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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 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 ⓒ 2016 GiRI (Global IT Research Institute)Volume. 5 Issue. 4

6J

1 An Adaptive Compression Algorithm for Energy-Efficient Wireless Sensor Networks

861

Beihua Ying**School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University,
Ningbo, Zhejiang, 315100, China

2 Automatic Zooming Mechanism for Capturing Clear Moving Object Image Using High869

Definition Fixed Camera
Hsien-Chou Liao*, Po-Yueh Chen**, Zi-Jun Lin**, Zi-Yi Lim*
*Department of Computer Science and Information Engineering, Chaoyang University of Technology,
168, Jifeng E. Rd., Wufeng District, Taichung, 41349 Taiwan, R. O. C.
**Department of Computer Science and Information Engineering, National Changhua University of
Education, No.1, Jinde Rd., Changhua City, Changhua County, 500 Taiwan, R. O. C.

3 Analysis of Very High Throughput (VHT) at MAC and PHY Layers under MIMO Channel in

877

IEEE 802.11ac WLAN
Gul Zameen Khan*, Ruben Gonzalez*, Eun-Chan Park**, Xin-Wen Wu**School of Information and Communication Technology, Griffith University, Australia**Dept. of Information and Communication Engineering, Dongguk University, South Korea

4 A Speculative Execution Strategy Based on Node Classification and Hierarchy Index

889

Mechanism for Heterogeneous Hadoop Systems
Qi Liu*, Weidong Cai*, Jian Shen*, Zhangjie Fu**, Xiaodong Liu***, Nigel Linge*****Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, Jiangsu,
210044, China** Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science andTechnology, Nanjing, Jiangsu, China***School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK****The University of Salford, Salford, Greater Manchester, M5 4WT, UK

5 A Comparison Study of the Quick Removing (QR) Approach (HSR Mode X) under Cut-

895

Through and Store and Forward Switching Modes
Saad Allawi NSAIF *, Semog KIM*, Jong Myung RHEE**Dept. of Information and Communications Engineering, Myongji University, Yongin-si, South Korea

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

861

An Adaptive Compression Algorithm forEnergy-Efficient Wireless Sensor Networks
Beihua Ying** School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo,
Zhejiang, 315100, China
yingbh@nit.zju.edu.cn

Abstract—Energy efficiency is one of the most importantdesign metrics for wireless sensor networks. As sensor data
always have redundancies, compression is introduces for energy
savings. However, different emphases on algorithm design
influence the operation effect of compression under various
applications and network environments. In order to improvethe energy utilization efficiency for the whole network, an
adaptive data compression is proposed in this paper, which
realizes a real-time adjustment of compression strategy. By
prediction and feature extraction of several relevant
parameters, the algorithm provides optimal execution strategies
for each sensor node in the network. The simulation results
show that, the proposed compression scheme enables all nodes
to complete data communication with near optimal energy
consumptions, and the maximum deviation against the ideal
condition is no more than 5%. Moreover, the algorithm caneffectively act on different data precision, transmit power andretransmission rate to meet the dynamic requirements of the
network with only a few costs introduced.
Keyword—wireless sensor networks, data compression,energy efficiency, adaptive mechanism

W

I. INTRODUCTION

IRELESS sensor network (WSN) is an emerging andpromising networking technique that has attracted
more and more attention in recent years. It facilitates humans
to sense and monitor the region of interest, and is widely usedin many application fields such as agricultural planting,medical care, smart homes, ecological monitoring and so on[1]–[3].
Since the power of sensor nodes supplied by batteries ishigh-limited and not easy to complement commonly, themost important issue in WSNs is prolonging network lifetime
by energy-efficient strategy. Data compression is introduced
into WSNs due to its ability to reduce the data amount by
exploiting the redundancy resided in sensing data [4].

———————————————————————
Manuscript received December 31, 2015. This work was sponsored by the
Nature Science Foundation of Zhejiang Province (Project No. LQ13F010005)
and Ningbo Natural Science Foundation (Project No. 2011A610185). The
research was supported by Ningbo Sc. & Tech. plan project (2014B82015).
The author would like to appreciate the valuable datasets from the TAO
Project Office of NOAA/PMEL, the ShakeMap Working Group, and the
NWISWeb Support Team.
Beihua Ying is with the School of Information Science and Engineering,
Ningbo Institute of Technology, Zhejiang University, Ningbo, Zhejiang,
315100, China (corresponding author to provide phone: 86-574-88130020;
fax: 86-574-88229505; e-mail: yingbh@ nit.zju.edu.cn).

Meeting the application requirement as a prerequisite
condition, such kind of in-network information processing isstrongly recommended to decrease energy consumptions indata communication and prolong the lifetime of WSNs.
However, from the implementation perspective andreevaluation, data compression may not achieve total energy
savings in any case [5]. On one hand, sensor nodes aredensely deployed which permits a low transmit power during
data collection. On the other hand, if high-resolution data are
required by applications, it is hard to get a satisfied
compression ratio. Thus, in-network data compression may
increase rather than decrease the total energy consumptions
when the savings in communication cannot compensate theadditional costs in data processing. Thus, a compression
arbitration system has been proposed in [6] by which
compression algorithm in time domain is examined carefully
to assess its energy efficiency before compression. The casein which compression is unnecessary will be avoided andsensor node will transmit raw data directly instead of thecompressed ones.
Based on our previous works, it is reasonable to believethat data compression in WSNs should be carefully carriedout. The variety of application scenarios will affect its
efficiency profoundly. In order to obtain more energy savings
for whole networks, more researches need to be done on datacompression. Among them, the adaptability of algorithm is
one of the most important design considerations, because it isan effective way to deal with the variability of different
situations. In this paper, we pay greater attention to thisadaptability and propose an adaptive algorithm that enables areal-time adjustment of compression strategy to increase theenergy efficiency of the whole network.
The remainder of this paper is structured as follows: in
Section 2, we discuss the related work on the adaptability ofcompression algorithms and the motivation for our adaptive
data compression. The proposed system and its mathematicalanalysis are presented in Section 3. The process of building
prediction models is detailed described in Section 4, andsubsequently, Section 5 presents the results of algorithm
simulation and implementation using an environmental
application. Finally, Section 6 concludes the paper.
II. RELATED WORK AND MOTIVATION
Most existing works enhance the adaptability ofcompression algorithm in WSNs by using tunable dataprocessing methods. In these researches, data precision is

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

restricted as an error tolerance, which is specified by eachapplication. The compression can be lossless or lossy for
different needs, such as wavelet transformation [7],predictive compression [8], data fitting [9], discrete cosine
transform (DCT) [10], compressive sensing [11]–[12], fuzzy
transform [13], and so on. Moreover, some losslesscompression methods are improved to adapt to WSN
applications. For example, an adaptive Huffman code is
proposed in [14] that can achieve tunable compression withrequired accuracy.
Another meaningful work is about the energy consumption
in compression that has received more and more attentions
instead of compression ratio. Different from otherapplications, energy saving is the ultimate design purpose of
WSNs in most cases. As a result, compression cannot merely
pursue high compression ratio, but take energy costs into fullconsideration [15]–[16]. It also reflects the adapt degree to
WSN applications. Several studies have begun to focus on
this. In order to reduce the total energy consumptions, a
trade-off between computation during data compression andcommunication energy is made carefully. [15] constructs
several energy models of computation and communication
for mathematical analysis, but gzip is adopted which cannot
be implemented in the resource constrained sensor nodes. In[17], error radius of prediction algorithm are tuned for
optimizing the desired tradeoffs between data quality andenergy saved, and a similar work is proposed in [18] based on
the discrete cosine transform.
Once the energy consumption of compression isconsidered, the practicality of several algorithms will begreatly reduced, especially for the one with highcomputational complexity. What is more, compression itself
cannot get energy saving in some situations. According tothis point, we introduce a novel pre-judgment mechanism todata compression. Once system estimates that compression
cannot save energy, sensor node will send raw data directly.
A similar work is presented in [19], which decides whether
compressing or not based on time delay.
As shown in our previous works, using “compression
pre-judgment” can remarkably make sensor nodes moreenergy efficiency in most cases. It also makes ourcompression system more suitable for WSN applications [20].
Nevertheless, this system is still insufficient in two aspects.
Firstly, the compression arbitration in [20] focuses on asingle node, but not a whole network. Decisions about
whether or not to compress data are made at the node-level,
which are not involved in the ubiquitous multi-hop
communication mode. As a result, data receiving is neglected
when the total energy costs are calculated and subsequently
compared. Since data reduction not only affects the energy
dissipation in sending, but also in receiving, it is notcomprehensive to estimate the energy-saving benefits ofcompression algorithms without data reception at the
network-level.
More importantly, the compression arbitration is proposed
under the assumption that only one compression algorithm isadopted in the whole network, which would be unable to
make the best use of data compression. According to theevaluation results of compression algorithms presented in [5],different algorithms show different properties though they862

