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
Prof. Dal-Hwan Yoon, Semyung University, korea
Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 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)

Editor Guide
 Introduction for Editor or Reviewer
All the editor group members are to be assigned as a evaluator(editor or reviewer) to submitted journal
papers at the discretion of the Editor-in-Chief. It will be informed by eMail with a Member Login ID and
Password.
Once logined the Website via the Member Login menu in left as a evaluator, you can find out the paper
assigned to you. You can evaluate it there. All the results of the evaluation are supposed to be shown in
the Author Homepage in the real time manner. You can also enter the Author Homepage assigned to
you by the Paper ID and the author's eMail address shown in your Evaluation Webpage. In the Author
Homepage, you can communicate each other efficiently under the peer review policy. Please don't missit!
All the editor group members are supposed to be candidates of a part of the editorial board, dependingon their contribution which comes from history of ICACT TACT as an active evaluator. Because the main
contribution comes from sincere paper reviewing role.

 Role of the Editor
The editor's primary responsibilities are to conduct the peer review process, and check the final cameraready manuscripts for any technical, grammatical or typographical errors.
As a member of the editorial board of the publication, the editor is responsible for ensuring that the
publication maintains the highest quality while adhering to the publication policies and procedures of
the ICACT TACT(Transactions on the Advanced Communications Technology).
For each paper that the editor-in-chief gets assigned, the Secretariat of ICACT Journal will send theeditor an eMail requesting the review process of the paper.
The editor is responsible to make a decision on an "accept", "reject", or "revision" to the Editor-in-Chief
via the Evaluation Webpage that can be shown in the Author Homepage also.

 Deadlines for Regular Review
Editor-in-Chief will assign a evalaution group( a Editor and 2 reviewers) in a week upon receiving acompleted Journal paper submission. Evaluators are given 2 weeks to review the paper. Editors are
given a week to submit a recommendation to the Editor-in-Chief via the evaluation Webpage, once all or
enough of the reviews have come in. In revision case, authors have a maximum of a month to submit
their revised manuscripts. The deadlines for the regular review process are as follows:

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Evalution Procedure

Deadline

Selection of Evaluation Group
Review processing
Editor's recommendation
Final Decision Noticing

1 week2 weeks
1 week
1 week Making Decisions on Manuscript
Editor will make a decision on the disposition of the manuscript, based on remarks of the reviewers. The
editor's recommendation must be well justified and explained in detail. In cases where the revision isrequested, these should be clearly indicated and explained. The editor must then promptly convey thisdecision to the author. The author may contact the editor if instructions regarding amendments to themanuscript are unclear. All these actions could be done via the evaluation system in this Website. The
guidelines of decisions for publication are as follows:

Decision

Description

AcceptAn accept decision means that an editor is accepting the paper withno further modifications. The paper will not be seen again by theeditor or by the reviewers.
The manuscript is not suitable for the ICACT TACT publication.
The paper is conditionally accepted with some requirements. A
revision means that the paper should go back to the original
reviewers for a second round of reviews. We strongly discourage
editors from making a decision based on their own review of themanuscript if a revision had been previously required.

RejectRevision Role of the Reviewer
Reviewer Webpage:
Once logined the Member Login menu in left, you can find out papers assigned to you. You can alsologin the Author Homepage assigned to you with the paper ID and author's eMail address. In there you
can communicate each other via a Communication Channel Box.

Quick Review Required:
You are given 2 weeks for the first round of review and 1 week for the second round of review. You must
agree that time is so important for the rapidly changing IT technologies and applications trend. Pleaserespect the deadline. Authors undoubtedly appreciate your quick review.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Anonymity:
Do not identify yourself or your organization within the review text.

Review:
Reviewer will perform the paper review based on the main criteria provided below. Please provide
detailed public comments for each criterion, also available to the author.
 How this manuscript advances this field of research and/or contributes something new to theliterature?
 Relevance of this manuscript to the readers of TACT?
 Is the manuscript technically sound?
 Is the paper clearly written and well organized?
 Are all figures and tables appropriately provided and are their resolution good quality?
 Does the introduction state the objectives of the manuscript encouraging the reader to read on?
 Are the references relevant and complete?

Supply missing references:
Please supply any information that you think will be useful to the author in revision for enhancing
quality of the paperor for convincing him/her of the mistakes.

Review Comments:
If you find any already known results related to the manuscript, please give references to earlier papers
which contain these or similar results. If the reasoning is incorrect or ambiguous, please indicate
specifically where and why. If you would like to suggest that the paper be rewritten, give specificsuggestions regarding which parts of the paper should be deleted, added or modified, and pleaseindicate how.

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
Dear Author,

 You can see all your paper information & progress.
 Step 1. Journal Full Paper Submission
Using the Submit button, submit your journal paper through ICACT Website, then you will get newpaper ID of your journal, and send your journal Paper ID to the Secretariat@icact.org for the review andeditorial processing. Once you got your Journal paper ID, never submit again! Journal Paper/CRF
Template Step 2. Full Paper Review
Using the evaluation system in the ICACT Website, the editor, reviewer and author can communicate
each other for the good quality publication. It may take about 1 month.

 Step 3. Acceptance Notification
It officially informs acceptance, revision, or reject of submitted full paper after the full paper review
process.

Status
Acceptance
Revision
RejectActionGo to next Step.
Re-submit Full Paper within 1 month after Revision Notification.
Drop everything.

 Step 4. Payment Registration
So far it's free of charge in case of the journal promotion paper from the registered ICACT conference
paper! But you have to regist it, because you need your Journal Paper Registration ID for submission of
the final CRF manuscripts in the next step's process. Once you get your Registration ID, send it to
Secretariat@icact.org for further process.

 Step 5. Camera Ready Form (CRF) Manuscripts Submission
After you have received the confirmation notice from secretariat of ICACT, and then you are allowed tosubmit the final CRF manuscripts in PDF file form, the full paper and the Copyright Transfer Agreement.
Journal Paper Template, Copyright Form Template, BioAbstract Template,

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Submission Guide
All the Out-Standing ICACT conference papers have been invited to this "ICACT Transactions on the
Advanced Communications Technology" Journal, and also welcome all the authors whose conference
paper has been accepted by the ICACT Technical Program Committee, if you could extend new contents
at least 30% more than pure content of your conference paper. Journal paper must be followed toensure full compliance with the IEEE Journal Template Form attached on this page.

 How to submit your Journal paper and check the progress?
Step 1. Submit Using the Submit button, submit your journal paper through ICACT

Step 2. Confirm

Step 3. Review

Website, then you will get new paper ID of your journal, and send yourjournal Paper ID to the Secretariat@icact.org for the review and editorial
processing. Once you got your Journal paper ID, never submit again! Using
the Update button, you can change any information of journal paper
related or upload new full journal paper.
Secretariat is supposed to confirm all the necessary conditions of yourjournal paper to make it ready to review. In case of promotion from theconference paper to Journal paper, send us all the .DOC(or Latex) files ofyour ICACT conference paper and journal paper to evaluate the differenceof the pure contents in between at least 30% more to avoid the self
replication violation under scrutiny. The pure content does not include any
reference list, acknowledgement, Appendix and author biography
information.
Upon completing the confirmation, it gets started the review process thru
the Editor & Reviewer Guideline. Whenever you visit the Author
Homepage, you can check the progress status of your paper there fromstart to end like this, " Confirm OK! -> Gets started the review process > ...", in the Review Status column. Please don't miss it!

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 2, Issue 3

1

Performance Investigation of Reduced Complexity Bit-Flipping using Variable Thresholds and Noise Perturbation

206

Julian Webber, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa
Hokkaido University, Sapporo, Japan

2

Classification of N-Screen Services, Scenarios and its Standardization

214

Changwoo Yoon, Hyunwoo Lee, Won Ryu
Electronics & Telecommunications Research Institute, Daejeon, Korea

3

Dempster-Shafer Evidence Theory Based Trust Management Strategy against Cooperative Black Hole Attacks andGray Hole Attacks in MANETs223

Bo YANG, Ryo YAMAMOTO, Yoshiaki TANAKA
Waseda University, Japan4

A Global Mobility Scheme for Seamless Multicasting in Proxy Mobile IPv6 Networks

233

Hwan-gi Kim*, Jong-min Kim*, Hwa-sung Kim
Kwangwoon University, Seoul, Korea

5

Alternatives to Network Selection in Heterogeneous Wireless Environments
Vinícius de Miranda Rios*, Claudio de Castro Monteiro**, Vanice Canuto Cunha**** University of Tocantins, Palmas - TO – Brazil, **Federal Institute of Education, Palmas - TO -Brazil, ACM member, ***University of Brasilia, Brasilia DF - Brazil

240

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013206

Performance Investigation of Reduced Complexity
Bit-Flipping using Variable Thresholds
and Noise Perturbation
Julian Webber, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa
Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
jwebber@ieee.org, {nishim,ohgane,ogawa}@icl.hokudai.ac.jp

Abstract—The near Shannon capacity approaching low-density
parity-check (LDPC) linear block codes are now in widespreaduse in modern systems including the long term evolution advanced (LTE-A) cellular, 802.11n Wi-Fi and DVB-S2 satellite
communications standards. The decoders based on the iterative
belief propagation algorithm provide near optimum performancebut also have very high computational complexity. Therefore
significant research has recently focused on reduced complexity architectures based on the group of so-called bit-flipping
algorithms. In the basic bit-flipping algorithm the number offailed parity checks for each bit is computed and the bit withthe maximum failed parity checks is inverted. Inverting bits
above a certain threshold removes the complexity involved with amaximum-search and, adaptive thresholds on each bit can further
reduce the computation overhead. The criterion for the thresholdupdate affects the error and convergence performances. Here, we
describe a low-complexity architecture that has two (or more)
decoder branches each with a different threshold scaling factor
and select the threshold and bits at each iteration from the branchwith the lowest syndrome sum. We then investigate the effect ofadding a random Uniform or Gaussian noise perturbation to thethreshold in order to reduce the average iteration count further
in order to provide the opportunity to escape from stuck decodingstates.
Index Terms—bit-flip algorithm, gradient-descent, reducedcomplexity, noise perturbation, LDPC decoding.

I. I NTRODUCTION
Gallager first proposed LDPC codes in his 1963 PhD
thesis [1]. However until their rediscovery in the mid-1990s [2]and coinciding with the availability of powerful low-costdigital signal processors, their use was largely forgotten. Nearoptimum decoding performance can be achieved with the softdecision belief propagation algorithm (BPA) that computes
the marginal probabilities at the nodes on factor graphs [3].
Manuscript received June 27, 2012. The work was supported by the GCOE
program 2009-2012, Hokkaido University, Japan.
J. Webber was with the Graduate School of Information Science andTechnology, Hokkaido University, Kita-ku, Sapporo 060-0814, Japan. He
is now with Wave Engineering Laboratory, Advanced Telecommunications
Research Institute International (ATR), Kyoto, 619-0288, Japan. (Tel: +81774951313; email: jwebber@ieee.org).
T. Nishimura is with the Graduate School of Information Science andTechnology, Hokkaido University, Kita-ku, Sapporo, 060-0814, Japan. (Tel:+81-117067396; email: nishim@ist.hokudai.ac.jp).
T. Ohgane is with the Graduate School of Information Science andTechnology, Hokkaido University, Kita-ku, Sapporo, 060-0814, Japan. (email:ohgane@ist.hokudai.ac.jp).
Y. Ogawa is with the Graduate School of Information Science and Technology, Hokkaido University, Kita-ku, Sapporo, 060-0814, Japan. (email:ogawa@ist.hokudai.ac.jp).

The highest performance is achieved with long-length cyclefree codes and when the soft data at the decoder input is
independent. However, the BPA also features a very highdecoding complexity. At the other end of the complexity
spectrum reside the bit-flipping algorithms (BFA) which are
aimed at low-power portable applications. The average numberof iterations in the BFA is higher than that of the BPA, but at
each iteration a considerably smaller number of computations
are required which can be computed in parallel, and the overall
complexity is much less onerous.
In previous work we investigated the performance of alow complexity dual-scaling factor bit-flipping decoder withadaptive thresholds [4]. The addition of adding a simple noise
perturbation to escape a stuck decoding state was proposed
and initially investigated. In this paper we have extended theresearch to examine in more detail the effects of the noise
perturbation on both the BER and iteration count. In particular, we investigate increasing the perturbation multiplication
coefficient as well as the iteration delay size before which theperturbation is applied.
The paper is organized as follows: The belief propagation
and bit-flipping algorithms are discussed in Section II. The
adaptive bit-flipping technique together along with single
and multiple-branch scaling factor decoders are introduced in
Section III. The addition of a noise perturbation to escape astuck-state is proposed and the effect of a noise coefficient
multiplier and a sliding window counter concept are analysedin Section IV. Bit error and iteration count performance results
are discussed in Section V and a conclusion is finally drawn
in Section VI.
II. D ECODING ALGORITHMS
The received sample vector is y = (y1 , y2 , ..., yN ) and thehard decoded bits are given by x ∈ {+1, −1}. A sparse parity
check matrix H is of size M × N , where the number of checknodes is M and the number of bits is N [5]. The non-zeroelements in column j are written as M (j) and the non-zeroelements in row i of H are N (i). The syndrome associatedwith the m-th check node is denoted∏as sm or equivalently
the bipolar syndrome is written as j∈N (i) xj . It is often
instructive to graphically represent the parity check matrix bya bipartite Tanner graph [6] with N variable nodes (number ofbits in the codeword) and M check nodes (number of paritybits) (Fig. 1).
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

207

+

+

+

+

Check to variable
information
Variable to checkinformation

Variable nodes
Fig. 1.

LDPC decoding using Tanner graph representation.

A. Belief Propagation
The belief propagation algorithm is based on the concept of
exchanging probabilities via ‘messages‘ between check nodes
and variable nodes which generally become more accuratewith each iteration. The message passed from the check node
to the variable node is the probability that the variable node
has a certain value given all the messages passed to thatcheck node in the preceding iteration from message nodes
except the variable node itself. At the same time, the message
passed from a variable node to a check node is the probabilty
that it has a certain value given its observed value, and allvalues passed to it in the previous iteration from check nodes
connected to it other than the check node itself [7]. Usually
the probabilities are represented by log-likelihood ratios. The
algorithm provides optimum performance but at the cost ofvery high computational complexity.

Objective function (normalized)

Check nodes
correct
codeword1large θ ,small ∆ λ0.8
localmaxima

0.6

0.4

0.2

0

small θ ,large ∆ λlarge θ ,small ∆ λsmall θ ,large ∆ λ
10

20

30

40

50

60

70

80

Iteration
Fig. 2. Concept of the stuck decoding states. The step-size is reduced onreaching a local-maxima and increased on escaping it. Eventually the correct
codeword is found.
|y |jwhere, Rij = β |ymax| . In addition to the syndrome sum theigradient descent bit flipping (GDBF) algorithm [11] includes
a correlation term between the initial received soft and harddecision which should be large and positive for a correctly
estimated bit [11].
∑ ∏xj + xn yn .
(3)∆GDn (x) =i∈M (n) j∈N (i)B. Bit-flipping algorithms
The sign of a single bit n with a maximum inversion
function, En is inverted at each iteration in the basic BFA.
A weighted BF (WBF) algorithm was proposed by Kou etal. [8] that incorporates a term related to the energy of the least
reliable bit involved in each check. This however necessitatesa search over the complete slot-length which contributes to anincreased complexity and delay. The WBF is expressed as∑∏
En ={minj∈N (i) |yj |}xj ,(1)i∈M (n)j∈N (i)minwhere ymis the least reliable message node associated withthe m-th check.
Given that two message bits have contributed to a failed
parity check, it is the message with the highest energy |yj |that is deemed most likely to be correct. In the reliability|yj |ratio algorithm [9] a normalization factor Rij = β |ymax|i
is calculated where |yimax | is the highest soft magnitude of
all message nodes associated with the m-th check (Eq. 2).
The normalization improves the performance with respect
to the WBF algorithm but the division operation in theinitialization stage contributes to an increased complexity.
An implementation-efficient reliability ratio WBF (IRRWBF)
algorithm was subsequently proposed which lowered the complexity particularly when the iteration count was low and thecode length was high [10].
∑ ∏En =/Rij .
(2)i∈M (n) j∈N (i)A bit is inverted if ∆GDn (x) is less than a global threshold.
An objective function indicates the state of the algorithm ateach iteration and is defined as
f GD (x) =∑∏i∈M (n) j∈N (i)

xj +

N
∑

xn yn .

(4)n=1

By monitoring the objective function it was proposed to switchfrom a large step-size (multiple bit-flips) to a small step-size
if a stuck decoding state was detected. On such occasions asingle small-descent of the objective function using a lower set
threshold with a random component was shown experimentallyto improve the performance. On escaping a local-maxima
the step-size can then increase and eventually the correct
codeword is found (Fig. 2).
III. A DAPTIVE FLIPPING THRESHOLDS
Recently adaptive thresholds for codes with a large numberof checks per bit were investigated by Cho [12]. The benefit
gained from inverting erroneous bits minus the loss from inverting correct bits was defined as the ‘flipping-gain’. A known
monotonically increasing number of errors was injected intoa packet, and the threshold range p that produced a flippinggain greater than one was calculated. It was observed that there
was indeed an optimal threshold that resulted in a minimum
number of iterations. It was conjectured that a similar analysis
could be applied to parity check codes containing a smaller
number of checks per bit, (e.g. as M =3), using non-integer
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

Iter. k

λ1,k

θk

λ2,k

*
Iter. K+1

θk*

λ1,k+1

λ2,k+1

λN,k

θk

208

TABLE IT HRESHOLD SCALING VALUES , θ AND PARTIAL COMPONENTS .

*λN,k+1

Fig. 3. The updated threshold λn,k+1 for bit n at iteration k+1 is computedby summing one or more shifted versions of the previous threshold λn,k .

θ (decimal)

dX

dY

θA

0.5

1

0

1/2

θB
0

0.625

1

3

1/2

+1/8

0.75

1

2

1/2

+1/4

0.875

0

3

1

-1/8

0.9375

0

4

1

-1/16

0.969

0

5

1

-1/32

250

300

4

The threshold update rule is written as (6)λn (k + 1) = θ λn (k),

2
1
0
-1
-2
-3

(6)

where k is the iteration number and θ is a threshold scaling
value. With this technique the algorithm moves naturally
from many to few bit-flips as the threshold is successively
lowered. The computational complexity is lowered as each
bit-processing operation (i.e. scaling / inversion) is computed
independently of other bits. Furthermore, the physical hardware multiplier can be replaced a simple shift if the scale
factor is chosen to be proportional to a power of two (Fig. 3).
A. Threshold scaling factor
The scaling factor θ in this paper is expressed as the sumof two fractional parts as :
θ = 2−dX + 2−dY ,3

En (Real)

thresholds (e.g. 1.33) and using a probabilistic strategy (e.g.
thresholds 1.0, 1.0, 2.0).
In [13], an adaptive threshold λn was set on each bit. The
sign of a bit was inverted if its inversion function was below
the particular threshold (5), and otherwise it’s threshold was
lowered.
En (k) ≤ λn (k).
(5)

(7)

where dX and dY are integers. The range of values for θ thatwere investigated in this work along with the required shifts
are listed in TABLE I. To generate the threshold update λn (k+1) = 0.625λn (k) for example, the current threshold λn (k)is shifted once 1 place (i.e. 0.5), once 3 places (i.e. 0.125),
and the two partial products are summed. A small value ofθ (e.g. 0.5) clearly equates to a large step-size and a large
value of θ (e.g. 0.969) corresponds to a small step-size. By
applying more shift addition operations any arbitrary scaling
factor can be created, though for simplicity two additions were
demonstrated in this work.

-4
-5
-6
50

100

150

200

350

400

450

500

bit position

Fig. 4. Snapshot of the inversion function En of each bit at iteration 60(θ=0.97). The mean value is indicated by the red horizontal line.

are flipped at each iteration. On flipping the value of the bits’
inversion function will change and become more negative as
its’ probability of being correct increases.
C. Multiple scaling-factors / branches
In this work a novel dual-scaling switching algorithm (DSA)
is proposed that jointly uses a fine and a coarse step-size
decoder. The estimated hard bits from the decode-branch thathave the minimum syndrome sum are passed to both decoder
branches for the start of the next iteration (Fig. 5). The
optional noise perturbation block is discussed further in thenext section. The algorithm is sumarized as
STAGE 1- Initialize the inversion thresholds λn =λ0 , ∀ n.
Set State=1.
STAGE 2- Compute the inversion function En ,for m = 1, 2, ..., M .

B. Single scaling-factor
In this paper two progressive edge growth (PEG) regular
LDPC codes are investigated with their properties listed inTable II. PEG codes are created with the aim to maximize thegirth (i.e. length of the shortest cycle) and therefore generally
provide good performance [14]. The inversion function En
recorded from a random trial at iteration 60 is plotted in Fig. 4.
The bits whose inversion function En are above the threshold

TABLE IILDPC CODE PROPERTIES .
CodeName [15]

Rows

Cols.

Col. deg.

1

PEGReg504x1008

504

1008

3

2

PEGReg252x504

252

504

3

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 201370
Switch
θ1
θ2

60

50

Syndrome sum

STAGE 3- If En (θ2 ) ≥ λn , Flip bit n.
Else λn =θ2 λn−1 . Optionally add noise perturbation to λn .
STAGE 4- If state=1, Repeat STAGE 3 for θ=θ1 .
STAGE 5If Σsm (θ1 ) ≥ Σsm (θ2 ), Set state=2;λn =λn (θ2 ),∀ n and x=x(θ2 ); Deactivate branch no.1 to reduce energy
consumption. Else λn =λn (θ1 ), ∀ n and x=x(θ1 ).
STAGE 6- Terminate if Σsm = 0 or maximum iterations
reached.
Else Goto STAGE 2.

209

40

30

20

10

State
1

0
0

10

Start
Set
State=1

State
2
20

30

40

50

60

70

80

90

100

Iteration

Fig. 6. Graph showing the syndrome sum versus iteration count. Single
scaling factor θ1 =0.5 and θ2 =0.97. ‘Switch’ indicates the dual branch decoder.

N

If state != 2,
Decode 1
Θ1=0.5

Decode 2
Θ2=0.97

S1(k) =
S1(k-10)?

S2(k) =
S2(k-10)
?

Y

N

Y

Add noise
perturbation

Add noise
perturbation

s1<
s2 ?

N

λk+1= λk(0.97)

Y

λk+1= λk(0.5)

N

Set State = 2

k= K
or
S=0 ?
Y

Stop
Fig. 5. Proposed dual scaling threshold algorithm flow chart showing optional
noise perturbation unit.

