic condition at the time, something that is
crucial for true optimization. Our test results shown in Figures
6 and 7 were achieved by Solver add-in in Excel within 1
minute, a performance that is a reasonable in terms ofcomputation time for a network of less than 20 convenience
stores within a city. With the usage of Google API for Work
to access the distance matrix for real-traffic condition andsome further customization of the Evolutionary Algorithm’s
parameters, this approach can also deal with a large scale
network in bigger cities or in a whole prefecture that use
multiple warehouses to cater to the needs of convenience
stores.

604

E. Mobile Application Prototype
The developed prototype has been tested on modernbrowsers, including Chrome mobile on Android and Safari on
iOS. Its performance has also been validated with several
cases of user input (postal codes, warehouse location, andre-fueling necessity), and with all the cases of distribution
network of convenience stores in Beppu City, the application
has delivered optimal routing solutions. Moreover, thanks to
the mobile device simulation function of desktop Chromebrowser, the application has also been tested with different
manual GPS locations to simulate continuous movement ofvehicles. With the rapid development of silicon technologies,
these days portable devices with large screen and high quality
display are becoming more affordable. As such, this prototype
is developed with the aim of integrating science andtechnology to come up with a practical solution to a realbusiness problem. Such strategy will be used in our ongoingand future studies and it is hoped that other researchers will
also pursue a similar trend.
V. CONCLUSION, LIMITATIONS AND FUTURE RESEARCHDIRECTIONSThis paper raises the awareness of utilizing available
technology and programming techniques to attain commercial
goal in convenience stores business, and simultaneously takethe perspective of CSR into consideration. This research
proposes practical methodologies for solving the convenience
stores’ distribution problem of optimizing product delivering
route from a warehouse to several convenience stores, passing
by a gas station for re-fueling, and returning back to thewarehouse. The approach involves collecting geographicallocation dataset of about 100,000 real-life convenience storesand gas stations, a web-based mobile application for practicalimplementing of Google API service and creating database tosolve the problem. Specially, a more general view of theconvenience stores problem, which recognizes the empiricalnecessity of dividing a fix number of drivers to many small
convenience stores distribution network, is introduced andresolved by using Evolutionary Algorithm of Solver on Excel.
Overall, considering its reliability and fast performance, theapproach can be extended for practical applications of not
only convenience stores’ business, but also in otherbusinesses that are looking for advancements in supply chain
management.
The followings can be considered limitations and somerelevant future research directions.
1) Current web scraping technique strongly replies on the
NAVITIME website structure. Specifically, theintroduced web scarping technique uses a tree-basedalgorithm to extract data at pre-indicated tag position.
Because of this, when the HTML tags structure of the
webpage with the needed data changes, the technique
may not work properly. To enhance this, development ofan algorithm with artificial intelligence ability, such as
the learning-based web wrapper algorithm, may benecessary so as to cope with possible structural changes
to the NAVITIME website.
2) The analytical approach of this paper heavily relies on
Google API service. Considering the academic nature of
the work, however, the developed prototype was only
tested with the free version of Google API service which

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

imposes certain limitation on the number of requests perday. Nonetheless, such limitation can be overcome byutilizing the Google API for Work, which facilitates up to
100,000 requests per day. Moreover, since the Google
API for Work also supports calculation of optimal routewith waypoints within real-time traffic condition, its
usage will ensure a much more reliable result.
3) Although the recommended approach provides apractical solution for the convenience stores’ distribution
problem with the goal of optimizing the delivery time,
which would result in reduction of gas consumption; theproblem can also be examined under a pattern learningperspective to have an inner sight into the convenience
stores’ distribution process. Specifically speaking, apartfrom travelling time, there are also many other factorsthat contributes to the gas consumption during the
product delivering process, namely; weather,temperature, route terrain, vehicle driving speed, etc.
However, not all such factors can be easily controlled andcalculated. A promising approach would be to collectdata on such factors’ impacts on the volume of gas
consumption and apply machine-learning techniques so
as to understand the patterns of how much each of suchfactors could contribute to the actual gas consumption.
Then, an algorithm that also takes these factors into
consideration can be developed. Moreover, because thedata is expectedly retrieved in real-time, the learning
pattern could be continuously updated to ensure the mostpragmatic suggestion. To obtain this, the researchpromotes further examination into not only pattern
learning techniques, but also ubiquitous and sensor
network for real-time data collection.
REFERENCES[1]