are all aimed to be used in WSNs. Take the existingalgorithms for example: LAA [21] and predictive coding [22]represent one kind of algorithms that have low complexity,
whereas their compression effects are not prominent.
Conversely, PMC-MR [23] and LTC [9] tend to achieve
better compression ratio at the costs of more energy losses incalculation. It is clear that, in those algorithms, compression
effect and algorithm complexity are two major considerations,but have different emphasis during the design. This isprobably the main reason causing the differences between
compression algorithms. As a result, when various settings ofapplications are selected such as data type, precision
requirement, communication quality, node location, and soon, energy-saving benefits obtained by compression
algorithms are distinct.
In summary, from the point of view of the entire network,
it is reasonable to believe that single compression algorithm
cannot achieve better energy efficiency. An adaptive datacompression with tunable precision is urgently needed which
is able to determine the optimal execution strategy according
to different application requirements and network settings.
Benefit from the adaptability, compression algorithms with
distinct characteristics will be adopted to maximize energy
savings at the network-level.
III. ADAPTIVE COMPRESSION SYSTEM
A. System Description

Applications

Data
Accumulation
Data Change

Precision

Precision

Prediction
ModelsBuilding

FeatureExtraction

Parameters
Adjustment

Dynamic Adaptation
New Sample

Comparison &Strategy Selection

Misprediction

RandomTesting
New Sample

Adaptive
Compression
SystemNode position/
Communication
Power/
ResultRetransmission
Rate

WSN Platform
Fig. 1. Framework of the adaptive compression system

In order to make the best use of different compression
algorithms for the total energy savings, a lightweight strategy
selection mechanism is proposed in this paper. Before
compressing, an optimal executive strategy for each sensor
node is derived by predicting and feature extracting of all
related parameters, which makes the whole network achieveenergy efficiency to the greatest degree. The adaptive
compression system framework is shown in Fig. 1.
According to the function of each module, the wholeprocedure can be divided into four steps:
1. Prediction modeling

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

Before deciding on the optimal strategy, two models are
established to predict the compression ratio and compression
time on-line. Data compression under various algorithms is
performed during this initial stage. Compression ratio andexecution time for each algorithm based on different datasets
and application requirements are recorded. These data are thebasis for the prediction model building. Since this is done
on-line, only a few samples are used to save energy.
2. Feature extraction
When starting the decision-making process, three types offeature need to be extracted, including raw data, algorithm
and network. Among them, data type, error tolerance andalgorithm type, which represent the features of raw data andalgorithm respectively, are used to calculate the compression
ratio and the required time. Moreover, the useful information
extracted from the network include node position,communication power and retransmission rate.
3. Compression evaluation & strategy selection
Once the required parameters are ready, system canevaluate all kinds of alternative strategies by calculating thetotal energy consumptions including data compression anddata transmission from each source node to sink. The optimal
executive strategy can be obtained based on the comparison
results. The selected strategy leading to the lowest energy
loss may be an alternative compression algorithm, or may not
perform any compression.
4. Model modification
Since compression ratio and execution time will greatly
influence the energy consumptions in communication andcalculation respectively, ensuring the predict precision caneffectively improve the accuracy of the strategy selection. In
view of this, model modification is used to guarantee theaccuracy of the two predictions. Raw datasets are randomly
selected to verify the predicted results. The model parameters
will be modified if the prediction accuracy is not satisfied.
B. Mathematical Analysis
In our system, the optimal strategy choosing is based on
the total energy costs of each sensor node in data
communication. Correct energy calculation is very important,because it will directly affect the judgment of the strategy.
Thus, the energy losses in both communication andcalculation are considered here.

Ecomp = PMCU * L *T MCU(e) + PTX(d)* L * CR(e)* Ttran * (1 + γ ),h = 1 (1)hEcomp = PMCU * L *T MCU(e) + ∑PTX(di )* L * CR(e)* Ttran *(1 + γi )
i =1

(2)

h −1

+ ∑PRX * L * CR(e)* Ttran *(1 + γi ),h > 1
i =1

Euncomp = PTX(d)* L * Ttran * (1 + γ),h = 1(3)

hEuncomp = ∑PTX(di )* L * Ttran * (1 + γi )
i=1

(4)

h−1

+ ∑PRX * L * Ttran * (1 + γi ),h > 1
i=1

The proposed system is analyzed where Euncomp denotes theenergy consumption for transmitting the raw data directlyand Ecomp denotes the total energy costs for compressing the863same data and then transmitting the compressed one. It isclear that Euncomp only involve the communication costs,
whereas Ecomp is related to the compression costs in MCU andthe communication costs in RF module. Considering that thecost in RF wake-up is the same, regardless of whether
compression is executed, it will not affect the final decision
results. On the other hand, since the length of the frame headis much smaller than the data part, it also can be ignored. As aresult, Ecomp and Euncomp can be simplified as (1) to (4).
L denotes the original data size and CR describes thecompression ratio which is a function of the error bound e.
Ttran is the time cost for transmitting one byte of data and
TMCU is the time overhead for compressing one byte, which isstrongly dependent on e. The transmit power PTX is closely
related to the communication distance d, while the MCU
power PMCU and the received power PRX are approximately
constant when the related modules work in the active mode.
It is clear that CR (e) and TMCU (e) are obtained by the two
prediction models. As the sink is a super node with unlimited
energy, there is no need to consider receiving energy
consumption for its neighbors whose hop count h is equal to
1. The data retransmission rate γi reflects the quality ofcommunication channel in the multi-hop routing. The worse
the communication channel becomes, the higher the data
retransmission rate is.
(2) shows that for different compression methods, thecharacteristics of each algorithm determine the energy saving
benefits of sensor nodes. For the compression with low
complexity, TMCU will be very small, which leads to a lower
computational energy costs. If sensor nodes are close to thesink (h is small), computational costs will account for a large
proportion, especially when PTX is turned down for the
short-range communication. In this regard, algorithm withlow complexity will bring a huge advantage. With theincrease of h, the proportion of computational costs will bedecreased, while the corresponding communication costs is
obviously increased. At this point, the influence ofcompression ratio on communication energy consumptions
will directly act on the total energy costs. Therefore,
compression algorithm, which focuses on the compression
effect gradually, shows its energy saving advantages with theincrease of node hops.
IV. ADAPTIVE MECHANISM IN PREDICTION MODELING
Accurate prediction of compression ratio and execution
time is an important part of ensuring the correct selection ofstrategy. Thus, an adaptive mechanism is introduced into our
proposed system. This mechanism requires fewer samples tobuild the prediction models, which makes on-line modelingpossible. Even if the initial models are somewhat inaccurate,they will be adjusted adaptively to the best results. The
adaptive mechanism is illustrated in Table I.
The beginning of the adaptive mechanism uses an initialsample step with a given range. Once a new sample is readyfor verification, the compression ratio and execution time areboth measured. The comparison then determines whether thedifference between the predicted value and the real one
exceeds a preset error bound. If the prediction error is large,
the information including the compression ratio and timeoverhead are recorded for the new model. Two prediction
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

models are rebuilt when the sample step (step) reaches theminimum value or the sample step is reduced continuously.
Alternatively, the sample step is increased until the maximum
value.
TABLE IPROCESS OF THE ADAPTIVE MECHANISM
Algorithm Adaptive mechanism for prediction modeling
1: set an initial sample step (step)
2: set an allowable step range [stepmin, stepmax]
3: if (sample is awaiting verification) then
4: measure CR and TMCU
5: if ((|prediction error of CR|>=error bound)or (|prediction error of TMCU |>=error bound)) then
6: record compression ratio and time overhead for the sample
7: if (step == stepmin) then8:
rebuild the models with the recorded samples
9:
reset step
10: else
11:
decrease step
12: end if
13: else
14: increase step until stepmax
15: end if
16: end if

V. PERFORMANCE EVALUATION
A. Experimental setting
In order to verify the energy saving effect of the proposedsystem, the experimental settings are confirmed, such as
original datasets, network topology, alternative strategies andexperimental platform.
1. Original datasets
Datasets from the Tropical Atmosphere Ocean Project(TAO) [24], which collects real-time oceanographic andmeteorological data in the Pacific Ocean, are selected for theexperiments. Among the various types of datasets, airtemperature, sea level pressure and relative humidity arechosen because of their very different data characteristics.
2. Network topology
Grid-based network topology is adopted with nodes even
distribution, as show in Fig. 2.