The number of failed parity checks versus iteration number
for the DSA is shown in Fig. 6. The solid line (marked ‘.’) plots
the syndrome sum when θ1 =0.5 and the solid line (marked‘+’) when θ2 =0.969. The number of failed checks decreased
to zero by iteration 98 using the small step-size. However, byswitching at iteration 11 from θ1 =0.5 to θ2 =0.969 when the
syndrome sum was lower on that branch, the number of failed
checks was rapidly reduced to zero by the 18th iteration.

Although the overall complexity is increased relative to asingle branch decoder, this can be limited by deactivating
the θ1 branch once the θ2 branch produces less errors. The
optimum scaling factor is a function of the modulation, SNR,
and particular code characteristic which makes calculating
an optimum value very complex and an off-line statistical
approach was made in [13] through experiment trials.
A further benefit of the dual-scaling detector proposed hereis that both a real-time or off-line threshold calculation are
avoided. A three-branch (three-scaling factors) detector was
also evaluated but achieved only a small reduction in theiteration count with respect to the dual-scaling decoder andhence, due to the additional complexity, was not consideredfurther in this work.
To reduced complexity an early-stopping algorithm wasinvestigated in which processing was stopped if f (k)=f (k −10) or f (k)=f (k − 11). It was necessary to apply the two
conditions in case f (k) oscillates between two values such as
seen by the black zig-zag line in Fig. 6.
IV. N OISE PERTURBATION
The local minima are a feature of the gradient descentalgorithm where trapped search points occur but the estimatedsequence does not correspond to the transmitted code-word.
In such a situation we investigate the technique of insertinga small random perturbation to the threshold of each bit
whenever the syndrome sum does not change over a sliding
window of the previous W iterations. When this condition issatisfied a new random noise sample zn is added to the current
value of λn as shown by
λn+1 = λn + Kzn ,(8)where zn is a random Uniform or Gaussian sample for bit n
and K is a noise perturbation coefficient typically between 1.2
and 2.4 for Uniform noise and 0.7 to 1.3 for Gaussian noise.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

100

80

90

70

Switch
θ

80

1

θ

70

Switchθ
1602Syndrome sum

Syndrome sum

210

60
50
40
30

θ

2

50
40
30
20

20
10

10
0
0

50

100

0
0

150

50

Iterations

A. Temporal study
We first investigate through experiment the instantaneous
effect on the syndrome sum of adding a noise perturbation.
In Fig. 7 Uniform noise was injected at iteration 50 and
subsequently at intervals of 10 iterations thereafter. We observea small but noticeable lowering in the number of syndrome
errors at iterations 50, 80 and 110. Due to the random natureof the noise perturbation, there can be occasions when there
is no improvement such as at iteration 100 for example.
In the absence of a random perturbation, the syndrome
sum recorded during another random decoding trial, is plottedin Fig. 8. Next, using the same received signal sequence yfor fairness, the syndrome sum is computed in a trial with
perturbation permitted (Fig. 9). The benefit of the the random
perturbation can clearly be seen where a stuck decoding state
was successfully exited and the syndrome sum reduced to zeroby iteration 66. On adding the perturbation, the particular bits
whose current inversion function is nearest to the threshold
are most likely to be inverted and the noise coefficient K
controls the number of additional bits that have the potential
to be inverted. Note that a program ’break’ was used within theiteration loop and in parallel the three independent decoders
were ran generating the red, blue, black curves. Once thesyndrome sum was zero on the red dual-threshold detector,
the loop was exited and so the trajectories for the blue andblack curves are not plotted beyond iteration 66, even though
they would continue in normal operation.
In the following two subsections we study in more detailthe effects of the noise coefficient multiplier K and the sliding
window counter size W . In particular, we aim to find optimum
values through experimentation across a range of practicalsignal to noise ratios.

threshold to be adjusted too quickly with a similar effect tothat of a large step-size θ. Thus an optimum coefficient range
would be expected and an experiment was set-up to find therange. The number of iterations versus the noise coefficientwere recorded across the SNR range from 2-4 dB. It is shownin Fig.10 that in the transition region the optimum value isabout 1.6 for both Code 1 (top) and Code 2 (bottom).
A comparison between Gaussian and Uniform noise perturbations is shown in Fig.11. It was found that in terms of thenumber of iterations, the same performance can be achieved
when K was set to 1.0 when applying a Gaussian sourceand 1.5 for Uniform sources. This result was valid for bothLDPC codes Code 1 and Code 2.
C. Sliding window counter size
The noise perturbation is only required when a stuck decoding state is reached. This can be detected when there is

100
90
80

Switchθ70

θ

A very low noise coefficient K would result in applying
no perturbation and too high a value would cause the flipping

1
2

60
50
40
30
20
10
0
0

B. Noise coefficient150

Fig. 8. Syndrome sum for another trial without added random perturbation.

Syndrome sum

Fig. 7.
Graph showing the syndrome sum against iteration with noise
perturbation at iterations: 50, 60, ... 150.

100

Iterations

50

100

150

Iterations
Fig. 9. Plot showing syndrome sum with added perturbation. This trial usedthe exact same received soft data, y as in Fig. 8 for fairness.

Copyright ⓒ GiRI (Global IT Research Institute)

140

140

120

120

100

100

Iterations (av.)

Iterations (av.)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

80
60
40

2 dB. Code 1
3 dB. Code 1
4 dB. Code 1

20
0
0.5

1

1.5

2

2.5

60
40
20
0

3

120

Iterations (av.)

Iterations (av.)

140

100
80
60
40
3 dB. Code 2
4 dB. Code 2
5 dB. Code 2

20

1.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Eb/N0 (dB)120

1

W=10. Code 1
W=20. Code 1
W=30. Code 1

80

Noise coefficient, K

0
0.5

211

2

2.5

W=10. Code 2
W=20. Code 2
W=30. Code 2

100
80
60
40
20
0

3

1

1.5

2

2.5

3

Noise coefficient, K

3.5

4

4.5

5

5.5

6

Eb/N0 (dB)

Fig. 10. Effect of the noise coefficient K on iteration count for Code 1 (top)and Code 2 (bottom).

no improvement in the syndrome sum despite the thresholdbeing lowered over a number of iterations. In such a situationa counter is incremented at each iteration and otherwise reset
to zero if the threshold was successfully lowered. If the counterreaches a set number, referred to as the sliding window counter
size W then a noise perturbation is added.
If a perturbation is added too soon the benefit of a small
step-size is not realized as there is little opportunity to slowly
lower the threshold. On the other hand, if the perturbation isadded too late then the algorithm takes an unnecessary longtime and wasteful iterations are required increasing the power
consumption. It was therefore thought that an optimum range
for the sliding window counter would exist.
In this experiment the counter is varied between 10 and 30
iterations and the maximum number of iterations was set
at 125. The resulting number of iterations until convergence (oralgorithm termination) is shown in Fig.12 for Code 1 (top) andfor Code 2 (bottom). The effect of the sliding window length

Fig. 12. Effect of sliding window size W on iteration count for Code 1(top) and Code 2 (bottom).

W on the average number of iterations as the SNR increases
is shown in Fig.13 Code 1 (top) and Code 2 (bottom).
The effect of the noise coefficient K on the average numberof iterations for various SNR is shown for Code 1 in Fig.14
for (top) Gaussian and (bottom) Uniform perturbation noise
sources. The optimum value is observed when the BER
gradient is at its largest value. For the case of Code 1 we canclearly see a minimum number of iterations exist at between 2
and 4 dB SNR for both Gaussian and Uniform noise sources.
In low SNR or as the algorithm approaches convergence thereis clearly no benefit in adding the perturbation. Further work
could investigate the optimum value for each threshold factors.
V. E RROR PERFORMANCESThe error performances of the various bit flipping algorithms
were evaluated in an AWGN channel through simulation. The
maximum number of iterations was set at 150 with BPSK
modulation. In the BER evaluation the sliding window counter
size, W was set at 10 iterations. Although the IRRWBF algorithm performed the best it also has the highest complexity.
The BER depends on the particular value of θ when the

K=1.0, Code 1, Gauss
K=1.5, Code 1, Uniform

100

140
120

50
0

1

2

3

4

5

6

7

8

Iterations (av.)

Iterations (av.)

150

100
80
60

1 dB, Code 1
2 dB, Code 1
3 dB, Code 1
4 dB, Code 1
5 dB, Code 1

40
20

Eb/N 0 (dB)

0
10

12

14

16

18

20

22

24

26

28

30

Sliding window length, W

K=1.0, Code 2, Gauss
K=1.5, Code 2, Uniform

140
120

100
Iterations (av.)

Iterations (av.)

150

50

100
80
60

2 dB, Code 2
3 dB, Code 2
4 dB, Code 2
5 dB, Code 2
6 dB, Code 2

40
20

0

2

3

4

5

6

7

8

Eb/N 0 (dB)

Fig. 11. Comparison of Gaussian and Uniform noise perturbations for (top)Code 1 and (bottom) Code 2.

0
10

12

14

16

18

20

22

24

26

28

30

Sliding window length, W

Fig. 13. Effect of sliding window length on average number of iterations(top) Code 1 and (bottom) Code 2.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

140

212

150
MWBF
IRRWBF
Dual θ=0.63/0.97
Dual θ early-stop
θ=0.5
θ=0.97100
8060

1 dB, Gauss
2 dB, Gauss
3 dB, Gauss
4 dB, Gauss
5 dB, Gauss

40
20
0
0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

Average Iterations

Iterations (av.)

120

1.5

Noise coefficient, K

140

Iterations (av.)

120
100
80
60

100

50

1 dB, Unif.
2 dB, Unif.
3 dB, Unif.
4 dB, Unif.
5 dB, Unif.

40
20
0
0.5

1

1.5

2

2.5

3

Noise coefficient, K

00Fig. 14. Effect of Noise coefficient K on average number of iterations forvarious SNR (top) Gaussian and (bottom) Uniform noise using Code 1.

10

10

10

10

-1

-2

-3

-4

0

MWBF
IRRWBF
Dual θ=0.63/0.97
Dual θ early-stop
θ=0.5
θ=0.97

1

2

3

4

5

6

E /N (dB)bFig. 15.

3

4

5

6

7

8

Average number of iteration versus Eb /N0 (dB) for Code 1.

10

10

-1

-2

BER

10

0

10

10

10

-3

-4

MWBF
Dual θ=0.63/0.97
Dual θ=0.63/0.97 + perturb.
θ=0.625
θ=0.625 + perturb.

-5

0

1

2

3

4

5

6

Eb/N0 (dB)

Fig. 17. BER versus Eb /N0 (dB) for LDPC Code 2 with noise perturbation
in AWGN.

0

BER

10

2

Eb/N 0 (dB)

Fig. 16.

number of iterations is limited as shown in Fig. 15. Whenθ=0.5, more bits are inverted on average per iteration and theoverall performance is worse than when θ=0.97. The proposed
DSA with θ=0.625/0.97 performed as well as well as the one
with a single θ=0.97 but its advantage is the reduced iteration
count as seen in Fig. 16 where the average number required
is lowered by 41% from 66 to 39 at 4 dB SNR. The numberof iterations of the ‘Dual early-stop’ algorithm was similarly
reduced to 17 at an Eb /N0 of 4 dB.
The performance of the random perturbation decoders aredenoted by ‘+ perturb.’. At the BER of 10−3 , an improvementof 0.5 dB was obtained for the dual scaling decoder with
perturbation (Fig. 17). As the fine step-size decoder slowly
converges to the desired solution there is only a small improvement in BER. However, at 3 dB Eb /N0 the number of iterations was reduced on average by (16.4, 9.2, 2.0) for the (dualscale, θ=0.63 and θ=0.97) decoders respectively (Fig.18). The
corresponding savings were (11.0, 10.1, 2.6) iterations at4 dB
Eb /N0 .

1

0

BER versus Eb /N0 (dB) with LDPC Code 1 in AWGN.

7

VI. C ONCLUSION
The error and convergence rates of adaptive threshold bitflipping algorithms have been studied. A dual-scaling architecture was proposed that applies the hard bit estimates andthresholds at each iteration from either the fine or coarse
step-size decode branch depending on its respective syndrome
sum. The dual-scale architecture with θ=0.63/0.97 performed
as well as that of the single step-size θ=0.97 decoder but the
average number of iterations was reduced by 41% at 4 dB
Eb /N0 . By adding a small noise perturbation the average
iteration count of the dual-scale detector could further be
reduced by 11 cycles at 4 dB Eb /N0 . An optimum value forthe perturbation coefficient was found to be 1.6 for Uniform
noise and applied after a delay of 20 iterations. Further work
could investigate the optimum value as a function of thethreshold step-size.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

Julian WEBBER Julian WEBBER received theM.Eng. and Ph.D. degrees from the University ofBristol, UK in 1996 and 2004 respectively. From1996 to 1998, he was with Texas Instruments working on ASIC and DSP. From 2001-07 he was a
Research Fellow at Bristol University working on
real-time MIMO-OFDM test beds, and from 20072012 he was a Research Fellow with Hokkaido
University working on MIMO signal processing. He
currently is a researcher at ATR, Kyoto, Japan working on spectrally efficient modulation techniques andfrequency recovery for satellite communications. His current research interests
include synchronization, MIMO signal processing and coding. Dr Webber isa member of the IEEE and IEICE.

150

Average Iterations

100

50

0
0

MWBF
IRRWBF
Dual θ=0.63/0.97
Dual θ=0.63/0.97 + perturb.
Dual θ early-stop
θ=0.625
θ=0.625 + perturb.
θ=0.97
θ=0.97 + perturb.

1

2

213

3

4

5

6

Eb/N0 (dB)

Fig. 18. Average number of iteration versus Eb /N0 (dB) for Code 2 with
perturbation.

R EFERENCES[1] R. Gallager, “Low Density Parity Check Codes (LDPC),” Mass. Inst.
Tech. Press, Phd. Thesis, 1963.
[2] D. MacKay and R. Neal, ‘Good error-correcting codes based on very
sparse matrices,” Crypt. and Coding 5th IMA conf., Jul. 1995.
[3] W. Chung, and J. Cruz, “An improved belief-propagation decoder for
LDPC-coded partial-response channels,” IEEE Trans. Magnetics, vol. 46,
no. 7, pp. 2639-2648, Jul. 2010.
[4] J. Webber, T. Nishimura, T. Ohgane, and Y. Ogawa, “A study on adaptive
thresholds for reduced complexity bit-flip decoding,” International Conference on Advanced Communications Technology (ICACT’12), Phoenix
Park, Pyeongchang, Korea, Feb. 2012.
[5] D. J. MacKay, “Information thoery, inference, and learning algorithms,”
Cambridge University Press, 2003.
[6] R. M. Tanner, “A recursive approach to low complexity codes,” IEEE
Trans. Inform. Theory, vol. 27, no. 5, pp. 533-547, Sep. 1981.
[7] M. Luby, M. Mitzenmacher, A. Shokrollahi, and D. Spielman, “Analysis
of low density codes and improved designs using regular graphs,” IEEE
Trans. on Inform. Theory, vol. 47, pp. 585-598, 2001.
[8] Y. Kou, S. Lin, and M. Fossorier, “Low-density parity-check codes basedon finite geometries: a rediscovery and new results,” IEEE Trans. Inform.
Theory, vol. 47, no. 7, pp. 2711-2736, Nov. 2001.
[9] F. Guo and L. Hanzo, “Reliability ratio based weighted bit-flipping
decoding for LDPC codes,” IEE Electron. Lett., vol. 40, no. 21, pp. 13561358, Oct. 2004.
[10] C. H. Lee and W. Wolf “Implementation-efficient reliability ratio based
weighted bit-flipping decoding for LDPC codes,” IEE Electronics Lett.
vol. 41, no. 13, pp. 755-757, Jun. 2005.
[11] T. Wadayama, K. Nakamura, M. Yagita, Y. Funahashi, S. Usami,
and I. Takumi, “Gradient descent bit-flipping algorithms for decoding
LDPC codes,” International Symposium on Information Theory and its
Applications (ISITA’08)., Auckland, New Zealand, Dec. 2008.
[12] J. Cho and W. Sung, “Adaptive threshold technique for bit-flipping
decoding of low-density parity-check codes,” IEEE Comm. Letts., pp.
857-859, col. 14, no. 9, Sep. 2010.
[13] M. Ismail, I. Ahmed, J. Coon, S. Armour, T. Kocak, and J.P. McGeehan,“Low latency low power bit flipping algorithms for LDPC decoding,”
IEEE PIMRC10, Sep. 2010.
[14] E. Psota, and L. Perez, “Iterative construction of regular LDPC codesfrom independent tree-based minimum distance bounds,” IEEE Comm.
Letts., vol. 15, no. 3, Mar. 2011.
[15] D. MacKay, “Encyclopedia of sparse graph codes [Online],”
URL: http://www.inference.phy.cam.ac.uk/mackay/codes/data.html, Accessed Feb. 2011.

Toshihiko NISHIMURA Toshihiko NISHIMURA
received the B.S. and M.S. degrees in physics
and Ph.D. degree in electronics engineering from
Hokkaido University, Sapporo, Japan, in 1992, 1994,
and 1997, respectively. In 1998, he joined the Graduate School of Engineering (reorganized to Graduate
School of Information Science and Technology atpresent) at Hokkaido University, where he is currently an Assistant Professor of the Graduate Schoolof Information Science and Technology. His current
research interests are in MIMO systems using smart
antenna techniques. Dr. Nishimura received the Young Researchers’ Award of
IEICE Japan in 2000, and the Best Paper Award from IEICE Japan in 2007.
Dr. Nishimura is a member of the IEEE.

Takeo OHGANE Takeo OHGANE received the
B.E., M.E., and Ph.D. degrees in electronics engineering from Hokkaido University, Sapporo, Japan,
in 1984, 1986, and 1994, respectively. From 1986 to
1992, he was with Communications Research Laboratory, Ministry of Posts and Telecommunications.
From 1992 to 1995, he was on assignment at ATR
Optical and Radio Communications Research Laboratory. Since 1995, he has been with Hokkaido University, where he is an Associate Professor. During
2005-2006, he was at Centre for Communications
Research, University of Bristol, U.K., as a Visiting Fellow. His interests are
in MIMO signal processing for wireless communications. Dr. Ohgane received
the IEEE AP-S Tokyo Chapter Young Engineer Award in 1993, the Young
Researchers’ Award of IEICE Japan in 1990, and the Best Paper Award fromIEICE Japan in 2007. Dr. Ohgane is a member of the IEEE.

Yasutaka OGAWA Yasutaka OGAWA received
the B.E., M.E. and Ph.D. degrees from Hokkaido
University, Sapporo, Japan, in 1973, 1975, and1978, respectively. Since 1979, he has been with
Hokkaido University, where he is currently a Professor of the Graduate School of Information Scienceand Technology. During 1992-1993, he was with
ElectroScience Laboratory, the Ohio State University, U.S.A., as a Visiting Scholar, on leave from
Hokkaido University. His interests are in adaptive
antennas, mobile communications, super-resolution
techniques, and MIMO systems. Dr. Ogawa received the Young Researchers’Award of IEICE Japan in 1982, and the Best Paper Award from IEICE Japan
in 2007. Dr. Ogawa is a member of the IEEE.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

214

Classification of N-Screen Services, Scenarios and
its Standardization
Changwoo Yoon, Hyunwoo Lee, Won Ryu*Electronics & Telecommunications Research Institute, Daejeon, Korea

cwyoon@etri.re.kr, hwlee@etri.re.kr, wlyu@etri.re.kr
Abstract—By the advent of IPTV and smart TV, thebroadcasting is transmitted using Internet. Bi-directional
programs are appeared on broadcasting services. The
convergence service combining with communication, information
and web service are appeared too.
N-Screen service is a killer service of smart TV. It uses several
terminals, either fixed or mobile, to provide bi-directional,
convergence and personal services with broadcasting service.
N-Screen service can be classified into three categories: first,
OSMU (One Source Multi Use) case, providing same contents toterminals having different capabilities such as screen size, CPU
speed, memory, codec, network speed, etc. Second case is avertical handover, continuous watching of content using different
terminal. Third case is a collaborative service among multiple
terminals. For example, a customer is watching soap opera using
TV, while watching a specific scene related information or
advertisement using his PAD or smart phone.
In ITU-T SG13, the Y.sof (Service Scenario over FMC) was
standardized. It defined detailed overall service scenarios using
feature extraction of seamless mobile convergence service on
several networks such as WiFi, 3G, WiMAX/WiBro. This
standard extracts key features of five key elements: person,
terminal, network, content, and service. Then, it analyzes
relationships among key elements and suggests overall service
scenario model.
The service scenario model can be easily adopted on describing
N-Screen service scenario because Y.sof handles scenario cases
among several fixed or mobile terminals.
In this paper, I will introduce Y.sof and classification of N-Screen
service scenarios described using the standard. Also I will refer
the standardization issues of N-Screen and its technologies.

enabling users to enjoy the same content or services
seamlessly, irrespective of which device / medium is used. N
Screen which is recently getting attention falls into the broader
concept of Multi-Device Service. N Screen is about enabling
the user to use multiple devices, which means, it should bemade up of integrated platforms for multi-devices. The coreelement of N Screen Service is a platform that mediates theuse of content or services on multiple devices. [1,2]N Screen Services and Multi-Device Services are often
used in the same meaning. Technically speaking, however,Multi-Device Service is a broader concept that encompasses N
Screen Service. Multi-Device Service is, literally, to provide
the same content or services on a variety of devices. Alongwith the evolution of the ICT environment, Multi-Device
Services have been evolving as well.
ITU-T Y.2720 Sup.14 describes overall scenario model forvarious services over FMC. In the overall scenario model,
shown in Fig. 1, the red dotted box describes N- screen service
scenario situation of an end user using his/her terminal devices
while the features of service are operated on several devices
synchronously. [3,4,5] An example is the case of displaying
the same content on different types of terminals converting
content quality. Another scenario is of an end user using
his/her terminal devices while the unit feature of converged
service is operated on separate devices synchronously.
Examples are the case of displaying VOD on TV+STB,
starting VOD using EPG displayed on mobile phone, anddisplaying VOD related information on a Notebook.