Akinobu Terasaka, (1998), Development of new store types: the role ofconvenience stores in Japan, GeoJournal, Volumn 45, Issue 4, pp.
317-325.
[2] 7-Eleven. (n.d.). Convenience Store Operations. Retrieved fromhttp://www.7andi.com/dbps_data/_template_/_user_/_SITE_/localhost/_res/en/ir/library/co/pdf/p24_33.pdf.
[3] Lawson In-Store Services. (n.d.). Retrieved February 18, 2015, fromhttp://lawson.jp/en/product_service/service.
[4] METI. (2014, October 14). Monthly Report on the Current Survey ofCommerce.
Retrievedfrom
http://www.meti.go.jp/english/statistics/tyo/syoudou_kakuho/index.ht
ml.
[5] USDA Foreign Agriculture Service. (2013, December 12). Japan
Retail Food - Retail Sector 2012.
[6] Arai, Y. and Yamada, H. (1994) Development of Convenience Store
System in Japan, 1970's-1980's. Terasaka, A. and Takahashi, S. eds.,
Comparative Study on Retail Trade: Tradition and Innovation. Ryutsu
Keizai University.
[7] Broda Christian, Ephraim Leibtag, and David E. Weinstein. (2009).
The Role of Prices in Measuring the Poor's Living Standards. Journalof Economic Perspectives, 23(2), pp. 77-97. Cordeau[8] J-F Cordeau, M. Gendreau, G. Laporte, J-Y Potvin, F. Semet. (May
2002). A Guide to Vehicle Routing Heuristics, The Journal of the
Operational Research Society, Vol. 53, No, pp. 512-522.
[9] G. Clarke and J. W. Wright. (1964). Scheduling of vehicles from acentral depot to a number of delivery points, Operations Research, vol.
12, pp. 568–581.
[10] Back, T. (1996). Evolutionary algorithms in theory and practice.
Oxford Univ. Press.
[11] Gendreau, M., Hertz, A., & Laporte, G. (1994). A tabu search heuristic
for the vehicle routing problem. Management science, 40(10), pp.
1276-1290.