864

The network size is 25*25, and the total number of sensor
nodes is 625. In these nodes, sink node is located in the centerof the whole network, which is denoted by a large solid circle.
The rest of the nodes are homogeneous. Each source node isrouted to the sink using SPT (Shortest Path Tree) [25].
Because of the even distribution, sensor nodes are assumed totransmit data with a uniform RF power level andretransmission rate.
3. Alternative strategies
Based on the evaluation results of data compression in [5],the tests select four different algorithms with better
performances, as shown in Table II. Combined with theimplementation of not compressed, sensor nodes can choose
the best on from these five alternative strategies.
TABLE IICOMPRESSION ALGORITHM IN THE TEST
Algorithm

Type

RemarkSingle Moving Average
LAA
PMC-MR
LTC

Predictive compression
Linear regression
Linear regression
Linear regression

N=3
——
——
——

4. Experimental platform
We choose MicaZ nodes as the test platform for our
experiments. They are commonly used in WSNs. The
processor is an 8-bit Atmel ATmega128L microcontroller,
and the processor speed is fixed at 8MHz. As the results
shown in [26], supply current of processor is nearly constantin active mode. Therefore, we consider PMCU as a fixed value
in the test. TMCU is obtained by ATMEL AVR Studio [27]. In
MicaZ node, a CC2420 unit is responsible forcommunicating with other nodes. It is a single-chip RFtransceiver that operates at 2.4 GHz. According to [28], thedata transmission rate of a MicaZ node is up to 250 kbps.
Besides, transmit power is configurable; in that case, CC2420
can be powered down by setting control register whencommunication distance is short. In the test, we assume thatsource nodes send information up to 100 m when the transmit
power level is set to 31. Experimental parameters mentioned
above are listed in Table III.
TABLE IIIVALUES OF THE EXPERIMENTAL PARAMETERS
SymbolFig. 2. Network topology in the test

Value

Unit

Remark

d

5-100

m

Outdoor monitoring

PMCU

26.4

mW

8mA current draw

PTX

57.42

mW

PA_Level=31

PRX

62.04

mW

18.8mA current draw

Ttran

32

µs

250kbps data rate

5. Relevant assumption
Several reasonable hypotheses are given in our
experiments. First, when source nodes, which arehomogeneous, establish a communication route with the sinkusing SPT, they are able to get the communication hops h andthe transmit power PTX. Second, accuracy requirement of thedata is given by WSN applications, and this information issent to the whole network by the sink. Third, the test network
is uniform distribution, so the node density determines the RF
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016transmit power required by the single hop communication.
With the failure of the nodes, the density will be reduced, andthe RF power and data retransmission rate will be increased
accordingly.
B. Impact of the characteristics in data and network
In order to explore the impact of the characteristics inoriginal data and network on energy saving strategy selection,
we choose data accuracy, transmit power level and data
retransmission rate as test variables for the deeply analysis.
Taking air temperature as an example, Fig. 3 shows theresults of selecting the best compression implementation
strategy by our adaptive compression method, which are set
by the different data accuracy requirements, the transmit
power level and the data retransmission rate. Among them,taking into account the actual situations of WSN applications,the variation grades of data accuracy, transmission power andretransmission rate are set to 1-12, 3-31, 10%-150%,
respectively.
Comparing Fig. 3(a) and Fig. 3(b) reflects the impact ofthe data accuracy on strategy selection. It can be seen that
there are three alternative strategies in the results: PMC-MR,
LTC and no compression. When the accuracy level is set
higher (the numerical value is small), source nodes near thesink are more inclined to choose no compression, which is ingood agreement with the conclusion that compression may
not achieve total energy savings in any case. At that time, due
to the compression algorithms cannot achieve satisfactory
compression effect, the energy savings in communication
may not compensate for costs in calculation. Therefore, fromthe point of view of total energy costs in nodes, transmitting
raw data without compression will obtain lower energy losses.
With the increase of the distance between source nodes andthe sink, the benefit of compression is also increasing. As aresult, the nodes tend to select the algorithm (LTC in Fig. 3)
which has a better compression effect.
Reducing data accuracy requirements, the effect obtainedby data compression is gradually clear. Sensor nodes mostly
choose to compress at first, and send the compressed data
instead of the original ones. Furthermore, during theselection of different algorithms, nodes near the sink will
make a trade-off between computational complexity andcompression effect, whereas nodes away from the sink will
still consider the compression effect as a primary goal.
Therefore, from the results of Fig. 3(b), although LTC canachieve the minimum compression ratio among the fourcompression algorithms, source nodes near the sink still
select PMC-MR as the best energy saving strategy in virtue
of its high computational costs.
Comparing Fig. 3(a) and Fig. 3(c) reflects the impact of thetransmission power level on strategy selection. The lower thetransmission power level is, the higher the probability thatcompression wastes the total energy costs. As a result, there
are over 50% of the nodes do not compress the raw data in
Fig. 3(a). With the increase of the transmission power, theadvantage of compression in energy savings becomes more
obvious. Thus, more nodes choose to perform thecompression operation, as shown in Fig. 3(c).
The impact of the retransmission rate on strategy selection
can be seen in Fig. 3(b) and Fig. 3(d). The lower the data

865

retransmission rate, the more likely that node that is close to
the sink selects no compression. With the deterioration ofchannel quality, retransmission rate and communicationenergy costs increase correspondingly, and nodes will
gradually tend to select the algorithm which has goodcompression effect.
yp

p

(a)

(b)

(c)

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016(d)□: PMC-MR +: LTC ◊: No compression
Fig. 3. Best compression strategy in different situations: (a) data accuracy:
level 1; transmission power: level 3; retransmission rate: 10%; (b) data
accuracy: level 12; transmission power: level 3; retransmission rate: 10%; (c)data accuracy: level 1; transmission power: level 23; retransmission rate:
10%; (d) data accuracy: level 1; transmission power: level 3; retransmission
rate: 100%.

C. Energy-saving effect of the adaptive compression
In order to evaluate the energy-saving effect of the
proposed compression algorithm quantitatively, the totalenergy consumptions of the sensor nodes are measured in thefollowing two cases. One is that all nodes use a single datacompression algorithm or do not perform any compression,and the other is that each node adopts the adaptive
compression to choose the best one from the five alternative
strategies.
In fact, if the nodes use the optimal strategy in each datatransfer, it can be considered as the best situation of energy
costs. Taking this ideal case as a reference, the statisticalresults of the total energy costs in different cases arecompared in the form of maximum deviation (expressed as apercentage), as shown in Table IV. In this table, E, RF and γ
represent the level of data accuracy, transmission power andretransmission rate, respectively. That is to say,
E2_RF7_γ0.1 denotes that the data accuracy and thetransmission power are in level 2 and level 7, and the866