Keywords— IPTV, Smart TV, N-Screen, OSMU, FMC

I. INTRODUCTION
Recently, the environment where consumers use multiple
devices according to time and place is being created. In thissituation, multi-device platforms are gaining attention for
Manuscript received September 15, 2012. This work was supported in partby the EU ITEA-2 project with Grant No.10028 “Web-of-Objects” (WoO)
funded by MKE and supervised by KIAT.
Changwoo Yoon is Principle Researcher with the Electronics &Telecommunications Research Institute, Daejeon, Korea (Phone: +82-42-8606543; fax: +82-42-860-5611; e-mail: cwyoon@etri.re.kr).
Hyunwoo Lee is Team Leader with the Electronics & Telecommunications
Research Institute, Daejeon, Korea (Phone: +82-42-860-6526; fax: +82-42861-1342; e-mail: hwlee@etri.re.kr).
Won Ryu is Director with the Electronics & Telecommunications
Research Institute, Daejeon, Korea (Phone: +82-42-860-6290; fax: +82-42861-1342; e-mail: wlyu@etri.re.kr).

Figure 1. N-Screen part on Overall scenario model over FMC

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

In this paper, we describe various kinds of N-screen service
scenarios based on overall scenario model over FMC.
II. GENERAL DESCRIPTION OF N-SCREEN SERVICES
By the advent of IPTV and smart TV, the broadcasting
through Internet is generalized. The bi-directional service isintroduced on broadcasting by using Internet’s bi-directionaltransmission characteristic. The convergence services
combined with telecommunication, information, web andpersonalized broadcasting services are appeared. Major
feature of smart TV is intelligence such as smart search,
extraction and UI technology providing customer targeted
information on the limited size of TV screen. N-Screen
service is a killer service of smart TV providing various kindsof bi-directional, converged, personalized and intelligent
contents and services to multiple fixed or mobile devices
We can classify N-screen service scenarios as three cases.
First case is sharing same content or service on more than one
screen among multiple kinds of screen; For example, it is aservice on which an end user can watch the same content onvarious terminals such as TVs with Setop, PCs, notebooks,
PMPs, or smart phones.

215

Second case is consuming same contents on several devices
continuously. . It is considered as one of the representative
service supporting service mobility among multiple kinds ofscreens.

Figure 4. Classification Case II of N-Screen Service: HandoverThird case is providing collaborating service among
multiple devices. The service provides to customer is consistof different shape of services that is operated on separate
screen. For example, PAD operates as EPG terminal for an
IPTV. A customer can select a program he wants to see andthe selected program is displayed on TV screen withsynchronous manner.

Figure 2. Classification Case I of N-Screen Service: OSMU

Another example of OSMU is migration of service among
terminals. Figure 3 shows an example of OSMU of service.
User can use smart TV application showing maps of specificlocation, while the user want to send an e-mail to her/his
friend. Because it is difficult to write an e-mail on smart TV,
the user migrate the e-mail window to his smartphone.
Figure 5. Classification Case I of N-Screen Service: Collaboration

Figure 3. Classification Case I of N-Screen Service: OSMU of service

More advanced example of case III collaboration is ASMD
(Adaptive Source Multi-Device) shown on figure 6. In ASMD
service scenario, user can divide and combine services making
convergence services. In figure 6, three persons are watching
TV while they are using their own handheld device such as
mobile phone and pad. While watching music program, each
person are using music program related services. Person A
watches different angle scenes. Person B watches celebrity
news about the singer who is showed in TV screen. Person C
watches album about the singer. By a touch of each person’s
handheld device, the services shown in each person’s device
transmitted to big TV screen and displayed in combined mode.
This ASMD type of N-Screen service will bring newwatching pattern on smart TV by providing collaborating TV
services with mobile devices. There will bring new business

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

opportunities by providing convergence services combining
broadcasting, telecommunications and web service freely.

216

Networks: This is a key part supporting mobility and
FMC of the terminal. The network is composed of anaccess network and a core network. We can classify theaccess network by its technology basis, whether it is“Fixed”, “Mobile” or “Wireless”.
 Contents: This is a key part that is presented by media
files and media processing. The contents form services
for the end user.
 Service: This is a key part for the end users providing a
set of functionalities enabled by a service provider.
I will describe the features of key element bellows.

Figure 6. Classification Case III of N-Screen Service: ASMD

III. Y.SOF: SERVICE SCENARIOS OVER FMC
One of the essential benefits of the NGN is a supporting of
convergences such as a fixed mobile convergence (called
FMC), telecom-broadcasting converged service like IPTV.
ITU-T produced several Recommendations on FMC and IPTV,
especially in the ITU-T Y.2000 series of Recommendations.
ITU-T is developing more detailed aspects of supporting theFMC, taking consideration of the fact that various services are
waiting to utilize the FMC as their service infrastructure,
which will extend their service coverage as well as give more
benefits to the user.
This Supplement to the ITU-T Y.2000-series
Recommendations on the scope of service scenarios over the
FMC provides service scenarios which are used over FMC.
This Supplement uses the features of involved key elements of
FMC to guide how services can be provided in detail. This
Supplement also introduces overall configurations andscenario models to identify service scenarios over the FMC.
There are five key elements to define: Person, Terminal,
Network, Contents and Service.
 Person (End User): This is a key subject for consuming
services over FMC. It is characterized by the location,that is, “Same Location” or “Change Location”
 Terminal: This is a key device supporting services forthe person. The person can own multiple terminal
devices either fixed, mobile or Wireless. The terminal
can be classified into two categories (Single mode andMulti mode) by the number of network connection
interfaces. As single mode terminal provides a single
connection to the network that is either fixed or mobile
or wireless. A multi-mode terminal provides several
ways of connection to the networks. In this supplement,
we use a dual mode terminal as an example of a multimode terminal. The dual mode terminal provides dual
connections to the network that are either fixed, mobile
or wireless.

A. Behavioural aspect of Person
A person will use terminal device either keeping the same
location or changing location. Changing the location causes
supporting of mobility if the person wishes to keep current
services. So we can determine the features of the person’s key
element by the location, whether it is, “Same location” or“Change location”.

Figure 7. Features of Person

B. Capability aspect of Terminal
The terminal is operated following the behaviour of theperson while trying to keep the service continuity. As a resultof following person’s behaviour, it is decided whether thecapability of the terminal should be changed or not.

Figure 8. Example cases of Person’s behaviour of terminal usage

For example, figure 8 shows three cases of the terminal
capability change or no-change according to the behaviour ofthe person.
First case shows the situation of the person watching highquality IPTV at home. He has to go to his company and hewants to watch the program seamlessly using his mobile
phone. In this case the capability of the terminal is changed:
from single mode, fixed network TV to dual mode, wirelessnetwork mobile phone.
Second case shows the situation of the person moving his
location from A (home, wireless network area) to B (company,

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

mobile network area), while continuing the service. In thiscase the capability of the terminal is changed: network modechange of mobile phone from wireless network to mobile
network.
Third case shows the situation of the person moving his
location from B (company, mobile network area) to C
(university, mobile network area), while continuing theservice. In this case, there is no terminal capability change
because it is merely a change of mobile base station.
Therefore we can determine the features of the terminal key
element by the capability, whether it is, the “Same capability”
or “Different capability”.

217

There are two cases for the Different quality: upgrade or
downgrade.
Example case for the upgrade is a handover from a mobile
terminal to a TV requiring high quality video and network.
The change of quality is required such as codec and resolution(CIF to HD).
Example case for the downgrade is a handover from a TV
to a mobile terminal. The change of quality is required such as
codec and resolution (HD to CIF).
E. Integration aspect of Service
Service feature can be classified by its integration, whether
it is, “service” or “Converged service”. A converged service
can be composed of several services.

Figure 9. Features of Terminal

C. Capability aspect of Network
The features of access network needs to be considered by
the technology basis: Fixed, Mobile and Wireless. For those
networks, the network capability may vary from one network
to another. For example, a fixed broadband network is able tosupport much higher bandwidth than cellular wireless.
Core network is a delivery part managing overall traffictransferring process such as re-routing, traffic congestion andfailure in the routing path etc. The core network’s capability
should be impacted by end user behaviour such as changing
access networks. Therefore, core network features can be
determined by “Same Capability” and “Different Capability”.

Figure 12.

Features of Service

F. Overall FMC configuration modelThis clause shows an overall high level configuration
model over FMC. This is determined by considering thefeatures of FMC key elements, and their characteristics.

Figure 13. Overall configuration model using features of key elements

Figure 10.

Features of Network

D. Quality aspect of Contents
We can determine features of the content by its quality suchas trans-coding QoS parameters, such as encoding codec,
resolution (CIF, SD, HD) or frame rate Same quality: This is the case in which the source ofcontents should maintain the quality. From the contents
point of view, there is no need to change trans-coding
QoS parameters such as codec, resolution or frame rate.
 Different quality: This is the case in which the source ofcontents should change the quality. From the contents
point of view, there is a need to change the trans-coding
QoS parameters such as codec, resolution or frame rate.
Same
Quality

Different
Quality

Figure 11.

Features of Contents

In this figure, a person will use his/her terminal device
either maintaining the same location or changing the location.
Changing the location demands support of mobility if theperson wishes to keep services while moving.
Then, a terminal device (either single mode or multiple
modes) is operated to follow the behaviour of the person while
keeping the network connection as much as possible. As aresult of following user behaviour (handover to other terminalor change of network connection), the capability of theterminal function should be changed.
In the case of access networks, it should be continuously
changed according to the end user’s behaviour such as moving
or changing the connection among fixed, mobile and wirelessaccess networks. One important thing here is that changing theaccess network causes change in the connecting capability likebandwidth, or overall traffic management process.
Sources of contents are influenced by the mobility, because
the result of mobility either caused at terminal device oraccess network requires changing the QoS (downgrading or
upgrading). Therefore this can be characterized by the quality:“Same Quality” or “Different Quality.”
Service providers provide several types of service such as
content on demand, real-time broadcasting IPTV service, IMS
based caller ID service, information display etc. Service

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013providers can provide converged services by combining
services such as displaying caller-ID, content related
information while the customer is watching VOD.
Using overall FMC configuration model, we can derive
overall scenario model over FMC shown in Figure 1.
In figure 1, scenario number 6 shows N-screen scenariocase. This is a scenario of an end user using his/her terminal
devices while the features of service are operated on several
devices synchronously. An example is the case of displaying
the same content on different types of terminals converting
content quality. Another scenario is of an end user using
his/her terminal devices while the unit feature of converged
service is operated on separate devices synchronously.
Examples are the case of displaying VOD on TV+STB,
starting VOD using EPG displayed on mobile phone, anddisplaying VOD related information on a Notebook.
IV. N-SCREEN SERVICE SCENARIO: CASE I
Case I N-screen is a service on which an end user canwatch the same content on various terminals such as TVs with
Setop, PCs, notebooks, PMPs, or smart phones. Each screenof terminals is used in a cooperative and synchronous manner.

of the end-user request the terminal to provide
current service to terminal B.
3.

[Service Provider: preparing N-screen service forterminal B & C] The N-screen service requested todisplay same content of terminal A to terminal B, C
is processed by service provider’s service control
function. The service control functions of the service
provider adjust quality of contents to network
bandwidth to be changed. The service control
functions decide to transcode origin contents into
suitable contents to be changed, considering display
sizes to be changed. Application functions of theservice provider may have contents mediation
functions like codec convertors. The application
functions converts content into small-size or big-size
display, depending on the device profile and accessnetwork bandwidths.

4.

[Service Provider -> Terminal B,C -> Enduser(Person): using N-screen service] The newly
generated contents are delivered into designatedstorage managed by content delivery functions. The
content delivery functions send adjusted contents toterminal B and C.
Terminal

End-User(Person)

STB
TV

Router A

Service Platform

218

Network Provider

Service Provider

Content
End User
Functions1
2

Terminal
Functions

1

Application
Functions

Network
Functions

Terminal A

Mobile PhoneAP
Laptop

End-User (Terminal)

Router B

Network
Provider2Contents Mediation

Service
ProviderContent
Provider4

A. Service scenarios of N-screen service case watching samecontent on multi-devices
1.

2.

[Service Provider > End-user(Person): watching Nscreen service on Terminal A] End-user(Person) is
watching contents provided by content delivery
functions of a service provider via network functionsof a network provider using terminal A (Forexample, a TV with setop). The end-user may have aplan to watch the content using terminal B (Forexample, a Laptop) and Terminal C (For example, amobile phone) at the same time.
[End-user(Person)-> Service Provider: request Nscreen service to Terminal B,C] End user functions

1

Terminal B

4

Figure 14. Overall configuration of N-screen case watching same content

Figure 14 is a 3-screen service with content sharing. Three
screen service allows an IPTV service subscriber consume
IPTV service contents on TV, PC, and wireless screens. The
basic type of 3-screen service is sharing the same IPTV
service contents on more than one screen among the threekinds of screens.

Terminal
Functions

Terminal
Functions
Terminal C

Service Control
Functions3

4

4

Content
Delivery
FunctionsFigure 15. Service scenario of N-screen case watching same content

V. N-SCREEN SERVICE SCENARIO: CASE II
Case II N-screen is consuming same contents on several
devices continuously. It is considered as one of therepresentative service supporting service mobility among
multiple kinds of screens. Figure 16 is one of the examples ofcase II N-screen service. Below is the service scenario of case
II N-screen: service continuity.
A. Service scenarios of N-screen service case consuming
same content on multi-devices continuously.
1.

[Service Provider-> End-user(Person): watching Nscreen service on Terminal A] End-user (Person) is
watching contents provided by content delivery
functions of a service provider via network functionsof a network provider using terminal A (Forexample, a TV with STB in a living room).

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

Terminal

End-UserEnd User
Functions219

1

Terminal
Functions

Network Provider

Service Provider
Application
Functions

Network
Functions1

Terminal A
2
Terminal
Functions5

1

Terminal B

3

Service Control
Functions4

6
Terminal
Functions5

Terminal C

Content
Delivery
FunctionsFigure 17. Service scenario of N-screen case consuming same content on
multi-devices continuously
Figure 16. 3-screen service with service continuity

2.

[End-user(Person): moving N-screen service on
Terminal B] The end-user moves from living room
to room A to watch the same content continuously
using terminal B (For example, a Laptop).

3.

[Terminal B-> Service Provider: request N-screen
service to Terminal B] Terminal B is authenticatedas an N-screen service and requests the service
provider to send the same content.

4.

[Service Provider: preparing N-screen service forterminal B] The N-screen service requested todisplay same content continuously on the terminal B
is processed by service provider’s service control
function. The service control functions of the service
provider adjust quality of contents to network
bandwidth to be changed. The service control
functions decide to transcode origin contents into
suitable contents to be changed, considering display
sizes to be changed. Application functions of theservice provider may have contents mediation
functions like codec convertors. The application
functions converts content into small-size or big-size
display, depending on the device profile and accessnetwork bandwidths.

5.

[Service Provider -> Terminal B -> Enduser(Person): using N-screen service] The newly
generated contents are delivered into designatedstorage managed by content delivery functions. The
content delivery functions send adjusted contents toterminal B.

6.

[End-user(Person): moving N-screen service on
Terminal C] The end-user moves from room A toroom B to watch the same content continuously
using terminal C (For example, a Cellular phone).
The terminal C shows the same content continuously
through the same process as described above.

VI. SCENARIO FOR N-SCREEN SERVICE CASE III SCENARIO 1:
TARGETED ADVERTISING SERVICE TO SEPARATETERMINAL

I will describe N-screen service scenario case III example,
targeted mobile advertisement service that is a collaborative
case.
Targeted advertisement service is providing Ad to person’s
mobile terminal while he is watching VOD on TV screen.
During watching a VOD channel, logging and selecting
keyword may be required to initiate the service. Choosing‘keyword’ plus ‘interests’ among menus may load Ad service
web pages, which gathers related Ad contents and metadatafrom Web and 3rd party Ad server. The login user’s interests
are dependent on user profile. In case that the keyword
belongs to ‘people’, the person may appear in aggregatedcommercial advertisement. As supplementary Ads, banneradvertisement, which is inserted by advertiser’s request, islocated near targeted advertising service or other convergence
services.

Figure 18. Targeted Advertising Service

The split EPG terminal has function to control IPTV
service via home AP. A mobile user in the right of figure 18
watches golf sports channel in VOD service. He already gave
his preference to the profile enabler via open service platform.
For instance, since the profile enabler knows he enjoys golf as
outdoor sports, when he detects golf driver on watching golf
contents, the mobile ad process enabler may give him ad
moving picture about newly released golf driver. Mobile ad

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

process enabler may give helpful information to him as wellas connect to the purchase step by the mobile device.

SOAP

SOAP

Open
Service
Platform

Open APIMedia
Enabler

MobileAdvertisement
Process Enabler

platform. As soon as golf moving picture is delivered to an
IPTV user, ad request event is generated.
A. Service scenarios of N-screen service case collaborating
convergence service: targeted advertising service toseparate terminal.

MobileAdvertisement
SystemIPTV Media
System1.

[Service Provider -> End-user(Person): watching
VOD channel on Terminal A] End-User(Person) is
watching VOD channel with Ad service which isprovided content delivery functions of service
provider via network functions of network provider
using terminal A (For example, a TV with setop).
We suppose that end-user with Terminal A is awoman in forties and she likes to collect jewelry.

2.

[End-user(Person) -> Service Provider: initiate
Targeted advertisement service on Terminal A] The
end-user can login and select keyword to initiate thetargeted advertisement service. By choosing‘keyword’ plus ‘interests’ on menues of Terminal A,
related Ad contents and metadata from Web and3RD party Ad Server is gathered by service control
functions of the service provider.

3.

[Service Provider: prefaring and generate targeted
web pages for Terminal A] Targeted advertised
content is generated with gathering Ad contents ,metadata and profile information of end-user withTerminal A

4.

[Service Provider -> Terminal A-> Enduser(Person): using targeted advertised service withTerminal A] The newly generated content which hastargeted information(For example, jewelry shops) for
end-user with Terminal A is delivered to Terminal A
by content delivery functions.

5.

[End user(Person) -> Service Provider : initiate
Targeted advertisement service on Terminal B] EndUser(Person) has a hand-helded device such as
smartphone. He lives with his mother who is enduser using Terminal A. Let’s suppose that end-userwith Terminal B is a man in twenties and he isinterested in car so much. He can also initiate
targeted advertised service by logining and selecting
keyword. At this time, user profile information of
end-user with Terminal B is transmitted to theservice control functions of the service provider.
Related Ad contents and metadata for end-user withTerminal B from Web and 3RD party Ad Server isalso gathered by service control functions of theservice provider.

6.

[Service Provider: prefaring and generate targeted
web pages for Terminal B] Targeted advertised
content is generated with gathering Ad contents ,metadata and profile information of end-user withTerminal B

ProfileEnablerEnterprise Service Bus

IPTV

Locationbased Ads

Home Appliances
Department AP

Split EPG
Terminal

Home AP

Targeted Ads

Figure 19. Example system of Targeted Advertising Service
Open Service Platform
Split EPG
Terminal(IPTVUsr01)

IPTV Screen

Media Enabler(VODSrv01)

Profile Enabler(IPTVProf01)

Mobile AD
Process
Enabler(ADSrv01)

Mobile AD
Agent(ADProv01)

Deliver Golf Moving Picture

Request User
Preference

Search Golf Ads
Return Ad

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

Request Golf VOD
Deliver Golf
Moving Picture

220

Request AD
Event
Event time

Deliver Golf Moving picture

Figure 20. Service flows of Targeted Advertising Service

Figure 19 shows example of targeted advertisement using
SDP. Mobile ad agent may be person or sub-system of an adservice provider. He also watches a golf VOD service via
media enabler within open service platform. The mobile adprocess enabler searches a golf ad among many ad contents ofa mobile ad agent. That golf ad is related with revealed
product in golf moving picture. The mobile ad process enabler
acquires user preference and whether mobile users own split
EPG terminals. The convergence enabler then inserts ad
metadata into golf VOD. A mobile user with split EPG
terminal requests to view that golf contents to open service

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

7.

[Service Provider -> Terminal A-> Enduser(Person): using targeted advertised service withTerminal B] The newly generated content which hastargeted information(For example, car Ad) for enduser with Terminal B is delivered to Terminal B bycontent delivery functions.
Terminal

End-User(Person)
End User
Functions1
2
4

Terminal
Functions

Network Provider

1

Application
Functions

Network
Functions

Terminal A
2
1
Service Control
Functions5
7

Terminal
Functions
Terminal B

4

3 6

5
7

functions of the end-user requests their own terminal
B, C, D to transfer current service to terminal A todiscuss about the game by watching each person’s
service together. The N-screen service requested is
to display combined contents of services of terminal
B, C, and D. The N-screen Service control function
receives the request.
4.

Service Provider

Content
Delivery
Functions[Service Provider: preparing N-screen service forterminal A] The N-screen service control functionsof the service provider adjust quality of services to
fit the capabilities of different terminals. The
Application function receives adapted content fromservice control function then sends it to terminal A
using collaboration function of N-screen service
control function.

VII.

N-SCREEN SERVICE SCENARIO CASE III SCENARIO
3: ASMD
ASMD (Adaptive Source Multi-Device) is a collaborative
case of N-screen service. The concept of collaborative case of
N-screen is an existence of collaboration among multi-screen
showing different content or services for screens. The ASMD
adds content/service adaption concept upon the collaboration.
For example, if a user watching TV content related news using
his mobile phone, then he wants to transfer the news to big TV
screen to share the news with his family. Because the size andresolution is different between TV and mobile phone, proper
adaption should be done on service presentation. In ASMD
type of collaborative N-screen service, it is not sufficient toprovide service only by collaboration.
A. Service scenarios of N-screen service case III
collaborating: ASMD
1.

[Service Provider > End-user(Person): watching TV
program(baseball) on Terminal A(Big-screen TV)]
User1, 2, 3 (3 Persons) are watching TV program on
Terminal A(Big-screen TV) provided by content
delivery functions of a service provider via network
functions of a network provider.

2.

[Service Provider > End-user(Person): using service
on Terminal] User1, 2, 3 (3 Persons) are using
services on their own handheld device (Terminal B,C, D). The services are related with TV program
shown on Terminal A (Big-screen TV). For
example, User 1 watches multi-angle scene of thebaseball game on his smartphone. User 2 watches
shorts news about the played baseball game on his
tablet. User 3 uses baseball-game statistics service.

3.