605[12] Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J. Y.
(1997). A tabu search heuristic for the vehicle routing problem with
soft time windows. Transportation science, 31(2), pp. 170-186.
[13] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system:
optimization by a colony of cooperating agents. Systems, Man, andCybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1), pp.
29-41.
[14] Dariusz Barbucha. (2014). A Hybrid Cooperative and Reactive Search
Approach for Vehicle Routing Problem with Time Windows,
Intelligent Information and Database Systems, pp. 207-216.
[15] Canhong Lin, K.L. Choy, G.T.S. Ho, H.Y. Lam, Grantham K.H. Pang,
K.S. Chin. (November 2014). A decision support system for
optimizing dynamic courier routing operations, Expert Systems with
Applications, Volume 41, Issue 15, pp. 6917-6933, ISSN 0957-4174.
[16] Jiafu Tang, Yang Yu, Jia Li. (January 2015). An exact algorithm forthe multi-trip vehicle routing and scheduling problem of pickup anddelivery of customers to the airport, Transportation Research Part E:
Logistics and Transportation Review, Volume 73, pp. 114-132, ISSN
1366-5545.
[17] Kunpeng Li, Bin Chen, Appa Iyer Sivakumar, Yong Wu. (October2014). An inventory–routing problem with the objective of travel timeminimization, European Journal of Operational Research, Volume 236,
Issue 3, pp. 936-945, ISSN 0377-2217.
[18] Mes, M., Schutten, M., & Rivera, A. P. (2014). Inventory routing for
dynamic waste collection. Waste management, 34(9), pp. 1564-1576.
[19] Zarandi, M. H. F., Hemmati, A., Davari, S., & Turksen, I. B. (2013).
Capacitated location-routing problem with time windows under
uncertainty. Knowledge-Based Systems, 37, pp. 480-489.
[20] Duygu Taş, Ola F, Tom Van Woensel. (December 2014). A Vehicle
Routing Problem with Flexible Time Windows, Computers &Operations Research, Volume 52, Part A, pp. 39-54, ISSN 0305-0548.
[21] A.L. Kok, E.W. Hans, J.M.J. Schutten. (May 2011). Optimizing
departure times in vehicle routes, European Journal of Operational
Research, Volume 210, Issue 3, pp. 579-587, ISSN 0377-2217.
[22] Narath Bhusiri, Ali Gul Gureshi, Eiichi Taniguchi. (2013). Vehicle
Routing and Scheduling Problems for Convenience Store Industry
Considering Soft Time Windows, Proceedings of the Eastern Asia
Society for Transportation Studies, Volume 9.
[23] M.M. Solomon. (1987). Algorithms for the vehicle routing andscheduling problems with time window constraints, Oper. Res., vol. 35,
pp. 254-265.
[24] András Király, János Abonyi. (February 2015). Redesign of the supplyof mobile mechanics based on a novel genetic optimization algorithm
using Google Maps API, Engineering Applications of Artificial
Intelligence, Volume 38, pp. 122-130, ISSN 0952-1976.
[25] Rodolfo Dondo, Jaime Cerdá. (February 2007). A cluster-based
optimization approach for the multi-depot heterogeneous fleet vehicle
routing problem with time windows, European Journal of Operational
Research, Volume 176, Issue 3, pp. 1478-1507, ISSN 0377-2217.
[26] Ren Chunyu; Wang Xiaobo. (2006). Research on VRP Optimizing
Based on Hierarchy clustering and IGA under Common Distribution,
Computational Intelligence and Security, 2006 International
Conference on, vol.1, pp. 143-146.
[27] VBA for Modelers: Developing Decision Support Systems With
Microsoft Office Excel S. Christian Albright (South-Western Pub
2011/08/26).
[28] Frontline Systems Inc., Solver URL: http://www.solver.com.
[29] NAVITIME. (n.d.). Retrieved February 19, 2015, fromhttp://www.navitime.co.jp.
[30] Marcos D. Assunção, Rodrigo N. Calheiros, Silvia Bianchi, Marco A.S.
Netto, Rajkumar Buyya. (August 2014). Big Data computing andclouds: Trends and future directions, Journal of Parallel andDistributed Computing, ISSN 0743-7315.
[31] Google Developers. (n.d.). The Google Directions API - Google MapsAPI
Web
Services.
Retrievedfromhttps://developers.google.com/maps/documentation/directions/.
Gulcin Dinc Yalcin and Nihal Erginel. (July 2012). A Heuristic Basedon Integer Programming for the Vehicle Routing Problem with
Backhauls. Proceedings of the 2012 International Conference on
Industrial
EngineeringandOperations
Management.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 4, Issue 2, March 2015

606

Thai Quang LE was born in Khanh Hoa Province,
Vietnam in 1993. He is presently pursuing his studies
at Ritsumeikan Asia Pacific University (APU) in thefield of Business Administration focusing on
Strategic Management and candidate for graduation.
He has received numerous scholarships including,
APU Tuition Reduction Scholarship from 2011-2015,
JASSO (Japan Student Services Organization)
Scholarship from 2011-2012, and Oita Prefecture
Scholarship in 2012-2013, 2014-2015. His research interests include applied
programming in business, modeling for decision-making and machinelearning, on which he has carried out a few presentations. The theme of histhesis is retail activity optimization and he has become an IEEE member
since 2014.
Davar Pishva is a professor in ICT at the College of
Asia Pacific Studies, Ritsumeikan Asia Pacific
University (APU) Japan. In teaching, he has been
focusing on information security, technology
management, VBA for modelers, structured decision
making and carries out his lectures in an appliedmanner. In research, his current interests include
biometrics; e-learning, environmentally sound and
ICT enhanced technologies. Dr. Pishva received his
PhD degree in System Engineering from Mie University, Japan. He is
Secretary General of IAAPS (International Association for Asia PacificStudies), Senior Member of IEEE, and a member of IEICE (Institute ofElectronics Information & Communication Engineers), IAAPS andUniversity & College Management Association.

Copyright ⓒ 2015 GiRI (Global IT Research Institute)