retransmission rate is 10%.
From the results of Table IV, it is clear that the proposedcompression method can make the whole sensor nodes
complete data transfer tasks with nearly optimal energy
consumptions, no matter how the precision requirement,
transmit power and retransmission rate change. It should
mainly owe to the adaptive mechanism introduced into thedata compression that uses strategy selection for energy
efficiency. When the accuracy requirement is high, like E2,
data compression cannot obtain a satisfied compression ratio.
Instead of saving energy costs, compression increases the
additional computational losses. Thus, the total energy
consumption has a greater deviation from the optimal once
complex compression is executed. At that time, nocompression or compression algorithm with lower
complexity will make the energy consumption approximate
to the optimal. With the decrease of the accuracy requirement,
the compression method gradually reveals the advantages ofenergy savings. More and more sensor nodes adopt
compression methods, especially the one with goodcompression effect and low implementation complexity, andno compression gradually increases the degree of deviationfrom the ideal situation.
Due to the limitation of prediction accuracy incompression ratio and execution time, erroneous judgment
will inevitably occur during the process of compression
evaluation and the following strategy selection. It also
probably leads to that a single compression algorithm is moreclose to the optimal energy consumption than the proposed
one in some cases, for example E10_RF7_γ0.1. But ingeneral, the adaptive compression method can provide a
relatively accurate energy efficient strategy, so that sensor
nodes can complete data transmission under an approximate
optimal energy costs, and the highest degree of deviation is
no more than 5%.
Certainly, the introduction of adaptive mechanism will
also bring two aspects of the costs, namely the overhead ofcomputation and storage. By simulation, the time cost of
executing the proposed method once is about 0.4ms (about3000 clock cycles), which is approximately equal to the timeoverhead of using the LAA algorithm to compress 50 bytes

TABLE IVMAXIMUM DEVIATION OF THE TOTAL ENERGY COSTS IN DIFFERENT SITUATIONSParameters

Forecast

LAA

PMC-MRLTC

No comp.

Adaptive comp.

E2_RF7_γ0.1
E2_RF7_γ1.0
E2_RF15_γ0.1
E2_RF15_γ1.0
E2_RF23_γ0.1
E2_RF23_γ1.0
E5_RF7_γ0.1
E5_RF7_γ1.0
E5_RF15_γ0.1
E5_RF15_γ1.0
E5_RF23_γ0.1
E5_RF23_γ1.0
E10_RF7_γ0.1
E10_RF7_γ1.0
E10_RF15_γ0.1
E10_RF15_γ1.0
E10_RF23_γ0.1
E10_RF23_γ1.0

59.84%
32.07%
46.78%
25.69%
38.07%
21.57%
52.98%
34.34%
43.44%
32.76%
37.61%
32.77%
64.93%
65.65%
65.04%
65.59%
65.14%
65.78%

31.99%
17.66%
25.17%
14.64%
20.66%
13.68%
40.97%
41.80%
41.13%
41.89%
41.27%
41.97%
97.17%
99.70%
97.57%
99.50%
97.93%
99.85%

36.17%
15.69%
26.51%
11.08%
20.07%
8.15%
15.70%
5.89%
9.29%
5.86%
5.69%
5.88%
3.37%
3.79%
3.43%
3.75%
3.49%
3.81%

43.17%
19.32%
31.94%
13.86%
24.45%
10.36%
32.13%
12.47%
22.33%
9.84%
16.16%
8.41%
40.58%
24.36%
36.29%
26.82%
32.92%
24.44%

12.99%
13.89%
13.17%
13.99%
13.32%
14.07%
48.00%
49.56%
48.30%
49.73%
48.56%
49.88%
133.31%137.68%134.01%137.34%134.62%137.77%

4.77%
3.07%
4.00%
3.08%
3.54%
3.08%
4.96%
3.83%
3.87%
3.76%
3.64%
3.78%
3.55%
3.79%
3.43%
3.75%
3.49%
3.81%

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016of raw data. Since the execution frequency of the method
depends on the change frequency of error tolerance, transmit
power and retransmit rate, it can be considered that thecomputational energy consumptions of the adaptive
mechanism is acceptable.
On the other hand, the implementation of the adaptive
mechanism is based on a set of alternative strategies.
Therefore, it needs to embed all kinds of alternative strategies
into sensor nodes. Through the realization of eachcompression algorithm, the storage overhead is obtained, as
shown in Table V. Compared to the overhead of dataacquisition and communication (about 12540 bytes), thestorage cost of the adaptive mechanism is also acceptable.
TABLE V
STORAGE OVERHEAD OF COMPRESSION ALGORITHMS
Algorithm

Storage cost

Unit

Single Moving Average
LAA
PMC-MR
LTC

902
356
690
2490

byte
byte
byte
byte

VI. CONCLUSION
Different emphases on algorithm design will influence theenergy efficiency of data compression under various
applications and network environments. In order to raise theenergy efficiency of data compression for the whole network,
this paper presents an adaptive data compression with tunableprecision, which enables a real-time adjustment ofcompression strategy.
By the prediction and feature extraction of the relevant
parameters, the method can provide the optimal energy
saving strategy for different sensor nodes. Experimental
results show that, by means of the network level
energy-saving improvement, all nodes can complete the datatransfer task with near optimal energy consumptions, and thedeviation degree is no more than 5%. Furthermore, themethod only introduces low costs in computation and storage,
and can effectively act on different error tolerance, transmit
power and retransmission rate to meet the dynamic
requirements of the network.
ACKNOWLEDGMENT
In this paper, the research is sponsored by the Nature
Science Foundation of Zhejiang Province (Project No.
LQ13F010005) and Ningbo Natural Science Foundation(Project No. 2011A610185). The work is supported by
Ningbo Sc. & Tech. plan project (2014B82015). The author
would like to appreciate the valuable datasets from the TAO
Project Office of NOAA/PMEL, the ShakeMap Working
Group, and the NWISWeb Support Team.
REFERENCES[1][2]

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci,“Wireless sensor networks: a survey,” Computer Networks, vol.38,
pp.393–422, 2002.
K. Romer, F. Mattern, “The design space of wireless sensor networks,”
IEEE Wireless Communications, vol. 11, no. 6, pp.54 – 61, 2004.

867

[3]