[End-user(Person)-> Service Provider: request
sending their services to Terminal A] End user

Terminal

End-UserEnd User
FunctionsFigure 21. Service scenario of N-screen case using targeted advertising
service

221

User 1End User
FunctionsUser 2End User
FunctionsUser 3

1

3
1 2

Terminal
Functions
Terminal A
Terminal
Functions

1 3
Terminal B2
3

2

Terminal
Functions

Network Provider

1,2

Application
Functions

Network
Functions

4

Broadcasting
Telecom
IT/Web

1,2
3

1,2
3

N-Screen
Service Control
Functions

4

Collaboration
Adaptation

1,2
3

Terminal C
Terminal
Functions

Service Provider

4

Content
Delivery
Functions

1,2
3

Terminal D

Figure 22. Service scenario of ASMD, N-screen collaborative case

VIII. CONCLUSIONS
The major keywords for N-screen service are cloudcomputing and social TV. The cloud computing is expending
its technological importance and business area. It is possible
to provide information synchronization service inside homeand dynamic resource allocation using cloud computing’s
technologies such as virtualization, remote storage and mobile
cloud [7]. Those kinds of services are closely related to Nscreen service.
It is important to combine with social TV to invigorate Nscreen service business. Using social TV concept, it is easy tointroduce N-screen into IPTV or smart TV because thecustomer can use collaborative services with main VOD orchannel content. We described the targeted advertisement
service that the customer watches advertisement content on
different screen while he watches TV. At that scenario, weused service delivery platform to deliver IPTV andadvertisement [8, 9, 10]N-screen service standardization is progressing through
ITU-T Q24/SG13 for the service scenarios and use cases. The
standardization of architecture and cloud computing related
will be progressed.
In this paper, we described N-screen service concept, Nscreen service classification, service scenario description
method, and service scenarios. Among three cases of N-screen
service classification, the case III, collaborative case among

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

several terminals is most importance in the aspect of businessmodel. Right now, the telecommunication venders arefocusing to provide OSMU and seamless case of N-screen.
However the case III will catch user’s attention because of itsvariety of service cases and features.
REFERENCES
[1]

[2]

[3]
[4]
[5]
[6]
[7]

[8]

[9]

[10]

Changwoo Yoon, Hyunwoo Lee, Won Ryu, Bongtae Kim, “IPTV
Service and Technology Evolutions,” Journal of Korea Information
and Communication Society, pp. 1-9, August, 2008.
Changwoo Yoon, Shinmo Kim, Hyunwoo Lee, “Convergence Service
Implementation based on Service Delivery Platform and Research
Issues,” International Technical Conference on Circuits/Systems,
Computers and Communications, pp. 1080-1083, July 2009.
ITU-T, Next Generation Network Global Standards initiative (NGNGSI), http://www.itu.int/ITU-T/ngn/index.phtml"Service scenarios over FMC," ITU-T Recommendation Series Y.2720
Supplement 14, 2011.1.
"Supplement on IPTV service use cases," ITU-T Recommendation
Series Y Supplement 5, 2008.
"Web-based IPTV brokering service models and scenarios," ITU-T
Draft Recommendation Series Y.iptvbs, September 2010.
Changwoo Yoon, Mohammad Mehedi Hassan, Biao Song, Hyunwoo
Lee, Won Ryu and Eui-Nam Huh, “Dynamic Collaborative Cloud
Service Platform: Opportunities and Challenges,” ETRI Journal,
Vol.32, No.4, pp. 634-637, August 2010.
Changwoo Yoon, Hyunwoo Lee, Won Ryu, “Design of Layered
service delivery platform for enabling I-centric convergence service,”
Communications and Information Technology, 2009. ISCIT 2009. 9thInternational Symposium on, pp. 243-247, September, 2009.
Changwoo Yoon, Hyunwoo Lee, “Service delivery platform forconvergence service creation and management,” Proceeding ICACT’10
Proceedings of the 12th international conference on Advanced
communication technology, pp.1335-1338 , Feb, 2010.
Changwoo Yoon, Hyunwoo Lee, Won Ryu, “Next generation IPTV
Platform,” 2010 9th International Conference on Optical Internet(COIN), pp. 1-3, July 2010.

Cloud computing,information retrieval.

222

Changwoo Yoon received the B.S. degree from
Sogang University, Seoul, Korea, in 1990. He
received M.S. degree from POSTECH, Pohang,
Korea, in 1992. He received Ph.D. degree in
Computer & Information Science & Engineeringfrom University of Florida, US, in 2005. Currently
he is principal researcher in Creative Future Researchteam, ETRI and adjunct professor at UST. His
current research interests include N-Screen, IPTV,
SOA, Service creation/delivery technology andHyunwoo Lee received M.S. and Ph.D. degrees in1995 and 2005, respectively, in Korea Aerospace
University (KAU). He is currently a principal
research engineer and team leader in convergence
service networking research team, smart screen
convergence research department, ETRI. His main
research interests include heterogeneous wirelessaccess network, Mobile P2P, open IPTV platform in
NGN. His current research interests include cloudcomputing and platform.
Won Ryu, Ph.D received the BS degree in computerscience and statistics from Pusan National
University, Busan, South Korea, in 1983, and the
MS degree in computer science and statistics fromSeoul National University, Seoul, South Korea, in
1987. He received his PhD degree in informationengineering from Sungkyunkwan University,
Kyonggi, South Korea, in 2000. Since 1989, he has
been a managing director with the Smart screen
convergence research department, ETRI, Daejeon,
Korea. Currently, his research interests are IPTV, Smart TV, IMT-advanced,
and convergence services and networks and etc.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Communications
Technology(TACT)
Vol. Vol.
2, Issue
3, May20132013
ICACT Transactions
Transactions on
on Advanced
the Advanced
Communications
Technology(TACT)
2, Issue
3, May223
223

Dempster-Shafer Evidence Theory Based
Trust Management Strategy against
Cooperative Black Hole Attacks andGray Hole Attacks in MANETs
Bo YANG*, Ryo YAMAMOTO*, Yoshiaki TANAKA*,***Global Information and Telecommunication Institute, Waseda University, Japan**Research Institute for Science and Engineering, Waseda University, Japan
yangbo_youhaku@ruri.waseda.jp, ryo_yamamoto@moegi.waseda.jp, ytanaka@waseda,jp
Abstract—The MANETs have been experiencing exponential
growth in the past decade. However, their vulnerability tovarious attacks makes the security problem extremely prominent.
The main reasons are its distributed, self-organized andinfrastructure independent natures.
As concerning these
problems, trust management scheme is a common way to detectand isolate the compromised nodes when a cryptographymechanism shows a failure facing inner attacks. Among huge
numbers of attacks, black hole attack may collapse the networkby depriving the route of the normal communication. The
conventional proposed method achieved good performance facing
black hole attack, while failing to detect gray hole attacks. In thispaper, a Dempster-Shafer (D-S) evidence based trust
management strategy is proposed to conquer not only
cooperative black hole attack but also gray hole attack. In the
proposed method, a neighbour observing model based onwatchdog mechanism is used to detect single black hole attack byfocusing on the direct trust value (DTV). Historical evidence isalso taken into consideration to go against gray hole attacks.
Then, a neighbour recommendation model companied withindirect trust value (ITV) is used to figure out the cooperative
black hole attack. D-S evidence theory is implemented to
combine ITVs from different neighbours. Some of the neighbour
nodes may declare a false ITV, which effect can also be
diminished through the proposed method. The simulation is
firstly conducted in the Matlab to evaluate the performance of
the algorithm. Then the security routing protocol is implemented
in the GloMoSim to evaluate the effectiveness of the strategy.
Both of them show good results and demonstrate the advantagesof proposed method by punishing malicious actions to preventthe camouflage and deception in the attacks.

Manuscript received July 25, 2012.
B. YANG is with the Global Information and Telecommunication Institute,
Waseda University, Tokyo, 169-0051 Japan. phone: 090-0495-246104; fax:
090-0495-246104; e-mail: yangbo_youhaku@ruri.waseda.jp.
R. YAMAMOTO is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051 Japan. e-mail: ryo_yamamoto@moegi.waseda.jp.
Y. TANAKA is with the Global Information and Telecommunication
Institute, Waseda University, Tokyo, 169-0051 Japan. He is also with the
Research Institute for Science and Engineering, Waseda University, Tokyo,
162-0044 Japan. e-mail: ytanaka@waseda.jp.

Index Terms—Dempster-Shafer evidence, Trust management,
Direct trust value, Indirect trust value, Black hole attack, Grayhole attack, MANETs

T

I. INTRODUCTION

he mobile ad hoc networks (MANETs) are flexible
networks that inherit common characteristics found inwireless networks in general. However, it adds characteristics
specific to ad hoc networks, such as distributed, self-organizedinfrastructure and mobility. MANETs have been primarily
been implemented for tactical network related applications toimprove battlefield communications and survivability. Laterthe technology of MANETs is introduced to some otherscenarios such as disaster relief, chemical leakage monitoring,forest fire monitoring, etc. However, owing to its flexibility
and infrastructure-independent nature, it is particularly
vulnerable to various attacks compared with conventional
networks. And security problems in MANETs are mainly
aroused by its unique characteristic such as dynamic network
topology, limited bandwidth and limited battery power.
Concerning about these, cryptography mechanisms, intrusion
detection system (IDS) and efficient routing protocols are used
to ensure the security of MANETs.
However, theseconventional methods, especially cryptography method, fail tofilter out compromised nodes or the legitimated ones withmalicious actions. Although lots of efficient routing protocols
are proposed to ensure the security, routing attacks aroused bylegitimated nodes that will make the protocols effectiveness.
Possible attacks include passive eavesdropping, denial ofservice (DoS) attacks, wormhole attacks, sybil attacks etc. As
one type of DoS attacks, black hole attack can cause
catastrophic damage to normal communication of a large areain the network. The black hole nodes can launch routing
attacks to deprive the routing path and relative operation suchas dropping packets. Most of the existing detection strategy
either spends a large overhead or cannot prevent thecooperative black hole attack effectively. This paper focuses

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol. 2,Vol.
Issue
2, Issue
3, May3, May20132013

on the black hole attack and gray hole attack that malicious
nodes pretend as if they have the shortest path to thedestination and then deprive the routing.
In order to detect a single black hole attack as well as acooperative black hole attack, a neighbour nodes observation
model (NNOM) and a neighbour recommendation trust model(NRTM) based on the former one is given in our previous
study [1]. The method introduces a trust mechanism to detect
inner attackers in ad hoc network. The NNOM is based on thewatchdog mechanism [2] and each node keeps on watching its
own neighbour nodes while judging its communication
behaviour. These statistical data are used to compute a directtrust value (DTV) that would be compared with a predefined
threshold to decide whether a neighbour node is a malicious
node or not. Even if a neighbour node acts as a normal node,
the node would not be trusted immediately since the next hopof the node considers the case that a cooperative black holeattack is hidden behind. That is, the NRTM is establishedamong the nodes and the other neighbour nodes are asked todeclare their opinion about the reputation of the two hopneighbour node. Furthermore, an indirect trust value (ITV) is
computed and simply compared with a predefined threshold todecide whether there is a cooperative black hole attack.
Malicious nodes may act abnormally after a long period
normal actions and their reputation still remains high. It is
known as another type of black hole attack: gray hole attack,which is taken in certain time period or to certain data packets.
This kind of attack is more harmful and even more difficult to
detect because of their malicious behaviour.
In this paper, historical evidence based NNOM and DTV
based on the Dempster-Shafer (D-S) evidence theory [3] areproposed to settle this problem. The proposed method makes
it difficult for each node to get a high reputation after a longrun, but easy to lose it. Moreover, the recommended
reputation from some neighbour nodes with ulterior motives
might confuse the final judgement of the mechanism. The
proposed method considers the data distance between two
reputation evidences [4] and that makes the cheating impact
on ITV minimized.
The rest of this paper is organized as follows. In section II,
the single black hole attack and the cooperative black holeattack are introduced firstly.
Then related works aredescribed.
In section III the D-S evidence theory is
introduced. Section IV describes the details of the proposedmodel and algorithms along with the processes step of theFig. 1. Single black hole attack

224
224

strategy. The numerical result of algorithms and the network
simulation study of security protocol are evaluated in section
V and section VI, accordingly. Finally, section VII concludes
this paper.

II. BLACK HOLE ATTACK AND RELATED WORKS
A. Black Hole Attack and Gray Hole Attack
Varieties of routing protocol are implemented in the
MANET that can be classified into three categories:
proactive, reactive, and hybrid.
Some famous andrepresentative ones are destination sequence distance vector(DSDV) as proactive protocol, and dynamic source routing(DSR) along with ad hoc on-demand distance vector (AODV)
as reactive protocol [5]. Reactive routing protocols such as
AODV initiate a route discovery process at the beginning of acommunication when there is no valid and fresh route fromthe source node to the destination node. In this process,destination sequence numbers and unique broadcast IDs areused to ensure that the routes are loop-free and freshness of
the routes. The source node broadcasts route request (RREQ)
packets to all its neighbour nodes and the packets are relayed
to next hop node until legitimated destination node receives
them. After receiving RREQ, the destination node or anintermediate node with fresh route to the destination responds
it by unicasting a route reply (RREP) packet. When thesource node receives the RREP packet, the route isestablished. Then communication between the source nodeand the destination node would be available though the route.
In this route discovery phase, two types of black hole attack
can be found: single attack and cooperative attack. The
attacks can be aroused in two phases: the RREQ phase and the
RREP phase. In this paper, the proposed method focuses on
the black hole attack in the RREQ phase and the behaviour of
malicious nodes in the same period.
As is shown in Figure 1, a single black hole attack is done
merely by one malicious node. When a malicious node M
receives RREQ message from the source node S, it sends backfallacious RREP message immediately without relaying it to
the real destination node D through node 3. Since theFig. 2. Cooperative black hole attack

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Communications
Technology(TACT)
Vol. Vol.
2, Issue
3, May20132013
ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Technology(TACT)
2, Issue
3, Maymalicious nodes gerrymander the sequence number and the ID
number, node S is convinced that node D is the next hop of
node M. In this way, the malicious node is able to collect all
the packets from the source node and do further actions suchas dropping them or analysing the data. In order to tackle thisproblem, some conventional methods are proposed to settle
this kind of problem by monitoring the neighbour nodes’
activity. However, it does not work properly when two or
more malicious nodes cooperate together.
Figure 2 illustrates an example of cooperative attack withmalicious node M1 and M2. When the source node S tries tofind a route to the destination node D, it broadcasts RREQ tothe destination node. Since node 2 might watches the
behaviour of node M1 closely, M1 tries to pretend to be anormal node and just relay the RREQ to node M2. After nodeM2 receives RREQ from node M1, it begins malicious actionthat is the same as the single attack. In this way, node M1 andM2 can hide in the network without being noticed by othernormal nodes. For the cases of more than two malicious
nodes in the network, the harmful influence becomes greater
since the prevention becomes much difficult.
More harmful type of attacks in black hole attack is the grayhole attack [6]. The significant difference between the grayhole attack and black hole attack is that the former one only
does the action in certain time period or to certain data. A
gray hole attack node firstly exploits the route by advertising it
has the shortest path to the destination node, then the node canestablish the route through it. By doing this, it may be able todrop packets from a certain target node in certain time
duration. However, it turns to normal behaviour for othernodes to hide its malicious presence for most of the time. In
some other case, the malicious node may arouse attack tosome certain data from the target node. Therefore, the grayhole attack becomes more difficult to detect than the ordinary
black hole attack.
B. Related Works
In our previous work, each node implements a global agent
acting as a watchdog that detects the packets relaying ofneighbour nodes [7], [8]. The model is called NNOM as isseen in Figure 3. In this figure, node 1 keeps on watchingnode 2, 3, 4 and M. When node M drops packets, theobservation nodes begin to observe its abnormal behaviour.
The node 1 calculates DTV on node M based on the statistical
data of node M’s behaviour by using following Eq. (1):

Di ( j) =

S
S+F

225
225

Fig. 3. Neighbour nodes observation model (NNOM)

smaller compared with F. That is, the DTV decrease sharply
along with the passage of time.
For the case of gray hole attack, however, long terms ofnormal behaviour may help the malicious node to get a higher
DTV. Since the attack nodes in a gray hole attack isintermittently, the malicious node act as normal as possible toearn higher DTV all the way. Irregular malicious behaviour
does not decrease the DTV obviously, thus the node is treatedas normal node.
In order to detect two cooperative malicious nodes, NRTM
[9] is proposed as is shown in Figure 4. When M1 acts
normal, node 1 does not trust it directly. However, it tries to
get other neighbour nodes’ opinion about node M2. Trust
request (TREQ) message and trust reply (TREP) message are
introduced to accomplish this requirement [10]. Firstly, thenode 1 broadcasts TREQ messages to its neighbour nodes 2, 3.
The nodes which receive TREQ reply TREP with Dj(k) of M2
immediately. After a recommendation time to live (RTTL),
the ITV is calculated using the following Eq. (2):

∑ D ( j)D (k)i

I i (k) =jj∈N i , j≠m

N i −1

where:Ii (k):Di (j):
Dj (k):N i:m:

the ITV of node k judged by node i;
the DTV of node j judged by node i;
the DTV of node k judged by node j;
the neighbour nodes’ set of node i;
the suspicious node between node i and node k.

ITV is stored in an indirect trust table (ITT) and comparedwith a predefined threshold Ith. If ITV is lower than Ith, thenode M1 along with node M2 are recognized as a malicious
node. Then, the ID of node M1 and M2 are added to node 1’s
malicious table and restart the route discovery phase.

(1)

where:Di (j):S:F:

the DTV of node j judged by node i;
the number of successful packets relayed by node j;
the number of failed packets relayed by node j.

Once DTV becomes lower than a predefined threshold Dth,the node is treated as a malicious node. It is apparent thatwhen the black hole node takes action all the way, S becomes(2)

Fig. 4. Neighbour recommendation trust model (NRTM).

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Communications
Technology(TACT)
Vol. Vol.
2, Issue
3, May20132013
ICACT Transactions
Transactions on
on Advanced
the Advanced
Communications
Technology(TACT)
2, Issue
3, May226
226

However, some intermediate neighbour nodes may lie to theobservation node and affect the judgment of observation nodeon two-hop neighbour node. For example, node 2 may give ahigh reputation of node M2 compared with otherrecommender, which makes ITV insensitive to the attack
actions of the malicious nodes. Then how to balance the ITVs
from different sources is also a problem needs solving.

III. D-S EVIDENCE THEORYThe Dempster-Shafer evidence theory is not only a theoryof evidence but also that of probable reasoning. It is aframework that can be deployed in diverse areas such as
pattern matching, computer vision, expert systems andinformation retrieval. The D-S evidence theory can handle therandomness and subjective uncertainty together in the trustevaluation. By accumulating evidences, it can narrow down ahypothesis set which provides a powerful method for the
representation and process of the trust uncertainty without thedemand of prior distribution. Moreover, Dempster’s rule ofcombination is the procedure to aggregate and summarize a
corpus of evidence. In the section below, basic concepts in
D-S evidence theory are reviewed and will be used to establish
model in the proposed method in this paper.
Definition 1. Suppose Φ is a finite set of states, and Φ isdefined as a frame of discernment {T, ¬T} as the set of
propositions under consideration where T and ¬T mean thatthe given agent considers a given correspondent to betrustworthy or not to be trustworthy, respectively. The
number of subsets is 2Φ, and 2Φ is defined as {ø, {T}, {¬T},{T, ¬T}}, where ø represent impossible events while {T},{¬T} and {T, ¬T} represent trust value, distrust value, anduncertain events respectively.
Definition 2. The mass value of an element A is defined asm(A), and the value of m: 2Φ->[0, 1]. As is closed-world
assumption, the mass value of null set is defined as m(ø)=0.
The basic probability assignment function is defined using thefollowing Eq.(3):⎧∑ m(A) = 1
⎪ A⊆Φ
(3)
⎨
⎪ m(φ ) = 0⎩
As is in Definition 1, there has the relationship betweenm({T}), m({¬T}) and m({T, ¬T}): m({T})+m({¬T})+m({T,¬T}) = 1.

Bel: 2Φ->[0, 1] is a belief function over Φ as is defined using
the following Eq.(4):Bel(A) = ∑ m(B)

(4)

B⊆A

Pls: 2Φ->[0, 1] is a plausibility function over Φ as is definedusing the following Eq.(5):Fig. 5. Evidence interval illustration⎧ Pls(A) =∑ m(B)⎪B∩A≠φ⎨⎪ Pls(A) = 1 − Bel(A)⎩(5)

As is in Definition 1, there are Bel({T})=m({T}) andPl({T})=m({¬T})+m({T, ¬T}).
The relationship between Bel(A) and Pls(A) can beillustrated as the figure in Figure 5.
Definition 3. Dempster’s combination rule of two evidences:
Suppose Bel1 and Bel2 are belief functions over the same
frame Φ, with basic probability assignments m1 and m2, andfocal elements A1,…,Ai, and B1,…,Bi, respectively. Then thefunction m(C): 2Φ->[0, 1] is defined using the following
Eq.(6):⎧∑ m1 (Ai )m2 (B j )⎪Ai ∩B j =C
⎪ m(C) = m1 (A) ⊕ m2 (B) =(6)
1 − ∑ m1 (Ai )m2 (B j )⎨Ai ∩B j =φ⎪⎪m(φ ) = 0⎩
for all nonempty C, m(C) is a basic probability assignment
which describes the combined evidence.
Definiton 4. Dempster’s combination rule of more than two
evidences: Suppose there are k evidences that are independent
with each other over the same frame Φ, with basic probability
assignments m1, m2, …, mp, and focal elements C1, C2 and Cp,
respectively. Then the function m: 2Φ->[0, 1] is defined using
the following Eq.(7):⎧⎪ m(C) = (m1 (C1 ) ⊕ m2 (C2 ) ⊕...) ⊕ m p (C p )(7)⎨m(φ ) = 0⎪⎩
for all nonempty C, m(C) is a basic probability assignment
which describes the combined evidence.

The trust management strategy proposed in this paper isbased on the D-S evidence theory. Firstly, it gives out aformal definition of the trust value. Then it quantifies the
direct trust value with a basic confidence function. The directtrust value is used to decide whether a neighbour node isbenevolent one. The indirect trust value comes from the

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Technology(TACT)
Vol.
2, Issue
3, MayICACT
Communications
Technology(TACT)
Vol. 2,Issue
3, May20132013

recommendation neighbour node. D-S combination rule isused to combine the indirect trust value together. Then, thecombined trust value is compared with the predefined
threshold to decide whether there is a cooperative black holeattack.

IV. PROPOSED MODELS AND ALGORITHMS
A. Proposed NNOM and DTV
As is in the previous work, it only considers about thecommunication factors such as routing packets as well as data
packets. Moreover, it is supposed that each node only marks
its neighbour nodes with cooperative and uncooperative by
calculating DTV [11]. However, the watchdog mechanism
usually considers about safety data fusion, which is used tomake sure data’s integrity that sent data and received data areexactly the same. In this case, a nodes’ behaviour can beclassified into three categories: normal, suspicious, andmalicious. The proposed method supposes that each node
watches its neighbour node and marks its behaviours as α, βand γ: the number of benevolent, malicious and suspicious
respectively in a certain time period respectively. The targetof the proposed method is to make it more difficult for a node
to get a higher reputation in a long run while easy to lose it.
The proposed method also takes historical evidence into
consideration. It is inspired by the Dempster-Shafer (D-S)
evidence theory, which is an effective method of combining
accumulative evidences or for changing priors in the presenceof new evidences [12]. The proposed DTV algorithm can be
described in following Table 1. This algorithm decides
whether a neighbour node is a malicious node or not.
From a time period [Tn, Tn+1], each node i will count the
recent trust evidence of its neighbour node j. The trust
evidence is refreshed form time Tn to Tn+1 using the following
Eq. (8) to Eq. (10). The refresh weight θ is decided by newly
counted trust evidence using following Eq. (11). In order toprevent the camouflage and deception, lower θ1 is used tolower the effect of the evidence supporting benevolent. In
order to diminish the effect of malicious actions, a high value
is given to θ2. For the gay hole nodes, when it acts normal
behaviour again after malicious actions, the value of θ is set tobe θ3. The value of θ1, θ2 and θ3 is 0 < θ3< θ1< 0.5 < θ2 <1.

α n+1 = (1 − θ )α n + θΔα(8)

β n+1 = (1 − θ ) β n + θΔβ(9)γ n+1 = (1 − θ )γ n + θΔγ

⎧
⎪ θ1,
⎪
θ = ⎨ θ 2,⎪
⎪ θ 3,⎩(10)if

Δα ≥ ΔβifΔα < Δβif

Δα ≥ Δβthen(11)Δα < Δβwhere:
Δα : the number of the normal behaviours in [Tn, Tn+1];
Δβ :the number of the malicious behaviours in [Tn, Tn+1];
Δγ :the number of the suspicious behaviours in [Tn, Tn+1];

227
227

TABLE 1DTV ALGORITHM
DTV Algorithm
Step 1:
Step 2:
Step 3:
Step 4:

Node i watches node j from Tn to Tn+1
(Δα, Δβ, Δγ ) is calculated
Compare Δα and Δβ to decide the value of θ using Eq. (11)Calculate the trust evidence (αn+1, β n+1, γ n+1) at Tn+1 using Eq.
(8)-(10)
Step 5: Calculate the DTV of node j using Eq. (12)-(15)Step 6: if Bi,j -Mi,j >η1 and Si,j < ε1node j is trusted
else if Bi,j -Mi,j <η2 and Si,j < ε1node j is malicious node and put into MT.
else
node j is listed on ST
End.
MT stands for the malicious table. ST represents the suspicious table. The
value of η1 , η2 and ε1 will be studied in the algorithm simulation.

αn :
βn :
γn :
θ:

the trust evidence of normal behaviour at Tn;
the trust evidence of malicious behaviour at Tn;
the trust evidence of suspicious behaviour at Tn;
the refresh weight.

Based on the trust evidence that cares about the historical
data, DTV is calculated using following Eq. (12) to Eq. (15):αnα n + βn + γ nβnM i, j =α n + βn + γ nγnSi, j =
α n + βn + γ n
Di, j = (Bi, j , M i, j , Si, j )Bi, j =

(12)
(13)
(14)
(15)

where:
Bi,j:
the benevolent actions DTV of node j at Tn;
Mi,j: the malicious actions DTV of node j at Tn;
Si,j:
the suspicious actions DTV of node j at Tn.
DTV of node j calculated by node i is represented by Di,j,which consists of three parts: Bi,j, Mi,j, Si,j. Each nodemaintains two tables: Malicious Table (MT) and Suspicious
Table (ST). If a neighbour node is considered to be malicious
node, the observation node records its node ID in MT. If there
is not enough evidence to make sure whether it is normal or
malicious at the very moment, its ID is recorded in the ST of
the observation node. Relative thresholds are described in the
Step 6 of the DTV algorithm. DTV is stored in a Direct
Trust-value Table (DTT) for further use in ITV.
B. Proposed NRTM and ITV
Although there is the proposed DTV, the route through one
neighbour node is never set up directly when it is trusted by
the observe nodes. As is shown in figure 6, node i tries to getother neighbour nodes’ opinion about the next hop of node j,
namely, node k. For this reputation, node i broadcasts TREQ
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACT Transactions
Transactions onon theAdvanced
Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol.Vol.
2, Issue2, Issue
3, May3, May
2013
2013

228
228

TABLE 2ITV ALGORITHM
ITV Algorithm
Step 1:
Step 2:
Step 3:
Step 4:
Setp 5:
Step 6:Do DTV algorithm on node j, if it acts normal go on to step 2
Node i asks nodes p’s DTV on node j
Calculate the evaluation difference using Eq. (16)-(17)Calculate the ITVs of node j using Eq. (18)-(21)Combine different ITVs using Eq. (22)-(25)if bi,k -mi,k > δ1 and si,j < ε2node k is trusted
else if bi,k -mi,k < δ2 and si,j < ε2node k is malicious node and put into MT.
else
node k is listed on ST

End.
MT stands for the malicious table. ST represents the suspicious table. The
value of δ1 , δ2 and ε2 will be studied in the algorithm simulation.
Fig. 6. Neighbour recommendation trust model (NRTM)

messages to get the DTV of node k.
In a predefined recommendation time to live (RTTL), theneighbour nodes such as nodes P1, P2 and P3 send back TREP
messages including DTVs of node k. Then, DTV ofneighbour nodes and DTV of node k are used together tocalculate ITV. However, some of the neighbour nodes may
deceive node i by giving a high reputation of node k.
Therefore, the proposed method tries to give each neighbour
node an evaluation difference to balance the neighbour node’s
cheating enhancements on the value of ITV. The proposed
ITV calculation algorithm is described in following Table 2
whether there is cooperative black hole attack or not.
The evaluation difference based on the data distance [13],which means each two data’s difference, is calculated byfollowing Eq. (16) to Eq. (17). The evaluation difference ofeach part in DTV is represented as dBp, dMp while the range is[0,1] and smaller difference makes them closer to 0.

d BP =d =P
M

where:
d Bp :
d Mp :Ni :∑Bi,q Bq,k − Bi, p Bp,kp,q∈N i , p≠ j,q≠ p

Ni − 2∑

M i,q M q,k − M i, p M p,kp,q∈N i , p≠ j,q≠ p

Ni − 2(16)(17)

the evaluation difference of B in DTV;
the evaluation difference of M in DTV;
the neighbour nodes set of node i.

Based on the evaluation difference, each neighbour node
can be given a different trust weight to calculate the ITV.
Take dB for example, if a given Bi,pBp,k is more different fromother Bi,qBq,k, the dB of this recommendation reputation islarger than other ones. Then, this recommendation reputation
may be given by a deceiving nodes and a low trust weight isused to decrease its impact on total ITV as is shown in thefollowing Eq. (18) to Eq. (21):bi,kp = Bp,k (1 −d Bp
Max Bi,q Bq,k − Bi, p Bp,kp
mi,k= M p,k (1 −

(18)

)

d Mp
Max M i,q M q,k − M i, p M p,k

)

(19)

p
= 1 − b − mi,k

(20)

p
p
p
p
I i,k
= (bi,k, mi,k, si,k
)

(21)

p
i,k

s

p
i,k

where:
bi,kp : the benevolent actions ITV of node k through node p
at present Tn;
mi,kp : the malicious actions ITV of node k through node p
at present Tn;
si,kp : the suspicious actions ITV of node k through node p
at present Tn;
Ii,kp : the ITV of node k through node p at present Tn.
According to the Dempster’s combination rule of more than
two evidences, the ITVs are combined together to calculate a
combined ITV and the ITV is calculated using the following
Eq.(22) to Eq.(25):123pbi,k = bi,k⊕ bi,k⊕ bi,k⊕ ⋅⋅⋅ ⊕ bi,k

(22)

mi,k = m ⊕ m ⊕ m ⊕ ⋅⋅⋅ ⊕ m(23)

si,k = s ⊕ s ⊕ s ⊕ ⋅⋅⋅ ⊕ s

(24)

1
i,k

1
i,k

2
i,k

2
i,k

3
i,k

3
i,k

p
i,k

p
i,k

(25)
I i,k = (bi,k , mi,k , si,k )where:
bi,k:
the benevolent actions ITV of node k at present Tn;
mi,k: the malicious actions ITV of node k at present Tn;si,k:the suspicious actions ITV of node k at present Tn;
Ii,k:the ITV of node k at present Tn.
ITV of node k calculated by node i is represented by Ii,k,which consists of three parts: bi,k, mi,k, si,k. The value of ITV isstored in an Indirect Trust-value Table (ITT). According to
the Step 5 in the ITV algorithm, if node k is trusted, the source

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Technology(TACT)
Vol.
2, Issue
3, MayICACT
Communications
Technology(TACT)
Vol. 2,Issue
3, May20132013

229

Fig. 7. Transition of DTV on black hole nodes.

Fig. 8. Transition of DTV on gray hole nodes.

node establishes the route though node j and node k. If not,node k is stored in the MT as a malicious node.

2) If a malicious recommendation node is trying to deceive,it scores a low reputation to a normal node same as whatis define as a black hole node or gray hole node, while ahigh reputation to a malicious node.

V. NUMERICAL EVALUATION
Performance evaluation is firstly conducted to study the
proposed DTV and ITV from the aspect of numerical analysis.
Matlab is used as the analyser tool. For DTV algorithm, α0=1,
β0=1 and γ0=1 are set at initial phase. The historical evidence
is given a high weight by assign a low value to θ1=0.4, a highvalue to θ2=0.9 to punish the nodes taking malicious action,
and a punish value to θ3=0.1. In each time period [Tn, Tn+1], a
set of evaluation evidences is given for three kinds of nodes:
normal nodes, black hole nodes, gray hole nodes.
1) For each normal node, a random function is exploited togenerate a random number B with the probability between[0.6, 0.7] to represent its normal action rate. A random
number M between [0.2, 0.3] is generated to represent its
malicious action rate while the last number S between [0,0.2] is left for the uncertain action rate. The relation
between B, M and S is S=1-B-M.
2) For each back hole node, a random function is exploitedto generate a random number B between [0.2, 0.3] torepresent its normal action rate. A random number M
between [0.7, 0.8] is generated to represent its malicious
action rate while the last number S between [0, 0.2] is left
for the uncertain action rate. The relation between B, M
and S is also S=1-B-M.
3) For each gray hole node, firstly it acts as a normal node
and all the action rates are generated as in 1), while it acts
as a black hole node for a certain time and also follow theaction rates in 2), then it acts as a normal node again.
For the ITV algorithm, it is supposed that each
recommendation node is given a high reputation because they
are all supposed to have been trusted. However, theirreputations are different values generated randomly.
1) For normal recommendation node, it will observe thetwo-hop node based on its own observation. The
reputation of the node under observation is given as
defined in the front according to its behaviours.

A. DTV Performance Evaluation
Figure 7 shows the performance of DTV with the blackhole node. The black hole node acts as a malicious node fromthe 11st round until the last 50th round. As is described
previously, the DTV of a neighbour node j calculated by node
i consists of three parts: Bi,j, Mi,j and Si,j. It is apparent that Bi,jdecreases sharply from 0.6 and is stable at about 0.2,
meanwhile, Mi,j increases suddenly from 0.3 and finally rests
at around 0.7. Bi,j-Mi,j also drops from minus 0.4 to aroundminus 0.4. Based on the explanation of Figure 7, some key
parameters in the decision part of the DTV algorithm can be
set as: η1 and η2 is around minus 0.5 and minus 0.2, while ɛ1 isaround 0.3. In this case, the black hole node is very easy to
be filtered out.
Figure 8 shows the performance of DTV with the gray holenode. As is described in the simulation environment, the grayhole node firstly acts as a normal node in the first 10 rounds.
The malicious node takes action from 10th round to 12thround. After that, the gray hole node acts as normal as
possible. It is apparent that Bi,j decreases sharply while Mi,jincreases immediately. Meanwhile, Bi,j-Mi,j also drops from
0.4 to minus 0.5 at the same time. However, Bi,j-Mi,j alongwith Bi,j increase at a very low speed compared with theirdecrease of the value. It is easy to find that the proposed
method makes it difficult for the gray hole node to get a highreputation on DTV. The mechanism is so sensitive that DTVchanges dramatically whenever the node drops packets.
However, it is difficult to restore its former reputation in ashort time period. Based on the previous explanation of
Figure 8, some key parameters in the decision part of the DTV
algorithm can be appropriate as follows: η1 to be around 0.55,
η2 to be around minus 0.3, and ɛ1 to be around 0.15. In thiscase, the gray hole node is very easy to be filtered out.
Based on the numerical analysis of both black hole attackand gray hole attack, the key parameters in the decision part of
the DTV algorithm are set to be: η1=0.55, η2=-0.25 and ɛ1=0.2.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Communications
Technology(TACT)
Vol. 2,Issue
3, May20132013
ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Technology(TACT)
Vol.
2, Issue
3, May230
230

Fig. 9. Transition of ITV on black hole node with 2 benevolent
recommenders and 1 deceiving recommender.

Fig. 10. Transition of ITV on gray hole node with 2 benevolent
recommenders and 1 deceiving recommender.

Fig. 11. Transition of ITV on black hole node with 3 benevolent
recommenders and 1 deceiving recommender.

Fig. 12. Transition of ITV on gray hole node with 3 benevolent
recommenders and 1 deceiving recommender.

B. ITV Performance Evaluation
Figure 9 shows the performances of ITV influenced by thedeceiving nodes under black hole node. For the proposed ITV,
it is set that there are totally 3 nodes acting as therecommendation nodes, among which 1 node is cheating. The
two-hop node takes black hole action from round 11 to the end.
As is described previously, the ITV of a two-hop neighbour
node k calculated by node i consists of three parts: bi,k, mi,k andsi,k. bi,k is decreasing from the level of 0.9 to around 0.2. mi,kincreases from 0.05 but always maintains about 0.9. si,kchanges sharply from 0.05 to 0.2, approximately. After the
proposed ITV is implemented, although the deceiving node
keep on cheating, the values of bi,k, mi,k, and si,k change
slowing.
The enhancement of the false recommended
reputation is minimized. Based on the Figure 9, some key
parameters are set to be: δ1 about 0.9, δ2 around minus 0.5 andɛ2 is around 0.3.
Figure 10 shows the performances of ITV influenced by thedeceiving nodes under gray hole node. For the proposed ITV,
it is set that there are totally 4 nodes acting as therecommendation nodes and 1 node is cheating among them.
From the result, bi,k first changes in a low speed while still no

more than 0.8, then it drops sharply when the two-hop takes
malicious action. mi,k increases sharply from 0.05 while
always maintains more than 0.9. si,k changes slightly from0.05 to 0.2, approximately. Under the proposed ITV, although
the deceiving node keeps on cheating, the values of bi,k, mi,k,and si,k change slowly. The gray hole node acts as a normal
node in the first 10 rounds. The malicious node takes action
from 10th round to 12th round. After that, the gray hole nodeacts as normal as possible. It is apparent that bi,j increases
sharply to nearly 0.9, while mi,j decreases immediately tonearly 0.05. Meanwhile, bi,j-mi,j also drops from 0.9 to minus
0.7 at the same time. Thus, the enhancement of the falsely
recommended ITV is minimized. Based on the analysis of
Figure 10, some key parameters are set to be: δ1 is around 0.8,
δ2 is around minus 0.5 and ɛ2 is around 0.1.
Figure 11 and figure 12 are the performances of ITV
influenced by deceiving nodes under black hole and gray hole,respectively. Same as what is analysed in figure 9 and figure
10, it is easy to find key parameters to be set. In Figure 11,
key parameters are set to be: δ1 is around 0.9, δ2 is aroundminus 0.5 and ɛ2 is around 0.25. In Figure 12, δ1 is around 0.9,
δ2 is around minus 0.5 and ɛ2 is around 0.25.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACTTransactions
Transactionson
onAdvanced
the Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol. 2,Vol.
Issue
2, Issue
3, May3, May20132013

231
231

Fig. 13. Packet delivery ratio with single-attack malicious nodes.

Fig. 14. Packet delivery ratio with single-attack malicious nodes.

Based on the numerical analysis of both black hole attackand gray hole attack, some key parameters in the decision partof the ITV algorithm are set to be: δ1=0.7, δ2=-0.3 andɛ2=0.35.

more, the black hole case, packet delivery rate decreases more
sharply than that in the gray hole case, for the reason thatblack hole node takes action persistently while gray hole nodetakes action selectively. Figure 14 shows the detection ratio of
P-SAODV and N-SAODV with single-attack black hole nodeor gray hole node. The detection ratio firstly increases with
the increase of the maximum speed of each node, but then it
decrease, which is caused by the increase of the packets
dropping between nodes. Form these two aspects, it is easy tofind that new method shows better performance comparedwith previous one.

VI. NETWORK SIMULATION EVALUATIONAs a second evaluation, the proposed mechanism isimplemented in AODV using simulator GloMoSim2.03 to
study the packet delivery ratio and detection rate. The
parameters are set as in Table 3.
A. Single Attack Performance Evaluation
Figure 13 shows the packet delivery ratio withsingle-attack black hole node or gray hole node. P-SAODV
stands for the AODV protocol with the security mechanism inthe previous work. N-SAODV stands for the newly proposed
method in this paper. The packet delivery ratio decreases with
the increase of the number of malicious nodes in the network.
Newly proposed SAOVE detection mechanism will increase
the packet delivery ratio more than that of the previous work,in both cases of black hole node and gray hole node. What’s
TABLE 3SIMULATION PARAMETERS
Parameters

Setting

Simulator
Routing protocol
Mac protocol
Simulation area
Node placement
Number of nodes
Transmission range
Maximum speed
Traffic type
Packet rate
Data payload
Pause time
Simulation time
Mobility model

GloMoSim2.03
P-SAODV／N-SAODVIEEE 802.11
1000m×1000 m
Random
50
180m
10m/s
CBR (UDP)
2 packets/s
512bytes/packets
10s
1000s
Random waypoint

B. Cooperative Attack Performance Evaluation
Figure 15 shows the packet delivery rate with cooperative
attack malicious nodes. The packet delivery ratio decreases
with the increase of the number of malicious nodes in the
network. Newly proposed SAOVE detection mechanism will
increase the packet delivery ratio more than that of theprevious work, in both cases of black hole node and gray holenode. Figure 16 shows the detection ratio of P-SAODV and
N-SAODV with cooperative-attack black hole node or grayhole node. The detection ratio firstly increases slightly with
the increase of the maximum speed of each node, but then it
decrease. The strategy can also reduce the impact from thedeceiving neighbour nodes and take advantages of therecommended reputation to make the detection determination
more rationally. Form the two aspects, it is easy to find thatnew method shows better performance compared with theprevious one. However, compared with the single-attack, thecooperative-attack is harder to detect even with the new
method.
VII. CONCLUSIONSIn this paper, the problem of black hole attack and gray holeattack are discussed and two algorithms, NNOM-based DTV
and NRTM-based ITV are proposed. The proposed DTV canbe used to detect the gray hole attacks in the networks. The
proposed ITV aims at the recommendation of cheatingneighbour nodes. If there is no such recommendation node or

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
Advanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May
2013

232
232

Fig. 15. Packet delivery ratio with cooperative-attack malicious nodes.

Fig. 16. Detection ratio with cooperative-attack malicious nodes.

the cheating nodes are too many, the proposed ITV may nottake effects. For the future study, it may find another bettermethod instead of the evaluation difference method.
Furthermore, we would like to apply this trust management
strategy into wireless sensor network (WSN) where the
network structure is similar to MANET. Some other problems
such as energy should also be taken into consideration.

Bo Yang received his B. E. degree in computer scienceand technology from Xi Dian University, Xi’an, China,
in 2009. He received his second B.E. degree in
economics from Xi’an Jiaotong University, China,
2012. Currently, he is working toward the M.E. degreein the Global Information and Telecommunication
Studies, Waseda University, Tokyo, Japan. He won the
ICACT best paper award in Feb. 2012. His present
research emphasizes on the study of security problems
in the wireless networks, such as ad hoc networks, MANETs, WSNs etc.

REFERENCES[1]

B. Yang, R. Yamamoto, and Y. Tanaka, “A Trust-aware management
strategy against black hole attacks in MANET,” IEICE Commun.
Society Conf., no. BS-6-39, pp S-106-S-107, Sept. 2011.
[2] S. Ganeriwal, L. K. Balzano, M. B. Srivastava, “Reputation-based
framework for high integrity sensor networks,” ACM Trans. Sensor
Netw., vol. 4, pp. 1-37, June 2008.
[3] A. P. Dempster, “Upper and lower probabilities induced by amultivalued mapping,” The Annals of Mathematical Statistics, vol. 38,
no.2, pp.325-339, April 1967.
[4] A. L. Jousselme, D. Grenier, E. Bosse, “A new distance between twobodies of evidence,” Information Fusion Journal by Elsevier, vol. 2,
pp.91-101, June 2001.
[5] D. Djenouri, L. Khelladi, A. N. Badache, “A survey of security issues inmobile ad hoc and sensor networks,” IEEE Commun. Surveys andTutorials, vol. 7, pp. 2-28, fourth quarter 2005.
[6] A. D. Wood, J. A. Stankovic, “Denial of service in sensor networks,”
Computer Society by IEEE , vol. 35, pp. 54-62, Oct. 2002.
[7] S. D. Roy, S. A. Singh, S. Choudhury, “Countering sinkhole and blackhole attacks on sensor networks using dynamic trust management,”
Comput. and Commun. by Elsevier, pp. 537-542, July 2008.
[8] T. Zahariadis, P. Trakadas, S. Maniatis, “Efficient detection of routing
attacks in Wireless Sensor Networks,” Systems, Signals and Image
Processing IWSSIP, pp. 1-4, June 2009.
[9] H. Chen, “Task-based trust management for wireless sensor network,”
Int. J. of Security and Its Applications SERSC, vol. 3, No. 2, April 2009.
[10] P. B. Velloso, R. P. Laufer, D. de O Cunha, O. C. M. B. Duarte, G.
Pujolle, “Trust management in mobile ad hoc networks using a scalable
maturity-based model,” IEEE Trans. on Netw. and Service Management,
vol. 7, pp. 172-185, Sept. 2010.
[11] B. Y. Yan, F. Y. Liu, M. J. Deng, J. L. Zhou, W. Lu. “Trust model basedon risk evaluation in wireless sensor networks,” J. of Central South
University, vol. 42, no. 6, pp. 1657-1662. June 2011.