J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network
survey,” Computer Networks the International Journal of Computer &Telecommunications Networking, vol. 52, no. 12, pp. 2292-2330, 2008.
[4] A. Mainwaring, D.Culler, J. Polastre, R. Szewczyk, and J. Anderson,“Wireless sensor networks for habitat monitoring,” in Proceedings ofthe First Acm International Workshop on Wireless Sensor Networks &Applications, Atlanta, Georgia, USA, 2002, pp. 88-97.
[5] B. Ying, Y. Liu, H. Yang, and H. Wang, “Evaluation of tunable datacompression in energy-aware wireless sensor networks,” Sensors, vol.
10, no. 4, pp. 3195-217, 2010.
[6] B. Ying, W. Liu, Y. Liu, H. Yang, and H. Wang, “Energy-efficientnode-level compression arbitration for wireless sensor networks,” inProceedings of the 11th International Conference on Advanced
Communication Technology, Phoenix Park, Korea, 2009, pp. 564-568.
[7] Z. J. Ming, L. Y. Ping, Z. S. Wang, “Haar wavelet data compression
algorithm with error bound for wireless sensor networks,” Journal of
Software, vol. 21, no. 6, pp. 1364-1377.
[8] Y. Liang, “Efficient temporal compression in wireless sensor
networks,” in IEEE the 36th Conference on Local Computer Networks(LCN), Bonn, Germany, 2011, pp.466-474.
[9] T. Schoellhammer, E. Osterweil, B. Greenstein, M. Wimbrow, D.
Estrin, “Lightweight Temporal Compression of Microclimate
Datasets,” in Proceedings of the 29th Annual IEEE International
Conference on Local Computer Networks, Tampa, Florida, USA, 2004,
pp.516-524.
[10] Y. I. Kefu, J. Wan, T. Bao, L. Yao, “A DCT regularized matrix
completion algorithm for energy efficient data gathering in wirelesssensor networks,” IEEE Communications Letter, vol. 19, pp. 54-57,
2015.
[11] R. Hussein, A. Mohamed, M. Alghoniemy, “Adaptive compression andoptimization for real-time energy-efficient wireless EEG monitoring
systems,” in The 2013 Biomedical Engineering International
Conference (BMEiCON-2013), Krabi, Thailand, 2013, pp.1-5.
[12] X. Xing, D. Xie, G. Wang, “Data Gathering and Processing forLarge-Scale Wireless Sensor Networks,” in IEEE 9th International
Conference on Mobile Ad-hoc and Sensor Networks, Dalian, China,
2013, pp.354-358.
[13] M. Abdelaal, O. Theel, “An Efficient and Adaptive Data Compression
Technique for Energy Conservation in Wireless Sensor Networks,”
in IEEE Conference on Wireless Sensor (ICWISE), Kuching, Sarawak,
Malaysia, 2013, pp.124-129.
[14] Y. Mo, Y. Qiu, J. Liu, Y. Ling, “A Data Compression Algorithm Basedon Adaptive Huffman Code for Wireless Sensor Networks,” in The 4thInternational Conference on Intelligent Computation Technology andAutomation, Shenzhen, China, 2011, pp.3-6.
[15] N. Arastouie, M. Sabaei, H. S. Shahreza, “Adaptive Approach for
Reducing Energy Consumption, Considering Nodes' Mission in
Wireless Sensor Networks,” in The 2nd International Conference on
Communication Software and Networks, Singapore, 2010, pp.152 156.
[16] S. Eswaran, J. Edwards, A. Misra, T. Porta, “Adaptive In-Network
Processing for Bandwidth and Energy Constrained Mission-Oriented
Multihop Wireless Networks,” IEEE Transaction on Mobile
Computing, vol. 11, no. 9, pp. 1484-1498, 2012.
[17] M. I. Mohamed, W. Y. Wu, M. Moniri, “Adaptive data compression forenergy harvesting wireless sensor nodes,” in The 10th IEEE
International Conference on Networking, Sensing and Control(ICNSC), Evry, France, 2013, pp.633-638.
[18] K. Hua, H. Wang, W. Wang, S. Wu, “Adaptive Data Compression in
Wireless Body Sensor Networks,” in IEEE the 13th International
Conference on Computational Science and Engineering, Hong Kong,
China, 2010, pp.1-5.
[19] X. Deng, Y. Yang, “Online adaptive compression in delay sensitive
wireless sensor networks,” IEEE Transactions on Computer, vol. 61,
no. 10, pp.1429-1442, 2012.
[20] B. Ying, Y. Liu, H. Wang, “Improved adaptive compression arbitration
system for wireless sensor networks,” Tsinghua Science andTechnology, vol. 15, no. 2, pp. 202-208, 2010.
[21] L. Ying, S.W. Loke, M.V. Ramakrishna, “Energy-saving dataapproximation for data and queries in sensor networks,” in The 6thInternational Conference on ITS Telecommunications, Chengdu,
China, 2006, pp. 782-785.
[22] C. Hui, L. Cui, “Forecast-based temporal data aggregation in wirelesssensor networks,” Computer Engineering and Applications, vol. 43, no.
21, pp. 121-125, 2007.
[23] I. Lazaridis, S. Mehrotra, “Capturing sensor-generated time series withquality guarantees,” in IEEE International Conference on Data
Engineering, Bangalore, India, 2003, pp. 429-440.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016868[24] Tropical Atmosphere Ocean Project [Online]. Available:
http://www.pmel.noaa.gov/tao/index/shtml[25] T. S. Moh, M. Dumont, T. S. Moh, “Evaluation of dynamic tree-based
data gathering algorithms for wireless sensor networks,” in IEEE
International Symposium on Signal Processing and Information
Technology, Athens, Greece, 2005, pp.170-175.
[26] M. Kramer, A. Geraldy, “Energy Measurements for Micaz Node,”Technical Report, Technical University Kaisers Lautem, GI/ITG KuVS,
2006, pp. 1-7.
[27] ATMEL AVR Studio [Online]. Available:
http://www.atmel.com/dyn/Products/tools_card.asp?tool_id=2725[28] CC2420 Data Sheet [Online]. Available:
http://inst.eecs.berkeley.edu/~cs150/Documents/CC2420.pdf
Beihua Ying received her Ph.D. degree from
Tsinghua University, Beijing, China, in 2010 and the
BS degree from Xidian University, Xi’an, China, in
2004, both in Electronic Engineering. She is currently
a lecturer with the school of Information Science andEngineering, Ningbo Institute of Technology,
Zhejiang University. Her research interests include
design and analysis of energy-efficient data process
for wireless sensor networks, data gathering androuting protocol in wireless ad hoc and sensor
networks, and optimization problems.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

869

Automatic Zooming Mechanism for Capturing
Clear Moving Object Image Using High Definition
Fixed Camera
Hsien-Chou Liao*, Po-Yueh Chen**, Zi-Jun Lin**, Zi-Yi Lim**

Department of Computer Science and Information Engineering, Chaoyang University of Technology,
168, Jifeng E. Rd., Wufeng District, Taichung, 41349 Taiwan, R. O. C.
**
Department of Computer Science and Information Engineering, National Changhua University of Education,
No.1, Jinde Rd., Changhua City, Changhua County, 500 Taiwan, R. O. C.
seafoodliao@gmail.com, pychen@cc.ncue.edu.tw, beiwue2006@gmail.com, joslimzy@gmail.com

Abstract—High definition (HD) camera is widely used insurveillance systems. An HD camera with optical zoom isuseful for monitoring a large area. However, it is inconvenient
for a user to manually control the optical zoom for a long time.
To exploit the functionality and extend the application
domains of a HD camera, the zooming should be controlled
automatically. Therefore, an automatic zooming mechanism is
proposed in this paper. When the number of an object is small
in the field of view (FOV) of the camera and an object ismoving through the FOV, the zoom is controlled for capturing
the object as clear as possible. A clear object image is usefulfor related image-based services, such as face recognition. In
order to achieve the above goal, a Gaussian Mixture Model(GMM), temporal image differencing, a CamShift tracking
method, and a Kalman filter are utilized for object detection
and tracking. Then, an adaptive neuro-fuzzy inference system(ANFIS) is used to learn and determine a suitable value foradjusting the zoom. According to the experimental study ofthe prototype, the results show that the proposed mechanism
is useful to capture the clear images of moving objects in apractical environment. A face detection algorithm is also used
to demonstrate the feasibility of the captured clear images.
Keywords—Object
tracking,
SurveillanceIntelligent video surveillance, Neural network

system,I. INTRODUCTION
urveillance systems are widely adopted for security andsafety consideration. In most of cases, surveillancecameras are connected to the internet and called IP (Internet
Protocol), or network cameras. The development of
hardware technology also increases the image quality of acamera to high definition (HD). An HD quality image isuseful for many related image-based functions, such as facerecognition. There are mainly two types of camera

S———————————————————————
Manuscript received March 7, 2016. This work is supported by
Ministry of Science and Technology, Taiwan, R.O.C., under Grant
MOST 103-2221-E-324-016.
Hsien-Chou Liao is with the Department and Graduate Institute ofComputer Science and Information Engineering, Chaoyang Universityof Technology, Taichung, Taiwan, R.O.C. Post code: 413.
(Corresponding author, phone: +886-4-2332-3000~4211; fax:
+886-4-2374-2375; e-mail: seafoodliao@gmail.com).
Zi-Yi Lim is with Chaoyang University of Technology, Taichung,Taiwan, R.O.C. Post code: 413. (phone: +886-4-2332-3000~7713; fax:
+886-4-2374-2375; e-mail: joslimzy@gmail.com).
Po-Yueh Chen and Zi-Jun Lin are with the Department andGraduate Institute of Computer Science and Information Engineering,
National Changhua University of Education, Taiwan, R.O.C. Post code:
500. (e-mail: pychen@cc.ncue.edu.tw, beiwue2006@gmail.com).

commonly used in a surveillance system, fixed cameras andactive cameras. A fixed camera can only be used to monitor
a fixed area. On the other hand, an active camera isequipped for pan/tilt/zoom operations. Such a camera canbe used to monitor a large area. However, an active camera
is inconvenient to be manually controlled for a long time
period. These two types of cameras are shown in Fig. 1. A
control mechanism must be designed for specificapplications, e.g., moving object tracking, smoke/flame
detection.