Ryo Yamamoto received his B.E. and M.E. degree inelectronic information systems from Shibaura Instituteof Technology, Tokyo, Japan, in 2007 and 2009. He ispresently a research associate of Global Information
and Telecommunication Institute, Waseda University.
He received the IEICE young researcher's award in2010. His current research interests are mobile ad hoc
networks and cross-layered protocols.
Yoshiaki Tanaka received the B.E., M.E., and D.E.
degrees in electrical engineering from the University ofTokyo, Tokyo, Japan, in 1974, 1976, and 1979,
respectively. He became a staff at Department ofElectrical Engineering, the University of Tokyo, in
1979, and has been engaged in teaching andresearching in the fields of telecommunication
networks, switching systems, and network security. He
was a guest professor at Department of Communication
Systems, Lund Institute of Technology, Sweden, from 1986 to 1987. He wasalso a visiting researcher at Institute for Posts and Telecommunications
Policy, from 1988 to 1991, and at Institute for Monetary and Economic
Studies, Bank of Japan, from 1994 to 1996. He is presently a professor at
Global Information and Telecommunication Institute, Waseda University, anda visiting professor at National Institute of Informatics. He received the IEEE
Outstanding Student Award in 1977, the Niwa Memorial Prize in 1980, theIEICE Achievement Award in 1980, the Okawa Publication Prize in 1994, theTAF Telecom System Technology Award in 1995 and in 2006, the IEICE
Information Network Research Award in 1996, in 2001, in 2004, and in 2006,
the IEICE Communications Society Activity Testimonial in 1997 and in 1998,
the IEICE Switching System Research Award in 2001, the IEICE Best Paper
Award in 2005, the IEICE Network System Research Award in 2006, in 2008,
and in 2011, the IEICE Communications Society Activity Award in 2008, theCommendation by Minister for Internal Affairs and Communications in 2009,
and the APNOMS Best Paper Award in 2009. He is a Fellow of IEICE.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionsononthe
Advanced
Communications
Technology(TACT)
Vol.
2, 2,
Issue
3, 3,
May
2013
ICACT
Advanced
Communications
Technology(TACT)
Vol.
Issue
May
2013

233
233

A Global Mobility Scheme for Seamless
Multicasting in Proxy Mobile IPv6 Networks
Hwan-gi Kim*, Jong-min Kim*, Hwa-sung Kim*
* Dept. of Electronics and Communications Engineering, Kwangwoon University, Seoul, Korea
Hwangi9999@gmail.com, sazemic@kw.ac.kr, hwkim@kw.ac.kr

Abstract— Recently, Proxy Mobile IPv6 (PMIPv6) has beendrawing attention as a mobility management protocol toeffectively use the limited wireless resources. And, there have beensome researches to apply PMIPv6 to multicasting, which is coretechnology of Internet broadcast system. However, PMIPv6 based
multicasting cannot support the global mobility directly between
different PMIPv6 domains because PMIPv6 is basically designed
for local mobility in single PMIPv6 domain. Moreover, PMIPv6
based multicasting causes the disconnection of services because it
does not solve problems of packet loss during binding and groupjoining procedure. In this paper, we propose a global mobility
scheme that supports the seamless multicasting service in PMIPv6
networks. The proposed scheme supports the global mobility due
to the addition of extra signalling messages between LMAs. Also,
it achieves low latency because it performs fast binding and groupjoining procedure. We present the simulation results which showthat the proposed scheme achieves the global mobility with low
latency through the NS-2 simulation.
Keywords— Proxy Mobile IPv6, Multicast Mobility, Global
Mobility, Seamless Service, Packet loss

I. INTRODUCTION

R

ecently, communication business is rapidly changed fromwired network market to wireless mobile network market.
Especially, mobile network environment is gaining attention
nowadays, as the portable device such as smart phones and
PDAs have been popular recently. In mobile networks, VOD
and VoIP have been the main services. But recently, mobile
broadcast services like a mobile IPTV are gaining attention in
mobile networks.
Mobile IPTV is an IP-based service unlike existing mobile
services such as DMB, and it is suitable service to All-IP next
generation networks. Mobile IPTV service has weakness in thatit has service disconnection problem due to the IP addresschange while the mobile terminal moves. In order to solve thisManuscript received June 30, 2012. This work was supported in part by the
Kwangwoon University Industry-Academic Collaboration Foundation, 2012.
Hwan-gi Kim is with the Dept. of Electronic and communications
Engineering,
Kwangwoon
University,
Seoul,
Korea(e-mail:hwangi9999@gmail.com)
Jong-min Kim was with the Dept. of Electronic and communications
Engineering, Kwangwoon University, Seoul, Korea (sazemic@kw.ac.kr)
Hwa-sung Kim is a professor at Dept. of Electronics and communications
Engineering, Kwangwoon University, Seoul, Korea (Tel: +82-02-940-5442;
e-mail: hwkim@kw.ac.kr).

problem, IETF(Internet Engineering Task Force) proposed
Mobile IPv6(MIPv6)[1] to support mobility regardless of IP
address change.
MIPv6 is a mobile-controlled mobility management protocolin that mobile node recognizes the handoff by itself and it
performs the handoff procedure. But MIPv6 needs to be
implemented in the mobile node and it needs many signalling inorder to provide the mobility of the mobile devices. These
problems make limited link resource overloaded. In order tosolve these problems, IETF NETLMM (Network-based
Localized Mobility Management) WG (Working Group)
proposed Proxy Mobile IPv6 (PMIPv6)[2].
Basically, PMIPv6 is a network-controlled mobility
management protocol. PMIPv6 provides mobility to Mobile
Node(MN) using two kinds of routers: Mobile Access
Gateway(MAG) and Local Mobility Anchor(LMA). MAG
performs signalling procedure for MN’s movement. LMA
performs agent role of MNs. When MN moves, MAGrecognizes that MN is moving, and MAG decides if means
handover or not. If it is handover, MAG performs handoverprocedure with LMA. This handover procedure could reducemany signals between MAG and MN. But if PMIPv6 domain
gets too wider, routing overhead gets too bigger. So, PMIPv6
assumes that the movements occur only in a PMIPv6 domain[2].
This problem is a critical weakness when adopting the multicast
to PMIPv6 networks in order to provide next generation
multimedia service. Therefore PMIPv6 needs to provide global
mobility as well as local mobility without binding delay andpacket loss due to the mobility. In this paper, we propose aseamless multicast scheme which supports global mobility in
PMIPv6 networks.
The remainder of the paper is organized as follows. In
section 2, as the related works, we will explain about the
PMIPv6 and PMIPv6 based global multicast schemes. In
section 3, we will explain about the proposed global mobility
scheme for seamless multicast service in PMIPv6 networks.
Next, in section 4, Simulation results of the proposed scheme
will be explained. And then, in section 5, we will conclude thispaper.
II. RELATED WORKS
A. Proxy Mobile IPv6
PMIPv6 was designed to provide the network-based IP
mobility to MN in a topologically localized domain, without

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
Vol.
Issue
May
2013
ICACTTransactions
Transactionsononthe
Advanced
Communications
Technology(TACT)
Vol.
2, 2,
Issue
3, 3,
May
2013

requiring the MN to participate in any IP mobility related
signalling. Existing host based mobility protocols have someweaknesses in that it needs many signalling between MN andnetwork in order to provide the mobility of the MN, and thismakes limited link resource overloaded. In order to remedy the
weaknesses of MIPv6, PMIPv6 makes the network mainly
perform the signalling that is needed to provide the mobility
instead of MN. So, MN can be provided the mobility without
implementation of the complicated signalling function.
The core functional components used to support mobility in
PMIPv6 are the Policy Store (PS), Local Mobility Anchor(LMA), and Mobile Access Gateway (MAG). Figure 1 shows
the PMIPv6 network. PS is the entity that manages an MN’sauthentication and maintains the MN’s profile which is a set ofparameters configured for a given MN. Meanwhile, MAG
performs the role of typical access router that detects the
movement of MN and performs the MN's location update bysending mobility related signals to the MN’s LMA on behalf ofthe MN. Also, the MAG ensures that an MN can obtain an
address from its Home Network Prefix (HNP) and receive its
HNP anywhere within the PMIPv6 domain. As a result, the MN
believes it is using the same link obtained with its initial addressconfiguration, even after changing its point of attachment within
the network. On the other hand, LMA is similar to HA (HomeAgent) in MIPv6, however, it has additional capabilities
required to support PMIPv6. The main role of the LMA is tomaintain reachability to the MN’s address while the MN moves
around within the PMIPv6 domain. The LMA includes a
Binding Cache Entry (BCE) for each currently registered MN.

234
234

IETF NETLMM WG is standardizing the interaction method
between PMIPv6 and MIPv6 to support the global mobility [3].
In this method, a HA is designated in the PMIPv6 domain where
MN enters for the first time. Thereafter, LMA in the domain,
where MN moves, sends the binding request massage to MN’s
HA. Figure 2 shows the detail interaction method between
PMIPv6 and MIPv6.
At first, MN is connected to MAG1. MAG1 that recognizes
the connection of MN by link layer signal, sends PBU(Proxy
Binding Update) massage to LMA1 for Binding Update. ThenLMA1 completes Binding Update by registering MN’s address.
This procedure is sufficient for Binding Update in existing
PMIPv6 protocol. In the case of PMIP-MIP interaction for
global mobility, however, LMA1 has to perform binding
Update for MIPv6 with HA. After Binding Update between HA
and LMA1, LMA1 need to send PBA massage to MN throughMAG1 in order to complete Binding Upadate procedure.
Thereafter, MN can be serviced.

Figure 2. PMIP-MIP Interaction

Figure 1. PMIPv6 network

B. Supporting Global Mobility in PMIPv6
As mentioned in introduction, PMIPv6 was designed toprovide the network-based IP mobility to MN in a topologically
localized domain[2]. However, MN can move out to otherdomain frequently in real situation. Therefore, the research for
global mobility in PMIPv6 is required because there is no
protocol definition to support the global mobility. IETF
NETLMM WG has researched this problem.
C. Interaction between PMIPv6 and MIPv6

If MN moves to MAG2, Binding Update is not required
because it is not global mobility. But MN’s
COA(Care-of-Address) has to be changed to MAG2’s address.
Therefore MAG2 performs Binding Update by sending PBU
message to LMA1. Then LMA1 requests Binding Update to HA
for COA change.
If MN moves to MAG3, LMA2 requests Binding Update to
HA because PMIPv6 domain is changed. HA, which received
Binding Update, recognizes that the path to MN was changed.
Also it recognizes that COA was changed to MAG3’s address.
HA redirects the packets destined to MN to new domain.
Therefore, MN can be serviced even in case of global mobility
without performing any signalling according to MN’s
movement.
D. Definition of new agent
PMIP-MIP interaction is a global mobility method proposed
by IETF NETLMM WG, in which PMIPv6 is combined with
MIPv6 signalling.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
Vol.
Issue
May
2013
ICACTTransactions
Transactionson
onthe
Advanced
Communications
Technology(TACT)
Vol.
2, 2,
Issue
3, 3,
May
2013

This method has a problem in that unnecessary delay is
occurred by additional signals between LMA and HA eventhough the advantage of PMIPv6 protocol is maintained
because MN does not participate to the binding updateprocedure. Therefore new method called I-PMIP that add the
new agent function to router, was proposed for solving
unnecessary delay problem[4]. I-PMIP provides the global
mobility by giving the role of HA to the first LMA connected forthe first time instead of using the separate HA[4].

235
235

HA. This unnecessary delay causes the problem when
PMIP-MIP Interaction is applied to multicasting. When a MN
receiving multicast service, is connected to LMA of new
PMIPv6 domain but LMA is not a member of multicast group,group joining procedure is occurred. In this case, new LMA
requests the binding to HA, and group joining procedure is
performed in turn. This leads to the delay of group joining andservice disconnection because MN is not able to receive any
packet before group joining procedure is completed.
In contrast to PMIP-MIP interaction, I-PMIP does not incur
unnecessary delay because it does not require signalling with
HA. But, I-PMIP causes the delay if global mobility occurs
frequently because the continued bindings to SMA are required.
Also, it does not solve the service disconnection problem
caused by group joining operation after the binding, because it
still needs the binding for global mobility, it reduces theunnecessary signalling with HA by using SMA though.
In this paper, we propose an effective global mobility scheme,
which performs the fast binding and group joining procedure,
without binding to HA or new agent. Also, the proposed scheme
does not incur any packet loss.

III. PROPOSED SCHEME FOR GLOBAL MOBILITY AND SEAMLESSMULTICAST IN PMIP

Figure 3. I-PMIP operation

Figure 3 shows the I-PMIP operation. When MN isconnected to first LMA, MN receive the service according to
PMIPv6 protocol. As shown in figure 3, the location of MN isregistered using PMIPv6 signalling messages when a MN isconnected to MAG1. Also, the mobility is supported using
PMIPv6 signalling messages when MN moves to MAG2. On
the other hand, LMA2 requests the binding of MN to LMA1,
when MN moves continuously and finally connects to MAG3.
This is the core aspect of I-PMIP operation. The first LMA, towhich MN is connected for the first time, is called SMA(Session Mobility Anchor) and it provides seamless session to
MN even in the case of global mobility..
The first LMA, to which MN is connected for the first time,
carries the role of SMA that is similar to HA of MIPv6.
Therefore, all packets destined to MN from outside passthrough SMA. And, LMA of new PMIPv6 domain requests thebinding to this SMA when MN moves away from PMIPv6
domain where SMA resides. After the binding, thebi-directional tunnel is established between SMA and new LMA.
Thereafter, all packets destined to MN are transmitted to new
LMA through bi-directional tunnel. If MN moves continuously
and is connected to LMA of another PMIPv6 domain,
bi-directional tunnel is established again.
E. Problems of PMIP-MIP Interaction and I-PMIP
PMIP-MIP Interaction adds the MIPv6 signalling to PMIPv6
signals to provide the global mobility. But adding MIPv6 signal
occur unnecessary delay because of binding between LMA and

A. LMA option massage for global mobility
In this section, we explain newly added massage forsupporting global mobility without HA and new agent. Using
this added massage, each LMA identifies each other without HAand new agent.

Figure 4. LMA option massage format

A router sends RA(Router Advertisement) massage to allother routers for sharing its information. In the newly added
massage shown in Figure 4, a LMA’s global address option isadded. Using this option, LMA configures the LMA entries ofother LMA’s global addresses in the same manner as their own
MAG entry. LMA’s global addresses are selectively stored as
LMA entry by configuring based on Hop Count according to the
distance field in the massage format because the distant LMA’s
global addresses does not need to be stored. Using this massage,
LMAs are able to share each address and inform MN’s
movement.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionsononthe
Advanced
Communications
Technology(TACT)
Vol.
2, 2,
Issue
3, 3,
May
2013
ICACT
Advanced
Communications
Technology(TACT)
Vol.
Issue
May
2013

B. global mobility for Seamless multicast service

236
236

receives multicast data from source. And n-LMA also buffereddata from p-LMA sending.
When MN handovers to n-MAG, n-MAG accesses to PS
server and request MN’s information. After that, n-MAG sends
PBU massage to n-LMA for MN’s binding update. At this time,n-MAG sends MLD(Multicast Listener Discovery) reportmassage[5](n-MAG could know MN’s multicast information
through PS server) to n-LMA at same time. After receiving
PBU and MLD massages, n-LMA could know MN is joined its
own domain then send PBA massage to n-MAG. And n-LMA
starts two channel configuration procedure. One of them is
Bi-Directional Tunnel for existed PMIPv6 protocol, anothertunnel is Multicast Packet Tunnel as we defined.
After channel configuration procedure, n-LMA sends
buffered data to MN through n-MAG. Finally, MN has been
moved global mobility and received seamless multicast service.

IV. SIMULATION
Figure 5. Seamless multicast for global mobility

Figure 5 shows the sequence diagram of global mobility for
seamless multicast service. MN first joins to MAG, then its
LMA(Previous LMA, p-LMA) provides multicast service to
MN via MAG. It assumes LMAs have LMA entry by using new
option massage in RA.
When MN moves away from p-MAG to other domain, it
performs Figure 5 procedure.
MN recognizes decreasing signal strength from current AP.
MN informs handover to its p-MAG via AP. Then p-MAG
sends HI(Handover Initiate) massage to p-LMA about MN’s
handover. This This HI massage includes n-MAG’s address as
MN’s new MAG. A p-LMA checks its own MAG entry whether
n-MAG’s address is included same domain. If n-MAG’s
address is not same domain, p-LMA pretends MN’s global
mobility. Then p-LMA sends NS(Neighbor Solicitation)
massage to LMAs that it is included a list of LMA entry. And
LMAs that received NS from p-LMA, check each own LMA
entries. If one of LMAs discovers n-MAG’s address from its
own MAG entry, it sends NA(Neighbor Advertisement)
massage to p-LMA. Then p-LMA knows MN moves which
LMA. And p-LMA sends HI massage to n-LMA for fasthandover procedure. In this HI massage, includes MN’s ID andmulticast information. After receiving HI massage from p-LMA,
n-LMA sends HACK(Handover Acknowledgement) andpretends MN’s handover.
On the other hand, p-LMA starts buffering multicast data thatwill forward to MN, when receive HI massage from p-MAG.
It is for seamless multicast service without data loss. P-LMA
continues buffering until n-LMA send Hack massage to p-LMA.
If p-LMA receives Hack massage, p-LMA sends buffered data
to n-LMA.
During buffered data is transmitted to n-LMA, n-LMA
updates newly configured MN’s information to PS(Policy
Store) server. After updating, n-LMA informs that updateprocedure is completed to p-LMA by HI massage. After that,
n-LMA performs group rejoining procedure. Then n-LMA

In order to evaluate the performance of proposed handoverscheme, we use the NS-2 network simulator[6]. Figure 6 shows
the topology used for the simulation. In Figure 6, MN starts
moving from AP1 and moves to AP4 with constant velocity,
60m/s. And, MN is connected to MAG through layer 2
handover at intervals of about 60 sec. The parameters used forthe simulation are shown in Table 1.

Figure 6. Simulation topology for multicast handover
Table 1. Simulation Parameter

Parameter

Value

Simulation time

300 sec

Distance between MAGs

5km

Link bandwidth

2Mbps

Velocity of MN

60m/s

Bit rate

CBR

Packet size

1KbyteMulticast routing protocol

PIM-SM

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionson
onthe
Advanced
Communications
Technology(TACT)
Vol.
2, Issue
3, May2013
ICACT
Advanced
Communications
Technology(TACT)
Vol.
2, Issue
3, May2013

In order to evaluate that the proposed scheme can provide theseamless multicast service when the global mobility occurs, wemeasured the UDP Datagram ID, TCP Sequence Number, UDP
and TCP Throughput during MN moves from MAG2 to MAG3.
Figure 7 shows the simulation results on UDP datagram ID
that MN receives when source node sends the UDP traffic. As
shown in Figure 7, PMIP-MIP interaction-based multicast
cannot receive the UDP datagram from 120.2 second to 122.5
second. This data loss occurs because of the global binding
delay among LMA2, LMA1 and HA after the layer 2 handover
between APs and multicast group joining procedure delay. In
case of I-PMIP-based multicast, packet loss occurs from 120.2
second to 122.1 second that is shorter than PMIP-MIP
interaction-based multicast. This result is caused by the fasterbinding procedure of I-PMIP because binding is completed at
LMA1 unlike PMIP-MIP Interaction. But, as shown in Figure 7,
packet loss still occurs from datagram ID 62231 to 62250 incase of I-PMIP.
On the other hand, the proposed scheme can receive the UDP
datagram during disconnection period from 120 second to 120.7
second by using buffering. That is, proposed scheme does notexperience the packet loss thus provides the seamless service
because the datagrams that are generated while MN is moving issaved and retransmitted.
This result affects the UDP throughput as shown in figure 8.
The UDP throughput in PMIP-MIP Interaction scheme drops tozero for about 2000ms because of the effect of the global
handover, which incurs the binding procedure with HA andgroup joining procedure after binding. Also, the UDP
throughput in I-PMIP-MIP scheme drops to zero for about
1600ms because of the effect of the global handover, which
incurs the binding procedure with SMA and group joining
procedure after binding. However, the UDP throughput in the
proposed scheme drops just for about 790ms because itprocesses the binding procedure and group joining procedure at
the same time. We can also notice that the proposed scheme
prevent the throughput from dropping to zero because it
performs the buffering.

237
237

Figure 8. Comparison of UDP Throughput

Figure 9, 10 and 11 shows TCP sequence numbers that MN
received. Figure 9 shows received TCP sequence number when
P-M Interaction is used. When MN’s global mobility is
performed, binding delay with HA and group joining delay is
occurred during 1990ms that is similar to UDP case. During this
delay time, the packets sent by the source cannot be delivered to
MN and these undelivered packets are retransmitted by TCP
algorithm. These retransmission causes additional delay forpacket delivery. Therefore TCP packets experience more delay
than UDP packets. Figure 10 shows the received TCP sequencenumbers that MN received when I-PMIP is used. I-PMIP also
has binding and group joining delay during 1550ms andadditional retransmission delay. As shown in Figure 11,
however, the proposed low latency global mobility schemeshows relatively short delay due to the fast binding. Also, using
buffering scheme, there is no need to retransmit the packets,
which leads to seamless multicast service without additional
delay.

Figure 9. TCP sequence numbers in P-M Interactio
Figure 7. Comparison of UDP datagram ID

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACTTransactions
Transactionson
onthe
Advanced
Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol.
Vol.
2, Issue2, Issue
3, May3, May
2013
2013

238

Figure 12 shows comparison of TCP throughput for threemethods. TCP throughput result is very similar to UDP
throughput. P-M Interaction is falling down to 0 during 2000ms.
And I-PMIP is falling down to 0 during 1600ms. But inproposed scheme, it shows that is not falling down to 0, even if
throughput is falling down during 800ms. As a result, thesethroughput results show the proposed scheme is more effective
than other two methods.