(a)(b)
Fig. 1. Two types of cameras (a) fixed camera (b) active camera

In this study, an automatic zooming mechanism is
proposed for tracking a moving person and capturing clear
images, using a HD fixed camera with an optical zooming
lens. The mechanism enables the facility to capture a clear
image of a moving person during the tracking period. An
example shown in Fig. 2 is used to illustrate theimplemented system. A fixed HD camera is used to monitor
a square at a distance. The distance is about 100 meters in
the experimental site. When a person is moving through thesquare, the person is blurred and difficult to identify fromhis face if the zoom is also fixed at wide-angle. If thezooming function can be controlled to track the person asshown in Fig. 2(a), a series of images as shown in Fig. 2(b)can be easily captured. For these six captured images, theleftmost and rightmost images are still blurred. However,two clear images can be captured as shown in the series of
the images. The clear images are useful for the related
image-based functions or services, such as face recognition.
Basically, the HD camera is controlled to zoom in when aperson is moving toward to the center of the square.
Oppositely, the camera is controlled to zoom out when the
person is moving away from the center of the square.
In order to achieve the above purpose, an adaptive
neuro-fuzzy inference system (ANFIS) is used to design thezoom controller in the zooming mechanism. The zoom
value and location of the moving person in a real-time
image are input to the ANFIS. Accordingly, a suitable value
for adjusting the zoom is derived and submitted to thecamera. The zoom is controlled to be as large as possible

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

and to capture the image as clear as possible.

(a)

(b)
Fig. 2. The demonstration of the automatic zooming mechanism (a)moving person tracking (b) captured images

The remainder of this paper has been organized asfollows: Section 2 reviews related work; Section 3 presents
the method of the proposed zooming mechanism; Section 4
describes the experimental study; and Section 5 presents
the conclusion and the need for future work as indicated by
the results of this study.
II. RELATED WORKS
Several previous studies related to HD cameras are
discussed here. Methods or systems were proposed forvarious requirements under some specific environments.
These studies are helpful to increase the functionalities of
HD cameras. For example: S. C. Chan et al. proposed an
intelligent video surveillance (IVS) system in a multi-HD
camera environment [1]. Every HD camera was connected
to a proposed FPGA (Field Programmable Gate Array)board. This board was designed to handle some basic
functions of pattern recognition, such as background model
establishment, foreground object detection and tracking.
The results were then transmitted to a server for handlingadvanced analysis functions with high-computing needs,
such as consistent labeling of objects cameras or computingof image depth information.
G. Scotti et al. proposed a pedestrian classification
system, called panoramic scene analysis (PSA) system, byintegrating omni-directional cameras and HD PTZ(Pan-Tilt-Zoom) cameras [2]. The PTZ camera can monitor
a large area by using its rotation and zooming functions. It
can also provide HD images of pedestrians. Firstly, PSA
system transformed the polar image to a wide-view
panoramic image. Then, the system classified the moving
objects into pedestrians or vehicles. The PSA system
controlled the PTZ camera to track all the pedestrians
Besides, M. S. Sayed and J. G. R. Delva proposed anefficient intensity correction algorithm for HD images [3].
The trend of surveillance systems is toward low cost, highefficiency, and high resolution. Many intelligent
surveillance functions, such as intrusion detection, should
be built in the HD camera for providing efficient responseor alarm. However, most of the function can only perform
well under fixed light condition. Therefore, the proposed
intensity correction algorithm is useful to increase therobustness of the intelligent functions. The correction canbe either global or local. An apparent gain factor is definedfor intensity correction. The algorithm is also realized in
Xilinx Spartan3A digital signal processor (DSP)870

XC3SD3400A device. The results showed that thealgorithm can process 1080p (19201080) images at 30
frames per second (FPS).
According to the above studies related to HD cameras,
the method or system design must take into account thehigh computing load on processing HD image. In this paper,the design of zooming mechanism not only speeds up theprocessing of HD images, but also integrates the tracking
technique and fuzzy controller for an HD camera with
optical zoom in order to increase the feasibility of HD
camera in the practical environment.
III. METHOD
In order to design an automatic zooming mechanism
for a HD fixed camera, two basic steps are performed. One
is foreground object detection and tracking, the other iszoom control. After several attempts to achieve the desired
results, the final process is depicted in Fig. 3. Firstly, theforeground moving objects are detected. When an object is
detected, its template is extracted for tracking. CamShifttracker is used in this process. Then, the location andmoving direction of the object is estimated and input to the
ANFIS zoom controller. A suitable delta zoom value forzoom-in or zoom-out is submitted to the HD camera. Thesemain steps are presented in the following subsections.
Real-time
ImagesHD
CameraCamShift Object
Tracking
Moving Object
DetectionNo

Objectdetected?

Location/Direction
Estimation

TargetObjectTargetTemplatTemplates
Templatee

Yes
Template
Extraction

Wide-Angle
Mode

ANFIS
Zoom ControllerNo

Is objectin FOV?

Yes

Fig. 3. The process of the proposed automatic zooming mechanism

A. Object detection phase
Foreground object detection is the first step forzooming control. Two popular methods are used in this step,
background subtraction and temporal differencing. In thispaper, the object detection is performed only in the
wide-angle mode. In order to avoid the influence of theenvironmental brightness, the Gaussian Mixture Model(GMM) is used to construct the background model. Then,the real-time image is subtracted with the backgroundimage to detect the foreground objects. Several
morphological operations are also used to generate clear
foreground objects.
In GMM, a set of Gaussian distributions is used torepresent the distribution of pixel values [4]. The model canbe represented using Eq. (1).
𝑝(𝑥𝑁 |λ) = ∑𝑀
𝑖=1 𝑤𝑖 g 𝑖 (𝑥𝑁 )(1)

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

Here, 𝑝(𝑥𝑁 |𝜆) is formed by M probability density
functions of GMM. Notation x represents the input data andN is the number of input data. 𝜆 is formed by{𝑤𝑖 , 𝜇𝑖 , Σ𝑖 }, 𝑖 = 1,2, … , 𝑀 , where wi is mixture weighted
value, 𝜇𝑖 is the average vector, Σ𝑖 is covariance matrix.
g 𝑖 (𝑥) represents the i-th density function of the Gaussian
distribution. The initial values of M, 𝜇, Σ, and 𝑤 are
determined by using the K-means algorithm. Then,
Maximum likelihood estimation (MLE) method is used for
updating the values to obtain the best results. The density
function of GMM is represented in Eq. (2).
𝐸(𝜆) = ln(∏𝑛𝑖=1 𝑃(𝑥𝑖 )) = ∑𝑛𝑖=1 ln𝑃(𝑥𝑖 )(2)Then, expectation maximization (EM) algorithm isused to find the maximal value iteratively, as shown in Eq.
(3).
𝑤𝑚 g(𝑥|𝜇𝑚 ,Σ𝑚 )𝛽𝑚 (𝑥) = 𝑝(𝑚|𝑥) = ∑𝑀

𝑗=1 𝑤𝑗 g(𝑥|𝜇𝑗 ,Σ𝑖 )Σ𝑚 =𝛽 (𝑥𝑖 )𝑥𝑖
𝑖=1 𝛽𝑚 (𝑥𝑖)

(4)

𝑇
∑𝑛
𝑖=1 𝛽𝑚 (𝑥𝑖 )(𝑥𝑖 −𝜇𝑚 )(𝑥𝑖 −𝜇𝑚 )𝑛∑𝑖=1 𝛽𝑚 (𝑥𝑖 )(5)

1Finally, the results of the above three parameters areapplied to Eq. (3) to check whether or not the convergencecondition is satisfied. If it is not satisfied, the iteration of
EM algorithm is repeated.
An example of foreground object detection is shownin Fig. 4. The background image constructed by using
GMM is shown in Fig. 4(a). The real-time image shown in
Fig. 4(b) is subtracted by the background image and theresult is shown in Fig. 4(c), which contains some noise or
fragments. After the processing of morphologicaloperations, including erosion and dilation, the final result isshown in Fig. 4(e), which is a clear detection.