V. CONCLUSION

Figure 10. TCP sequence number in I-PMIP

In this paper, we propose a new low latency global mobility
scheme which supports seamless multicast service without
out-of-sequence problem and packet loss in PMIPv6 networks.
Before discussing the proposed scheme, the existing global
mobility schemes in PMIPv6 networks are explained. The
multicasting based on existing global mobility schemes has the
problem in that it causes the unnecessary delay and service
disconnection because the global binding to HA or new agent
and the group joining procedure has to be occurred in turn. In
the proposed scheme, however, the global mobility can besupported without extra global binding because each LMA canidentify each other using LMA global address option added to
RA massage format. Additionally, LMA could prevent thepacket loss using buffering procedure during the global mobility
procedure …..
Using the NS-2 simulation, we showed that the proposed
scheme reduces the unnecessary delay that the existing global
mobility methods have.
REFERENCES[1][2]

Figure 11. TCP sequence number in proposed Inter mobility scheme
[3]
[4]
[5]
[6]

D. Johnson, C. Perkins, J. Arkko, “Mobility Support in IPv6,” IETF RFC
3775, Jun. 2004.
S. Gundavelli, K. Leung, V. Devarapalli, K. Chowdhury, B. Patil, “Proxy
Mobile IPv6,” IETF RFC 5213, Aug, 2008.
G. Giaretta, “Interactions between PMIPv6 and MIPv6: scenarios andrelated issues,” <draft-ietf-netlmm-mip-interactions-06>, May. 2010.
N. Neumann, J. Lei, X. Fu, G. Zhang, “I-PMIP: An Inter-Domain
Mobility Extension for Proxy-Mobile IP,” IWCMC’ 09, Jun. 2009
R. Vida, L. Costa, “Multicast Listenet Discovery Version 2 (MLDv2) for
IPv6,” IETF RFC 3810, Jun. 2004
“The
Network
Simulator-ns(version2)
website,”http://www.isi.edu/nsnam.

Hwan-gi Kim is currently enrolled in M.Sc. program atthe Dept. of Electronics and communications
Engineering, Kwangwoon University, Seoul, Korea. His
research interests include mobile network protocol,
sensor network, context reasoning, augmented reality.

Figure 12. Comparison of TCP Throughput

Jong-min Kim received the M.Sc degree at the Dept. ofElectronics
andcommunications
Engineering,
Kwangwoon University, Seoul, Korea. . His researchinterests include mobile network protocol, sementic web,context reasoning.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACTTransactions
Transactionson
onthe
Advanced
Communications
Technology(TACT)
Vol.
2, 2,
Issue
3, 3,
May
2013
ICACT
Advanced
Communications
Technology(TACT)
Vol.
Issue
May
2013

239

Hwa-sung Kim received Ph.D degree at the Dept. ofComputer Science, Lehigh University, Bethlehem, PA
18015 USA. He is now a professor at the Dept. ofElectronics
andcommunications
Engineering,
Kwangwoon University, Seoul, Korea. His researchinterests include mobile network protocol, mobile web
computing, embedded software

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
Advanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May
2013

240
240

Alternatives to Network Selection in
Heterogeneous Wireless Environments
Vinícius de Miranda Rios*, Claudio de Castro Monteiro**, Vanice Canuto Cunha****Information Systems Department, University of Tocantins, Palmas - TO - Brazil**Federal Institute of Education, Science and Technology of Tocantins, Computing Science Department, Palmas - TO Brazil, ACM member***University of Brasilia, Electrical Engineering Department, Brasilia - DF - Brazil
vinicius.mr@unitins.br, ccm.monteiro@acm.org, vanicecunha@gmail.com

It can be classified into two types [3]: horizontal andvertical. The horizontal handover is designed to manage
switching between similar network technologies (e.g. wi-fi(Wireless Fidelity) to wi-fi) during a data transfer, in which
only the signal loss is the motivation for the exchange ofaccess points, whereas the vertical handover aims to manage
switching between different access technologies (e.g. wi-fi to3G) during a data transmission, in which the use of preferred
applications requiring certain thresholds for each requirementof QoS (Quality of Service) or the user preferences are themotivators for exchanging access points [4].
This access network exchange happens in three distinct
Index Terms—Network selection, AHP, GRA, Fuzzy logic
steps, which are [2]:• finding networks in the environment in which theI.
INTRODUCTION
mobile device is located;• approaches to decision making/selection of the best
With the emergence of wireless networks, users haveavailable access network;
become able to move into many different environments. This
mobility has brought some challenges such as [1]: choosing• the implementation of the change of the access points.
the best access network, keeping the session to data
Therefore, the network selection, as an integrating andtransmission and allowing the mobile user to be always best indispensable part of the handover management, aims toconnected anywhere and anytime on the best available access provide the mobile user with the best traffic condition accessnetwork (ABC conception – Always Best Connected).
point, allowing applications, whether voice, data or video, to
In this sense, the next-generation wireless networks be transmitted with the required quality from source to(NGWN) focus on the free users’ movement between destination.
heterogeneous networks through mobile terminals (Notebooks,
The remainder of this paper is organized as follows: section
Netbooks, PDA - Personal Digital Assistant, cell phones, etc.) 2 presents related work to the techniques used; section 3
with network interfaces of different technologies (WWAN - describes the proposed network selection; the characterization
Wireless Wide Area Network, WLAN - Wireless Local Area of the experiments is shown in section 4; in section 5, theNetwork, WMAN - Wireless Metropolitan Area Network, etc.) obtained results are displayed stating which proposal is theallowing continuous access to real or not real time services, best; and finally, section 6 presents the conclusion and future
always aiming at the continuity of the service (seamless).
work.
Keeping the service active while switching access networks
is the function of one of the key parts of mobility management,
II.
RELATED WORK
called handover [2]. The handover function is to control
The authors in [11] make a comparison among the MADM,
exchanges between users’ access points during a data
SAW, WP and TOPSIS methods, whose goal is to classify
transmission [1].
access networks in three different scenarios. In the firstscenario, TOPSIS and SAW methods proved similar in
Manuscript received July 30, 2012. This work was supported in part by theclassification of networks, while the WP method showed a
UnB – University of Brasilia.
Vinícius de Miranda Rios is with the UNITINS – University of Tocantins, slight variation in its classification. In the second scenario,
where two networks are removed from the classification, theBR Brazil (+55 (63)-3218-4926; e-mail: vinicius.mr@unitins.br).
Claudio de Castro Monteiro is with the IFTO – Federal Institute of SAW and WP methods proved similar, while the TOPSIS
Education, Science and Technology of Tocantins, BR Brazil (+55 (63)-3229- method obtained a change in its classification for suffering
2200; e-mail: ccm.monteiro@ acm.org).
from the problem of abnormality ranking. Finally, a
Vanice Canuto Cunha is with the IFB – Federal Institute of Education, distinction between the classifications obtained in the previous
Science and Technology of Brasilia, BR Brazil (+55 (61)-2103-2154; e-mail: scenarios is presented in the third scenario, where the TOPSIS
vanicecunha@gmail.com).
method proves to be more consistent in the variations, while
the SAW and WP methods are constant with little variability
in the classification of networks.
Abstract—This article presents two proposals in order to solve
the problem of choosing the best access network available in theenvironment where the user is located. One based on a
combination of fuzzy logic technique with two decision-making
methods, AHP (Analytic Hierarchy Process) and GRA (Grey
Relation Analysis), and the other based only on fuzzy logic
technique. In order to demonstrate the effectiveness of these
proposals, they were compared with a third one, of the authors in[7], which uses a combination of AHP method with a costfunction. The obtained results show that the two proposals
presented in this paper are more efficient in sorting and selecting
the best access network when compared to the third.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACTTransactions
Transactionson
onthe
Advanced
Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol.Vol.
2, Issue2, Issue
3, May3, May2013
2013

The network selection made by the authors in [12] is basedon a fuzzy multiple criteria decision-making, where all theselected criteria are normalized by a normalization function
and the result is fuzzified, generating a degree of membership
between 0 and 1, which will be used to give weights to thesecriteria. Finally, the selection of the best access network is
made by a cost function.
The network selection algorithm of the authors in [7] ispremised on originally giving preference to the UMTS
(Universal Mobile Telecommunications System) in case the
wi-fi is not available, since the former has a larger
geographical coverage and does not allow the mobile device
to run out of connection. Thus, the environment where theterminal is located is composed of four networks: three wi-fi
access points and a 3G base station (cellular network).
Therefore, the mobile terminal is shifted 1000m, at an average
speed of 1m/s, with 3G coverage during all its path, while the
wi-fi network coverage is segmented.
Therefore, the mobile device only initializes the datacollection in order to select the best access network when
there is at least one wi-fi network available, when the signal
limit is greater than the established limit and when it may
possibly stay longer in this environment, avoiding the pingpong effect, thus. After confirmation, the mobile device
initiates the data collection.
The final decision on network selection shall be taken by
the cost function (4), in which the weights used by W j are
given through the AHP method. In this way, the authors
standardized parameters using the following assumptions:
The bigger, the better:S(xij ) =

xij

(1)

max{x ij | i = 1,2,..., m}

The smaller, the better:S(xij ) =Where

min{x ij | i = 1,2,..., m}

(2)

xij

xij express parameter j in network i.

The normalization of the parameters is:S(x )N(xij ) = n ij
∑ S(xij )i = 1,2,..., m

(3)

j=1

Therefore, the rate of network decision-making can becalculated as:n

I i = ∑ w j N(X ij )i = 1,2,..., m

(4)

j=1

The values of each parameter used to select the best accessnetwork are characterized in Table I.
TABLE I.
NETWORK PARAMETERS
Parameters
UMTS WLAN1
QoS
Available
0,4
20

WLAN2
10

factors

bandwidth
Delay
Jitter

Cost

Reliability

Bit error
rate
PacketLoss
Cell load

Securit

241
241

20
5
1

30
10
0,01

50
10
0,01

40
10
0,01

10-9

10-6

10-6

10-5

0,01

0,1

0,2

0,1

0,3
8

0,8
4

0,1
2

0,8
4

The entire article was implemented by using the EXCEL
spreadsheet. There the data provided by the authors wereinserted, as well as the values of the collected parameters andalso the implementation of the AHP method and the above
equations, which reached the same values of weights andresults presented in the article.
The exception of results could be noticed in thenormalization of parameters of the scenario when the mobile
is in the area of WLAN2, WLAN3 and 3G network coverage.
The parameters α, β, γ and δ, express the QoS factors, cost,reliability and security respectively. The values generated by
our implementation differ from those provided by the article,
as shown in tables II and III below.
TABLE II.
NORMALIZATION OF NETWORK PARAMETERS
Simulation
Scenarios
UMTS
WLAN2
WLAN3

α
0.01
0.33
0.66

0.48
0.32
0.20

β
0.50
0.25
0.25

0
0.50
0.50

γ
1
0
0

0.87
0.09
0.04

δ
0.23
0.09
0.68

0.57
0.29
0.14

TABLE III.
OUR NORMALIZATION OF NETWORK PARAMETERS
SimulationαβγScenarios
UMTS
0.01
0.53
0.50
0
1
0.87
0.23
WLAN2
0.25
0.21
0.25
0.50 0
0.04
0.69
WLAN3
0.99
0.33
0.33
0.99 0
0.09
0.27

0.57
0.14
0.33

δ

A big difference between the values generated by thenormalization equation (3) of the authors in [7] in Table II canbe observed in relation to our values presented in Table III.
Therefore, the result we reached was that, in the scenario
where the preference is for reliability and security, the mobile
selected the 3G network all the times, whereas in the article
there is a selection for WLAN2 network.
In addition, when we implement the article in a real testbed,
we can observe that if there is a value of the collectedparameters equal to 0, its final result will also be 0, thus
affecting the choice of the best access network, as it is shownin section 5.
III.

PROPOSALS

The proposals of this work are presented here with theintention of using the fuzzy logic, AHP and GRA for thecomposition of an architecture of network selection inheterogeneous wireless network environments.

WLAN3
30

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
ICACT Transactions
Transactions onon theAdvanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013

A. Proposal 1- Network selection by using Fuzzy logic
The first network selection proposal aims to using only thetechnique of fuzzy logic considering the accurate output
provided upon the input of raw data collected by the system,
as it is illustrated in Figure 1.

242
242

This collection takes place through two rounds of 10 ICMP
requisitions, and because we are dealing with the sum of all
end-to-end delay [8], the average RTT (Round Trip Time)values are also stored in each round.
TABLE IV.
PSEUDOCODE FOR COLLECTOR FUNCTION
collector(redes, iteracoes, rounds, ICMP, custo){
while(n < rounds){
while(m < iteracoes){
redes.dados = requisicoes.ICMP;
delay = f(redes.dados.delay);
jitter = f(redes.dados.jitter);perda = f(redes.dados.perda);
processor(redes, delay, jitter, perda, custo);n = n + 1;}
m = m + 1;}}

Figure 1.

Architecture of proposal 1 using only fuzzy logic only.

Thus, the system is divided into three functional blocks,
which are: the collector, processor and decision-maker.
Collector
The collector aims to collecting data on delay, jitter andpacket loss, provided by the ping application, as it can be seenin Figure 2. The monetary cost parameter is fixed, so there isno need to be collected, but only informed by the mobile
operator, taking the value of wi-fi networks equal to zero real
and the values in networks 1 and 2 base stations equal to 89.9
and 79.9 brazilian reals, respectively, since only the accessfrom the terminal to the access point is considered, i.e., the
ICMP (Internet Control Message Protocol) requisitions of theping (packet internet grouper) application will be transmitted
from the client terminal to the interface output gateway,
passing only by the access point it is connected.

Figure 2.

File with the collection of ICMP requisitions.

Processor
The processor aims to manipulate the data that is collected
through the fuzzy logic technique in order to classify accessnetworks in the environment the terminal is located. For this,audio thresholds were used in the fuzzy system, because they
are already well known and documented and they also statethat in an audio transmission (VoIP), the delay cannot begreater than 300ms, the jitter cannot be greater than 150ms
and the packet loss may not exceed over 3% [9], [10], whichmakes the sound unintelligible to the human ear in such cases.
Under this assumption, each linguistic variable (jitter, delay,
packet loss and monetary cost) has three linguistic terms in the
fuzzy system, which are: low, medium and high, where theuniverse of discourse of each of them is within the thresholdsof the audio traffic. Each of these terms was fuzzified with thefunction of triangular relevance and in accordance with
the Mandani method inference on the obtained result, as it canbe seen in Figure 3.

Figure 3.

Fuzzification.

Each fuzzy subset within the discourse universe associatedwith the delay is composed of:

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
ICACT Transactions
Transactions onon theAdvanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013• low, between 0 and 200;
• medium, between 100 and 300;
• high, above 300.
But the discourse universe associated with the jitter iscomposed of:• low, between 0 and 100;
• medium, between 50 and150;• high, above 150.
The same happens in the discourse universe associatedwith the packet loss:• low, between 0 and 1;• medium, between 0.5 and 1.5;
• high, above 1.5.
And finally, the discourse universe associated with themonetary cost:• low, between 0 and 90;• medium, between 45 and 145;
• high, above 145.
Figure 4 illustrates the rules used in this proposal.

243
243

• great, above 5.
TABLE V.
PSEUDOCODE FOR PROCESSOR FUNCTION
processor(redes, delay, jitter, perda, custo){
redes.analise[]=delay, jitter, perda;
OQN=fuzzy(redes.analise.delay, redes.analise.jitter,redes.analise.perda, redes.custo);decisor(OQN);}

Decision-Maker
The decision-maker module checks, every 60 seconds, thetotal time of iteration, the highest score access point generated
by the processor module, then stores it in a text file, thereby
allowing any mobility management solution on layer 3 to readit and to take the decision to perform the handover to the
network stored in its content. Then, as it can be seen, theproposed fuzzy system consists of four inputs and one output,
the latter stating how much quality each network has using
the OQN variable.
TABLE VI.
PSEUDOCODE FOR DECISION FUNCTION

Figure 4.

Rule Base.

Finally, in the fuzzification, there is the linguistic variable
called OQN (Objective Quality of Network) which has five
linguistic terms: bad, close to good, good, close to great
and great, as it can be seen in Figure 5. The final result is
calculated by the center of maximum defuzzification method.

Figure 5.

Defuzzification.

Each fuzzy subset within the discourse universe associatedwith OQN is composed of:• bad, between 0 and 1;• close to good, between 1 and 3;• good, between 2 and 4;• close to great, between 3 and 5;

decisor(OQN){
if(WLAN1.OQN > WLAN2.OQN){
if(WLAN1.OQN > 3G1.OQN){
if(WLAN1.OQN > 3G2.OQN){
altera_rede(WLAN1);}
else{
altera_rede(3G2);
}
}
else
{
if(3G1.OQN > 3G2.OQN){
altera_rede(3G1);}
else{
altera_rede(3G2);
}
}
}
else
{
if(WLAN2.OQN > 3G1.OQN){
if(WLAN2.OQN > 3G2.OQN){
altera_rede(WLAN2);}
else{
altera_rede(3G2);
}
}
else
{
if(3G1.OQN > 3G2.OQN){
altera_rede(3G1);}
else{
altera_rede(3G2);}}}

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013
the Advanced
Communications
Technology(TACT)
2, Issue
3, May
2013

244
244

}

Main
The algorithm is modularized and parameterized, i.e., itenables the user to perform the experiments with the necessaryamount of ICMP repetitions and requisitions. The algorithm
below is started to pass on the information the collector
module will need to gather the values to be processed by theprocessor module.
TABLE VII.
PSEUDOCODE FOR MAIN FUNCTION
main(){
read(redes);while (redes >= 2 && redes <= 4){read(host);read(gateway);read(custo[]);}read(iteracoes);read(rounds);
read(ICMP);
collector(redes, iteracoes, rounds, ICMP, custo);}

B. Proposal 2 - Network Selection using Fuzzy Logic, AHP
and GRAThe aim of the second proposal of network selection is to
use the combination of two strategies: fuzzy logic, which isalike the first proposal, combined with two MADM methods,
AHP and GRA. This combination aims to propose a different
vision of combination between decision-making methods andartificial intelligence techniques.
The choice of AHP was motivated by being an efficientmethod to generate weights for objective data, while thechoice of GRA was motivated by being a very efficientmethod in sorting alternatives to meet a particular purpose, inthis case, the choice of the best access network, as it can benoted in the authors’ article [4], and [11]. Figure 6 illustrates
this proposal.

Collector
The collector module works the same way as in the first
proposal.
Processor
The processor module uses all the features of the fuzzy
logic of the first proposal and, in parallel, it uses the GRA
method, which will also receive the same collected values ofjitter, delay, packet loss and monetary cost. The generated
result will be the rank (score) of each network. This
classification is possible due to the weights provided by the
AHP, according to each criterion. The weights generated by
the AHP for jitter, delay, packet loss and monetary costcriteria are 0.18, 0.25, 0.05 and 0.52, respectively.
These weight values were based on the importance of each
QoS criterion of network for audio transmission, i.e., for voicetraffic the jitter has a bit greater importance than the delay andthey have far greater importance than the packet loss [10],while the monetary cost to the user's preference has muchgreater importance than the previous criteria, since it isassumed that the user will always opt for the cheapest accessnetwork when a change in the access network is necessary.
TABLE VIII.
PSEUDOCODE FOR PROCESSOR FUNCTION
processor(redes, delay, jitter, perda, custo){
ahp[]=peso.delay, peso.jitter, peso.perda,
peso.custo;
redes.analise[]=delay, jitter, perda;
gray=GRA(redes.analise.delay,redes.analise.jitter,redes.analise.perda,redes.custo);
OQN=FUZZY(redes.analise.delay,redes.analise.jitter,redes.analise.perda,redes.custo);
NQI=(gray + OQN)/2;
decisor(NQI);}

The decision-maker module checks, every 60 seconds,
the total time of iteration, the highest score access point
generated by the processor module, then stores it in a text file,
thereby allowing any software to read it and to take thedecision to perform the handover to the network stored in its
content. Then, as it can be seen, the proposed fuzzy system
consists of four inputs and one output. This output informs
how much quality each network has using the NQI (Network
Quality Index) variable. The value of this variable is the resultof the arithmetic average of the OQN variable value with the
GRA value.
TABLE IX.
PSEUDOCODE FOR DECISION FUNCTION

Figure 6. Architecture of Proposal 2 using fuzzy logic, AHP and GRA.

Just as in proposal 1, the system is divided into threefunctional blocks, which are: the collector, the processor andthe decision-maker.

decisor(iQR){
if(WLAN1.iQR > WLAN2.iQR){
if(WLAN1.iQR > 3G1.iQR){
if(WLAN1.iQR > 3G2.iQR){
altera_rede(WLAN1);}
else{
altera_rede(3G2);}

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013
the Advanced
Communications
Technology(TACT)
2, Issue
3, May
2013

}
else
{
if(3G1.iQR > 3G2.iQR){
altera_rede(3G1);}
else{
altera_rede(3G2);
}
}
}
else
{
if(WLAN2.iQR > 3G1.iQR){
if(WLAN2.iQR > 3G2.iQR){
altera_rede(WLAN2);}
else{
altera_rede(3G2);
}
}
else
{
if(3G1.iQR > 3G2.iQR){
altera_rede(3G1);}
else{
altera_rede(3G2);
}
}
}

245
245

the network selection process without mobility, a scenario
involving two computers and two wi-fi access points was set
up, structured as shown in Figure 7 and with the following
function:• Computer 1: Client;• Computer 2: Router;• wi-fi access point 1: 802.11b;• wi-fi access point 2: 802.11g;• 3G Base Station 1: UMTS;• 3G Base station 2: UMTS.
The client computer has two USB (Universal Serial Bus)
network interfaces and two USB 3G network interfaces, eachpreviously connected to its respective access point. Therefore,
the wi-fi interfaces are connected to access points 1 and 2,
while the 3G interfaces are connected to base stations 1 and 2of distinct cellular operators.

}

Main
The algorithm is modularized and parameterized, i.e.,
it enables the user to perform the experiments with thenecessary amount of ICMP repetitions and requisitions. The
algorithm below is started to pass on the information thecollector module will need in order to gather the values tobe processed by the processor module.
TABLE X.
PSEUDOCODE FOR MAIN FUNCTION
main(){
read(redes);while(redes <= 2){read(host);read(gateway);read(custo);}read(iteracoes);read(rounds);
read(ICMP);
collector(redes, iteracoes, rounds, ICMP);}IV.

METHODOLOGY

Here the necessary procedures to implement the proposals
outlined above are presented in order to demonstrate theeffectiveness in the proposed scenarios.
C. Experiments without mobility
To assess the impact of network parameters (QoS), jitter,delay and packet loss, besides the monetary cost parameter inFigure 7.

Representation of the scenario structure of the tests without
mobility.