(c)

(b)

(d)

(7)

𝑀10 = ∑ℎ𝑦 ∑𝑤
𝑥 𝑥𝐼(𝑥, 𝑦)(8)

𝑀01 = ∑ℎ𝑦 ∑𝑤
𝑥 𝑦𝐼(𝑥, 𝑦)

(9)

𝑀10
𝑀00

，𝑌𝑐 =

𝑀01
𝑀00

(10)

I(x, y) is the pixel intensity at the location (x, y) of Ft.
h and w represent the height and width of the search area,
respectively. Xc and Yc represent the moving distance in the
X and Y direction, respectively. The above process isrepeated until the convergence of the location (Xc, Yc) isachieved. An example of such a convergence is shown in
Fig. 5.

(6)

𝑛

(a)

𝑀00 = ∑ℎ𝑦 ∑𝑤
𝑥 𝐼(𝑥, 𝑦)𝑋𝑐 =𝑚
𝜇𝑚 = ∑𝑖=1𝑛

𝑤𝑚 = ∑𝑛𝑖=1 𝛽𝑚 (𝑥𝑖 )with an adaptive region sizing step [5]. In general, the sizeof a moving object is varied during tracking. CamShiftalgorithm can reduces the error caused by the change ofobject size in the mean-shift algorithm. It searches the most
likely region, which is most similar to the template, in anarea. Assume the template image at time t is denoted as Ft.
The probability distribution of Ft is denoted as P(t). The
centroid of Ft1 and P(t) is used to search the new locationof the template in the CamShift algorithm. In order toachieve the above goal, the zero order moments (M00) andthe first order moments (M01 and M10) are estimated byusing Eq. (7)-(10):(3)

𝛽𝑚 (𝑥) is the generating function of m-th Gaussian
distribution. 𝑤𝑚 , 𝜇𝑚 , and Σ𝑚 are the parameters to be
estimated and can be obtained by Eq. (4)-(6):
∑𝑛

871

(e)

Fig. 4. An example of foreground object detection (a) GMM backgroundimage (b) real-time image (c) background subtraction (d) erosion (e)dilation

B. Object tracking phase
After an object is detected, the next step is object
tracking. CamShift tracking algorithm is used in this step.
CamShift algorithm combines basic mean-shift algorithm

Fig. 5. Convergence of the location (Xc, Yc) in the CamShift algorithm

A series of (Xc, Yc) can be obtained in the above
CamShift algorithm. However, the location is unstable dueto the influence of noise or environment brightness. So, a
Kalman is used to smooth the tracking results. The filter is
described in the following equations [6].
𝑥̂𝑘− = 𝐴𝑥̂𝑘−1 + 𝐵𝑢𝑘−1(11)

𝑃𝑘− = 𝐴𝑃𝑘−1 𝐴𝑇 + 𝑄(12)

𝐾𝑘 = 𝑃𝑘− 𝐻 𝑇 (𝐻𝑃𝑘− 𝐻𝑇 + 𝑅)−1(13)𝑥̂𝑘 = 𝑥̂𝑘− + 𝐾𝑘 (𝑧𝑘 − 𝐻𝑥̂𝑘− )(14)

𝑃𝑘 = (𝐼 − 𝐾𝑘 𝐻)𝑃𝑘−(15)

where 𝑥̂𝑘− is a priori state estimate at step k givenknowledge of the process prior to step k, 𝑥̂𝑘 is a posterioristate estimate at step k given measurement. zk The matrix A
relates the state at the previous time step k-1 to the state at
the current step k, and matrix B relates the optional control
input uk to the state x. Pk is estimate error covariance, Kk is
Kalman gain, Q is process noise covariance, and R is the

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

measurement noise covariance. Eqs. (11) and (12) are time
update equations. Eqs. (13)-(15) are measurement update
equations. An application of Kalman filter is shown in Fig.
6. A series of y coordinates is processed by the Kalmanfilter. The original coordinates in blue are smoothed into
the curve in red.

872

denoted as . Its output equals the multiplication of all
the input as represented in Eq. (17). The output
represents the strength of the rule.
𝑂2,𝑖 = 𝑤𝑖 = 𝜇𝐴𝑖 (𝑥)𝜇𝐵𝑖 (𝑦), 𝑖 = 1,2(17)3) The third layer is the normalization layer. The nodes aredenoted as N. In a node i, the ratio of the strength of i-thrule to the strength of all the rules are computed using
Eq. (18). The output is the normalized firing strength.
𝑂3,𝑖 = ̅̅̅𝑤𝑖 =𝑤𝑖
𝑤1 +𝑤2, 𝑖 = 1,2

(18)4) The forth layer is inference layer of consequence. The
node i in this layer is also an adaptive node with thenode function represented in Eq. (19). ̅̅̅
𝑤𝑖 is the output
and {𝑝𝑖 , 𝑞𝑖 , 𝑟𝑖 } is the parameter set. All the parameters
are called consequence parameters.
𝑂4,𝑖 = ̅̅̅𝑓
𝑤𝑖 𝑖 = 𝑤𝑖 (𝑝𝑖 𝑥 + 𝑞𝑖 𝑦 + 𝑟𝑖 ), 𝑖 = 1,2

Fig. 6. An application of Kalman filter(parameters Q=0.001 and R=0.0001)

C. Zooming mechanism
For the zoom control of an HD fixed camera, thecamera should be zoomed in when the object is moving
toward the center of FOV. Oppositely, the camera should bezoomed out when the object is moving away from thecenter of FOV. Therefore, a fuzzy controller is used for thezooming mechanism. In advance, the settings of fuzzy rules
and membership functions are the main challenge on
designing the fuzzy controller. The adaption of a fuzzycontroller from one environment to another is also an issue.
According to the above considerations, an adaptive
neuro-fuzzy inference system (ANFIS) is used on thedesign of automatic zooming mechanism [7]. The
first-order Sugeno fuzzy model is used in ANFIS. The
structure of ANFIS is shown in Fig. 7.
A1

π

x1
A2

N

π
x1 x2 x3

B1

x2

π

Σ

N

y

B2

...
C1

x3

x1 x2 x3πNlayer 2layer 3C2
x1 x2 x3

layer 1layer 4layer 5

Fig. 7. The structure of ANFISANFIS is a five-layered structure. Every layer is
described as follows:
1) The first layer is input layer. Every node in this layer isan adaptive node with a node function represented in Eq.
(16).
𝑂1,𝑖 = 𝜇𝐴𝑖 (𝑥), for 𝑖 = 1,2, or𝑂1,𝑖 = 𝜇𝐵𝑖−2 (𝑦), for 𝑖 = 3,4(16)

where O1,i is the membership value of the input x
or y corresponding to the fuzzy set Ai or Bi. The
parameters in this layer are called premise parameters.
2) The second layer is rules. All the nodes are fixed node,(19)5) The fifth layer is output layer. There is only one node inthis layer denoted as . The output is the summation of
all the outputs in the previous layer as represented in Eq.
(20).
∑ 𝑤𝑓

𝑂5,1 = ∑𝑖 ̅̅̅𝑓
𝑤𝑖 𝑖 = ∑𝑖 𝑖 𝑖
𝑖 𝑤𝑖

(20)

Although ANFIS is more complicated than theconventional fuzzy inference system, it cannot be applied toany fuzzy inference applications unless the following
criteria are satisfied:
1) The ANFIS structures must be the first-order orzero-order Sugeno models.
2) Singleton output: A weighted average method is used
for defuzzification.
3) Different rules cannot be applied to the same
membership function.
4) The weight of all the rules is the same.
Sugeno models apply polynomial functions toconstruct the consequent values. A weighted averagemethod defuzzification method is used to generate the final
result.
The construction of the fuzzy inference system (FIS)
is based on the subtractive clustering method. The
parameters, including range of influence, squash factor,
accept ratio, and reject ratio are set to 0.2, 1.25, 0.5, and0.15, respectively. When the FIS is initialized, a set of aseries of zoom values is collected. During the period of an
object moving through the monitoring area of HD camera,
the zoom values of manual control are recorded. Then, afuzzy error backpropagation algorithm is used to learn the
fuzzy control rules membership functions. The errors arepropagated backward from layer to layer through the node
links. A steepest descent method is used to decrease theerror by updating the weights in the neurons of the different
layers. The parameters, error tolerance and epochs, are set
to 0.001 and 10000, respectively. According to the moving
directions of foreground objects, four fuzzy controllers arelearning separately. The inputs of the controller include thecurrent zoom value and location. The training results areshown in Fig. 8. The blue circles represent the training data

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

and the red circles represent the training results. There are
600 training data, including 200, 200, 100, and 100 data forleft, right, up and down directions separately. When an HD
camera is installed at a different environment, a fuzzycontroller can still be trained based on the above process.