All computers used in the assembly of this scenario havethe same configuration: Intel Atom Dual Core processor, 2GB
RAM and 500GB hard drive. Table 10 shows the list of
software and hardware installed and used on the computers.
TABLE XI.
LIST OF SOFTWARE AND HARDWARE USED IN THE SCENARIO FOR THE
EXPERIMENTS
Computer
SoftwareHardware1- Linux Ubuntu v.
- Two wi-fi network cards:
11.04 Natty Narwhal* Tenda 802.11N pattern.
Operating System;- Two 3G network cards:* ONDA MAS190UP model;- gcc v. 4.5.2* HUAWEI E173 model.
2- FreeBSD v. 8.2- Three network cards:
operating system.
* 100Mbits Ethernet.
- ipfw dummynet v. 4.

Thus, the experiment consisted of 35 iterations. Each
iteration is composed of two rounds and each turn consists of
10 ICMP requisitions from the client bound to the network
gateway where the interface is connected. The values of jitter,delay and packet loss generated in these two turns werecollected during a whole week, in the morning, afternoon and

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
ICACTTransactions
Transactionson
onthe
Advanced
Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol.Vol.
2, Issue2, Issue
3, May3, May2013
2013

evening, totaling 420 iterations on a single day. Each item ofthis experiment has the following characteristics:• iteration: includes the whole process, i.e., collection,
processing and decision;• turn: is the action of collecting the data for each
network criterion;• collection: consists of sending ICMP requisitions to thegateways of each network interface;• collection time: is the time at which the collection is
performed.
It is noteworthy that the best access network is selected ineach iteration (60 seconds). The competing traffic generated
by the server through ipfw (ipfirewall) command was only for
wi-fi networks, since telecommunication operators do notallow access to the infrastructure core of 3G networks.
D. Experiments with mobility
To assess the impacts of network parameters (QoS), jitter,delay and packet loss, in addition to the monetary cost
parameter in the network selection process with mobility, ascenario with a notebook, access point and a 3G operator was
set up, structured as shown in Figure 8 and having thefollowing function:• notebook;• wi-fi access point: 802.11g pattern;• 3G Base station: UMTS.
The client computer contains a wi-fi network interface anda USB 3G network interface, which are previously connected,
each to its respective access point. The wi-fi interface isconnected to the access point, while the 3G interface isconnected to a base station of a mobile operator.

Figure 8.

Representation of the structure of the test scenario with mobility.

The notebook used in this scenario has the following
configuration: Intel Core 2 Duo 2.4 Ghz processor, 4GB RAM
and 250GB hard drive. Table 16 shows the list of installed andused software and hardware.
TABLE XII.
LIST OF SOFTWARE AND HARDWARE USED IN THE SCENARIO FOR THE
EXPERIMENTS
Computer
SoftwareHardware1- Linux Ubuntu v. 11.04- One wi-fi network card:
Natty Narwhal* Broadcom model
Operating System;
BCM4328 802.11b/g pattern.
- One 3G network card:- gcc v. 4.5.2.
* ONDA MAS190UP model;

246

Thus, the experiments consisted of 358 iterations generated
during the one hundred (100) drives, in which fifty (50) fromthe starting point toward the edge of the wi-fi access point celland fifty (50) from the edge of the wi-fi access point cell tothe starting point, at an average speed of 1m/s straight, as it
can be seen in Figure 9.
Each iteration is composed of two turns and each turnconsists of 10 ICMP requisitions from the client bound only to
the gateway of each one of the access networks. It collects thevalues of jitter, delay and packet loss generated in these twoturns. It is important to stress that the best access network is
selected in each iteration (60 seconds). Each item of thisexperiment has the following characteristics:• iteration: includes the whole process, i.e., collection,
processing and decision;• turn: is the action of collecting the data for each
network criterion;• collection: consists of sending ICMP requisitions to thegateways of each network interface.
• collection time: is the time at which the collection is
performed.

Figure 9.

Trajectory of the mobile.

Based on the experiments of these two scenarios, someanalyses were necessary to validate the proposals presented inthis article.
V.

RESULTS

E. Results of the experiments without mobility
The result obtained with each of the proposals in the
scenario without mobility is characterized in the following
figures, in which charts 1, 2 and 3 represent the collector andthe processor modules, while charts 4, 5 and 6 represent thedecision-maker module. In each examination, a universe of
420 collection iterations of network variables (delay, jitter and

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013
ICACT Transactions on the Advanced Communications Technology (TACT) Vol. 2, Issue 3, May 2013

packet loss) was observed and also the amount of times aparticular network was selected by each of the proposals. This
amount is expressed in percentage in charts 4 to 6, while incharts 1 (P1), 2 (P2) and 3 (P3) the averages of these selected
network variables are shown considering the set of samples
which resulted in the selection of that particular network. Thismethodology was followed for the presentation of otherresults.
No competing traffic on WLAN1 and WLAN2 networks
Figure 10 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.

Figure 10.

No competing traffic on the WLAN1 and WLAN2 networks.

As it can be seen in chart 5, WLAN1 network has got
63.81% of choice, averaging its delay network parameteraround 500ms, jitter around 30ms and packet loss around 1%.
The WLAN2 network has obtained 25.95% of choice,averaging its delay network parameter around 300ms, jitteraround 60ms and packet loss around 2%. The 3G1 network
has obtained 1.67% of choice, averaging its delay network
parameter around 500ms, jitter around 60ms and packet loss
around 4%. And finally, the 3G2 network has obtained 8.57%
of choice, averaging its delay network parameter around450ms, jitter around 60ms and packet loss around 0%.
In the first proposal, represented by the fourth chart, there is39.38% of choice in the selection of more than one accessnetwork, which is a problem, since the handover has tohappen for the best network selected. The WLAN1 network
has got 40.57% of choice, averaging its delay network
parameter around 350ms, jitter around 50ms and packet loss
around 0%. The WLAN2 network has obtained 7.40% of
choice, averaging its delay network parameter around 500ms,

247
247

jitter around 150ms and packet loss around 3%. The 3G1
network has obtained 2.86% of choice, averaging its delay
network parameter around 450ms, jitter around 60ms andpacket loss around 3%. Finally, the 3G2 network has got
9.79% of choice, averaging its delay network parameteraround 550ms, jitter around 60ms and packet loss around 0%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 53.33% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 16.67% of choice, averaging its delay network
parameter around 600ms, jitter around 180ms and packet loss
around 12%. The 3G2 network has got 30% of choice,averaging its delay network parameter around 14000ms, jitteraround 13500ms and packet loss around 13%.
Moderate competing traffic in the WLAN1 network
Figure 11 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.
As it can be seen in chart 5, the WLAN2 network obtained
61.19% of choice, averaging its delay network parameteraround 400ms, jitter around 60ms and packet loss around 1%.
The 3G1 network has obtained 9.29% of choice, averaging its
delay network parameter around 400ms, jitter around 70ms
and packet loss around 0%. And finally, the 3G2 network has
obtained 29.52% of choice, averaging its delay network
parameter around 400ms, jitter around 60ms and packet loss
around 0%. It may be observed that because the WLAN1
network parameters are above the audio traffic parametersconsidered as good or great, it was not selected in any of
the iterations.
In the first proposal, represented by chart 4, there is 0.24%
of choice in selecting more than one access network, whichcauses a problem, since the handover has to happen for thebest network selected. The WLAN2 network has obtained
55.24% of choice, averaging its delay network parameteraround 400ms, jitter around 60ms and packet loss around 1%.
The 3G1 network has obtained 11.19% of choice, averaging
its delay network parameter around 400ms, jitter around 70ms
and packet loss around 0%. And finally, the 3G2 network has
obtained 33.33% of choice, averaging its delay network
parameter around 400ms, jitter around 60ms and packet loss
around 0%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 75.95% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 6.19% of choice, averaging its delay network
parameter around 700ms, jitter around 300ms and packet loss
around 8%. The 3G2 network has obtained 17.86% of choice,averaging its delay network parameter around 4800ms, jitteraround 4100ms and packet loss around 3%.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
ICACT Transactions
Transactions onon theAdvanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May
2013

248
248

Figure 11. Moderate competing traffic in the WLAN1 network.

Figure 12. Moderate competing traffic in WLAN2 network.

Moderate competing traffic in the WLAN1 network
Figure 12 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.
As it can be seen in chart 5, the WLAN1 network has got
82.62% of choice, averaging its delay network parameteraround 300ms, jitter around 20ms and packet loss around 2%.
The 3G1 network has obtained 2.86% of choice, averaging its
delay network parameter around 400ms, jitter around 120ms
and packet loss around 0%. And finally, the 3G2 network has
obtained 14.52% of choice, averaging its delay network
parameter around 400ms, jitter around 110ms and packet loss
around 2%. It may be observed that because the WLAN2
network parameters are above the audio traffic parametersconsidered as good or great, it was not selected in any of theiterations.
In the first proposal, represented by chart 4, there is 0.71%
of choice in the selection of more than one access network,
which causes a problem, since the handover has to happen
for the best network selected. The WLAN1 network has got
79.52% of choice, averaging its delay network parameteraround 300ms, jitter around 20ms and packet loss around 2%.
The 3G1 network has obtained 3.10% of choice, averaging
its delay network parameter around 400ms, jitter around120ms and packet loss around 0%. And finally, the 3G2
network has obtained 16.67% of choice, averaging its delay
network parameter around 400ms, jitter around 110ms andpacket loss around 2%.

The authors’ proposal in [7], represented by chart 6, has notselected any network in 51.19% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 16.19% of choice, averaging its delay network
parameter around 1400ms, jitter around 900ms and packet loss
around 10%. The 3G2 network has obtained 32.62% of choice,averaging its delay network parameter around 8000ms, jitteraround 7500ms and packet loss around 9%.
Moderate competing traffic in the WLAN1 and WLAN2
networks
Figure 13 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.
As it can be seen in chart 5, the 3G1 network has obtained
29.52% of choice, averaging its delay network parameteraround 500ms, jitter around 60ms and packet loss around 0%.
And finally, the 3G2 network has obtained 70.48% of choice,averaging its delay network parameter around 400ms, jitteraround 50ms and packet loss around 2%. It may be observedthat because the WLAN1 and WLAN2 network parameters
are above the audio traffic parameters considered as good orgreat, they were not selected in any of the iterations.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
Advanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013

Figure 13. Moderate competing traffic in the WLAN1 and WLAN2
networks.

In the first proposal, represented by chart 4, there is 2.62%
of choice in selecting more than one access network, which isconsidered a problem, since the handover has to happen forthe best network selected. The WLAN2 network has obtained
0.24% of choice, averaging its delay network parameteraround 6700ms, jitter around 500ms and packet loss around
0%. The 3G1 network has obtained 27.62% of choice,averaging its delay network parameter around 500ms, jitteraround 60ms and packet loss around 0%. And finally, the 3G2
network has obtained 69.52% of choice, averaging its delay
network parameter around 400ms, jitter around 50ms andpacket loss around 2%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 54.52% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 19.05% of choice, averaging its delay network
parameter around 700ms, jitter around 200ms and packet loss
around 11%. The 3G2 network has obtained 26.43% of choice,averaging its delay network parameter around 14000ms, jitteraround 13000ms and packet loss around 9%.
High competing traffic in the WLAN1 network
Figure 14 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.

249
249

Figure 14. Competing traffic in the WLAN1 network.

As it can be seen in chart 5, the WLAN2 network has
obtained 65.95% of choice, averaging its delay network
parameters around 400ms, jitter around 50ms and packet loss
around 4%. The 3G1 network has obtained 12.14% of choice,averaging its delay network parameter around 400ms, jitteraround 190ms and packet loss around 0%. And finally, the3G2 network has obtained 21.90% of choice, averaging its
delay network parameter around 400ms, jitter around 50ms
and packet loss around 0%. It may be observed that because
the WLAN1 network parameters are above the audio trafficparameters considered as good or great, it was not selected inany of the iterations.
In the first proposal, represented by chart 4, there is 0.71%
of choice in the selection of more than one access network,
which is considered a problem, since the handover has tohappen for the best network selected. The WLAN2 network
has obtained 59.05% of choice, averaging its delay network
parameter around 400ms, jitter around 50ms and packet
loss around 4%. The 3G1 network has obtained 14.29% of
choice, averaging its delay network parameter around 400ms,
jitter around 190ms and packet loss around 0%. And finally,
the 3G2 network has obtained 25.95% of choice, averaging its
delay network parameter around 400ms, jitter around 50ms
and packet loss around 0%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 59.76% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 10.24% of choice, averaging its delay network
parameter around 1900ms, jitter around 1550ms and packet
loss around 10%. The 3G2 network has got 30% of choice,averaging its delay network parameter around 6400ms, jitteraround 5550ms and packet loss around 8%.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013
ICACT Transactions
Transactions onon theAdvanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013

High competing traffic in the WLAN2 network
Figure 15 shows that proposals 2 and 3, charts 5 and 6
respectively, have succeeded in obtaining great performancein selecting the best access network, within the given
characteristics.

250
250

and WLAN2 networks. The 3G1 network has obtained
13.57% of choice, averaging its delay network parameteraround 250ms, jitter around 10ms and packet loss around 10%.
The 3G2 network has obtained 31.90% of choice, averaging
its delay network parameter around 300ms, jitter around 15ms
and packet loss around 7%.
High competing traffic in WLAN1 and WLAN2 networks
Figure 16 shows that the second proposal, chart 5, hasperformed well in selecting the best access network whencompared to the first proposal, but a little worse than the third
proposal.

Figure 15. Competing traffic in WLAN2 network.

As it can be seen in chart 5, the WLAN1 network has got
86.43% of choice, averaging its delay network parameteraround 250ms, jitter around 10ms and packet loss around 2%.
The 3G1 network has obtained 3.10% of choice, averaging
its delay network parameter around 260ms, jitter around 10ms
and packet loss around 0%. And finally, the 3G2 network has
obtained 10.48% of choice, averaging its delay network
parameter around 250ms, jitter around 10ms and packet loss
around 0%. It may be observed that because the WLAN2
network parameters are above the audio traffic parametersconsidered as good or great, it was not selected in any of theiterations.
In the fourth graph representing the first proposal, there is
0.48% of choice in the selection of more than one accessnetwork, which is a problem, since the handover has tohappen for the best network selected. The WLAN1 network
has got 83.33% of choice, averaging its delay network
parameter around 250ms, jitter around 10ms and packet loss
around 2%. The 3G1 network has obtained 4.52% of choice,averaging its delay network parameter around 260ms, jitteraround 10ms and packet loss around 0%. And finally, the 3G2
network has obtained 11.67% of choice, averaging its delay
network parameter around 250ms, jitter around 10ms andpacket loss around 0%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 54.52%of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in WLAN1

Figure 16. Competing traffic in the WLAN1 and WLAN2 networks

As it can be seen in chart 5, there is 0.24% of choice inthe selection of more than one access network, which is aproblem, since the handover has to happen for the best
network selected. The WLAN1 network has got 1.19% of
choice, averaging its delay network parameter around30400ms, jitter around 21000ms and packet loss around80%. The WLAN2 network has obtained 1.67% of choice,averaging its delay network parameter around 50400ms, jitteraround 42000ms and packet loss around 80%. The 3G1
network has obtained 20.95% of choice, averaging its delay
network parameter around 400ms, jitter around 10ms andpacket loss around 0%. And finally, the 3G2 network has
obtained 75.95% of choice, averaging its delay network
parameter around 400ms, jitter around 10ms and packet loss
around 2%.
In the first proposal, represented by chart 4, there is 2.14%
of choice in the selection of more than one access network,
which is considered a problem, since the handover has tohappen for the best network selected. The WLAN1 network
has obtained 0.48% of choice, averaging its delay network
parameter around 15400ms, jitter around 10ms and packet loss

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions
Transactions onon theAdvanced
Communications
Technology(TACT)
Vol.Vol.
2, Issue
3, May2013
ICACT
Advanced
Communications
Technology(TACT)
2, Issue
3, May2013

around 80%. The WLAN2 network has obtained 0.24% of
choice, averaging its delay network parameter around15400ms, jitter around 10ms and packet loss around 80%. The
3G1 network has obtained 20.95% of choice, averaging its
delay network parameter around 400ms, jitter around 10ms
and packet loss around 0%. And finally, the 3G2 network has
obtained 75.95% of choice, averaging its delay network
parameter around 400ms, jitter around 10ms and packet loss
around 2%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 44.05% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in the
WLAN1 and WLAN2 networks. The 3G1 network has
obtained 20.24% of choice, averaging its delay network
parameter around 400ms, jitter around 10ms and packet loss
around 14%. The 3G2 network has obtained 35.71% of choice,averaging its delay network parameter around 400ms, jitteraround 15ms and packet loss around 12%.
Result of the experiments with mobility
To obtain the results presented in this scenario a set of 358
collection iterations of the network variables was consideredduring the 50 drives/shifts from the access point to the edge
and vice versa.

251
251

the monetary cost parameter in the WLAN is 0 real and in the
3G network 89.90 reais, leading to a preference for the
WLAN network.
In the first proposal, chart 4, there is 14.80% of choice inthe selection of more than one access network, which isconsidered a problem, since the handover has to happen forthe best network selected. The WLAN network has obtained
72.91% of choice, averaging its delay network parameteraround 100ms, jitter around 10ms and packet loss around 8%.
The 3G network has got 12.29% of choice, averaging its delay
network parameter around 1700ms, jitter around 900ms andpacket loss around 1%.
The authors’ proposal in [7], represented by chart 6, has notselected any network in 44.05% of total iterations, because inaddition to the criterion of packet loss that can have a value of0, the monetary cost criterion has a value of 0 real in WLAN
network. The 3G network has obtained 62.29% of choice,averaging its delay network parameter around 10000ms, jitteraround 9500ms and packet loss around 25%.
VI.

CONCLUSIONS AND FUTURE WORKThe network selection is a very important step, or even themost important, in the handover process, since it will makeevery effort in implementing the handover so that the terminal
can connect to the selected network through decision-making
techniques that best meet the user’s access point preferences.
Therefore, we can see that the decision-making methods areuseful in sorting alternatives in order to achieve a goal, andtogether with an artificial intelligence technique, the result
becomes even more accurate.
As future work, we intend to integrate these proposals withhandover software by encompassing the whole process ofmobility management.
REFERENCES
[1]

[2]

[3]

[4]

Figure 17.

Traffic in the environment with mobility.

As it can be seen in chart 5, the WLAN network has
obtained 86.59% of choice, averaging its delay network
parameter around 1000ms, jitter around 1000ms and packet
loss around 12%. The 3G network has obtained 13.41% of
choice, averaging its delay network parameter around 900ms,
jitter around 10ms and packet loss around 0%. Although the
3G network parameters are smaller than the WLAN network,

[5]

[6]

[7]

[8]
[9]

Kassar, M., Kervella, B., and Pujolle, G. An overview of verticalhandover decision strategies in heterogeneous wireless networks.
Computer Communications, 31(10):2607–2620, 2008.
Singhrova, A. and Prakash, N. Adaptive Vertical Handoff Decision
Algorithm forWireless Heterogeneous Networks. In Proceedings of the2009 11th IEEE International Conference on High Performance
Computing and Communications, volume 0, pages 476–481. IEEE
Computer Society, 2009.
Ciccarese, G., De Blasi, M., Marra, P., Mighali, V., Palazzo, C.,
Patrono, L., and Stefanizzi, M. Vertical handover algorithm forheterogeneous wireless networks. In 2009 Fifth International Joint
Conference on INC, IMS and IDC, pages 1948–1954, 2009.
Stevens-Navarro, E. and Wong, V. Comparison between verticalhandoff decision algorithms for heterogeneous wireless networks. In
Vehicular Technology Conference, volume 2, pages 947–951, 2006.
Godor, G. and Detari, G. Novel network selection algorithm for various
wireless network interfaces. In Mobile and Wireless Communications
Summit, 2007. 16th IST, pages 1–5, 2007.
Alkhawlani, M. and Ayesh, A. Access network selection based on
fuzzy logic and genetic algorithms. Advances in Artificial Intelligence,8(1):1–12, 2008.
Wei, Y., Hu, Y., and Song, J. Network selection strategy inheterogeneous multi-access environment. The Journal of China
Universities of Posts and Telecommunications, 14:16–49, 2007.
Kurose, J. and Ross, K. Redes de Computadores e a Internet: uma
abordagem top-down. 5. ed. - São Paulo: Addison Wesley, 2010.
ITU-T, R. G. 114. One-way transmission time, 18, 2000.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on the
Advanced
Advanced
Communications
Communications
Technology
Technology(TACT)(TACT)
Vol.Vol.
2, Issue2, Issue
3, May3, May
2013
2013

[10]

[11]

[12]

Silva, D. J. Análise de qualidade de serviço em redes corporativas.
Dissertação de Mestrado, Instituto de Computação, Universidade
Estadual de Campinas (UNICAMP), 2004.
Tran, P. and Boukhatem, N. Comparison of madm decision algorithms
for interface selection in heterogeneous wireless networks. In Software,
Telecommunications and Computer Networks, 2008. SoftCOM 2008.
16th International Conference on, pages 119–124, 2008.
Radhika, K.; Reddy, A. V. Network selection in heterogeneous
wireless networks based on fuzzy multiple criteria decision making.
International Journal of Computer Applications, Foundation ofComputer Science (FCS), v. 22, n. 1, p. 7–10, 2011.

Vinícius Rios is assistant professor at the University ofTocantins (UNITINS) in distance education mode andface. He is Graduated in Information Systems from theLutheran University of Brazil (ULBRA) (2005),
specialization in Management and Consulting in
Telecommunications from the Brazilian Institute of
Postgraduate Studies and Extension (IBPEX) (2006)
and Master’s degree in Electrical Engineering from the
University of Brasilia (UNB) (2012). He has experiencein Information Systems with emphasis in Computer Networking, Operating
Systems, Wi-Fi Networks, Mobility Management and Artificial Intelligence.
Claudio Monteiro is a full professor at the Federal
Institute of Education, Science and Technology ofTocantins, Computing Science Department, Palmas –TO, ACM member and leader of the Research Group on
Networks Computers (GREDES - gredes.ifto.edu.br).
He graduated in data processing technology from the
University of Amazonia (1990) and Masters in
Computer Science from Federal University of Paraíba(1997) and Ph.D. in Electrical Engineering from the
University of Brasilia - UNB (2012). Actualy, has been developed aframework for reduce the latency of the handover between heterogeneous
wireless networks. In addition, a. He has experience in computer science, withemphasis on wireless networks, network protocols, QoS/QoE and operating
systems.
Vanice Cunha was born in Brasília, Federal District(Brazil) on august 24, 1984. She became Master degree
in Electrical Engineering from the University ofBrasilia in 2012. Her research project involved QoV(Quality of Video) and QoS (Quality of Service) in
wireless networks of 3rd Generation. Currently she is afemale teacher at the Instituto Federal de Brasilia.

Copyright ⓒ GiRI (Global IT Research Institute)252
252