873






(a)

(b)





(c)

(d)

Fig. 8. The training data and results based on zoom value and location (a)up (b) down (c) left (d) right

Another set of fuzzy controllers are also trained using
the zoom value and moving speed of an object as inputs.
The training data and results are shown in Fig. 9. There are
four controllers for different directions. However, the zoom
value is not close related to the moving speed. Therefore,
the errors of these controllers are larger than the previous
controllers shown in Fig. 8. The maximal zoom value canbe achieved during the moving period is smaller than theprevious controllers. It causes the captured image is alsoless clear than that captured by using the previous
controllers. So, the previous controllers are chosen in thedesign of zooming mechanism.

(a)

(b)

(c)
(d)
Fig. 9. The training data and results based on zoom value and moving
speed (a) up (b) down (c) left (d) right

IV. EXPERIMENTAL STUDY
An experiment is designed to evaluate the performanceof the automatic zooming mechanism. The experiment
design and results are presented in the following
subsections.
A. Experiment Design
The experiment is conducted on a commercial HD
camera (AXIS Q1755) and a prototype implemented using
Visual C# 2012, MATLAB R2012a and the EmguCV. The
screenshot of the prototype is shown in Fig. 10. The
prototype is executed on a PC with i3-550 3.2GHz CPU
and 6GB RAM.

Fig. 10. The screenshot of the prototype

The area marked with  is the basic control of the
HD camera. The area marked with  is the functions of theautomatic
zoomingmechanism.
The
checkbox“Auto-Select” enables the system to select a moving object
automatically. The checkbox “Auto-Zoom” enables theautomatic zooming mechanism. The area marked with  isthe result of the foreground object detection and the settingof parameters. The area marked with  is the real-time
image of HD camera. The area marked with  is the
tracking template of an object. The area marked with  isthe captured images of a moving object under different
zooms.
There are totally 70 tests in the experiment. 50 tests
are objects moving in horizontal direction and 20 tests are
objects moving in vertical direction. The tests of horizontal
moving are larger than those of vertical moving because
most of the objects are moving in horizontal direction. A
moving object is selected randomly in a square and theautomatic zooming mechanism is enabled. The zooming
mechanism adjusts the zoom every 600 and 800ms forhorizontal and vertical moving, respectively. For thevertical moving of objects, the frequency of zoom
adjustment is lower because the object size and location ismore stable, compared with the horizontal moving. For
further analysis, the images of the tracking object arecaptured before the zoom adjustment as well.
B. Experiment Results
When the automatic zooming mechanism of the
prototype is enabled, the zoom of the HD camera iscontrolled to capture the object image as clear as possible.
Two examples of the captured images with two different
moving directions are shown in Fig. 11 and Fig. 12. The
image resolutions are increasing when the object is moving
toward the center and then decreasing when leaving thecenter.

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 11. A series of images captured when the object is moving left (a) 0s(b) 3.8s (c) 5.2s (d) 5.3s (e) 8.9s (f) 11.8s

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Fig. 12. A series of images captured when the object is moving down (a)0s(b)1s (c)4s (d)5.1s (e)6.6s (f)7.7s (g)9.7s

The results are analyzed based on the captured images
collected in the experiment. There are three results
described below:
1) The processing time: The average processing time ofall steps are computed. An HD image is usually resized
to shorten the processing time.
2) The maximal zoom value: For every tracking, themaximal zoom value is recorded and counted to realize
the performance of the automatic zooming mechanism.
A higher zoom value means that the zooming
mechanism is effective to control the optical zoom of
an HD camera.
3) The maximal height of an object: The larger the heightof an object is, the clearer is the object quality.
Therefore, the maximal height of an object is recorded
and counted.
Firstly, the processing time of an image in each step isrecorded. In order to reduce the computing load of an HD
image, the image is resized to 640480. The average timeof the foreground object detection, CamShift tracking, andzoom control is 7.96 ms, 3.11 ms, and 2.65 ms, respectively.
It shows that the proposed zooming mechanism is efficient.
The largest zoom value of the HD camera is 10,000.
The maximal zoom values of object tracking are recorded
for horizontal and vertical moving separately. These values
are summarized by counting the ratio of the maximal
values larger than a specific value. The results are depicted
in Fig. 13. For the curve of the horizontal moving, it showsthat all the maximal zoom values are larger than 3,000(100%). The ratio remains high (92%) for the zoom value
6,000. For the zoom value 7,000, the ratio is decreased to80%. For the vertical moving, the object in the image isstable. The zooming mechanism is able to achieve higher
zoom than the horizontal moving. So, the ratio of verticalmoving is always higher than the ratio of horizontal
moving. The results show that the zooming mechanism canachieve a quite high zoom value for capturing clear images.

874

Fig. 13. The percentage of the maximal zoom values

The maximal height of an object is also recorded.
The distributions of the maximal heights of two kinds ofmoving directions are shown in Fig. 14. For the horizontal
moving, most of the heights are within 350 to 500 pixels.
Oppositely, most of the heights are within 500 to 600 pixels
for the vertical moving. It is caused by the same reason thatvertical moving is more stable.
Some examples of the captured images with the
maximal height are shown in Fig. 15(a) and (b). They
illustrate that the object images can be identified clearly.

Fig. 14. The distributions of the maximal heights

(a)

(b)
Fig. 15. The captured images with various heights (a) horizontal (b)vertical

Besides, 20 captured images with maximal zoom
value of the vertical moving are processed for facedetection algorithm. The detection algorithm is based on
the skin regions and the geometric constraints, such as ratio.
The detection algorithm is implemented by using OpenCV
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

library. All of them can be detected successfully as shownin Fig. 16 since their zoom value is larger than 6,000.
Several examples are also shown in Fig. 17. When the faceis detected, the detection result is marked by a red circle.
These results demonstrate that the proposed automatic
zooming mechanism is useful to capture clear images for
related image-based services, such as face recognition.

Fig. 16. The zoom values of successful face detection

875

V. CONCLUSION
HD cameras are widely used in a surveillance system.
In this paper, an automatic mechanism is proposed for the
HD fixed camera with zooming capability. When an object
is moving through the FOV of the camera, a clear image
can be captured and preserved. The mechanism consists of
the object detection, CamShift tracking, ANFIS algorithm,
and fuzzy controller. ANFIS is used to help the fuzzycontroller learn the fuzzy rules and membership functions
from the manual control data. The mechanism can be used
in a new environment based on the same learning process.
According to the experiment results, the maximal
zoom values of 80% tests can reach 7,000. It ensures that ahigh resolution image of the moving object can be captured.
Furthermore, the average processing time of the mechanism
is efficient, about 13.7ms. The face can also be detectedsuccessfully in the captured images. It demonstrates thatthe images captured by the automatic zooming mechanism
are feasible for image-related services. Many related
services, such as license plate recognition (LPR), can beapplied to such HD fixed cameras.
In the experimental study, it is found that the zooming
mechanism is directly influenced by the object tracking.
The tracking accuracy will be enhanced in the future toincrease the performance of the mechanism.
REFERENCES[1]

Fig. 17. Examples of the captured images applied to face detection[2]

Besides, the time spans between two successive zoom
adjustments are also recorded. The distributions of timespans for horizontal and vertical directions are shown in
Figure 18. The x-axis represents the time span inmilliseconds and the y-axis represents the counts ofadjustments. The time spans of horizontal direction areabout 1,400 to 1,600 milliseconds. The time spans ofvertical directions are about 1,700 to 1,900 milliseconds.
The moving of an object in the vertical directions is stable
and causes the time spans can be longer than that ofhorizontal direction. The time span is also related to thetraining data of ANFIS. If the time spans of manual zoom
adjustments can be shorten, the time spans of the automatic
zooming mechanism can be shorten, too. The maximal
zoom values will be possibly higher than the current
mechanism.

[3]

[4]

[5]

[6]

[7]

S. C. Chan, S. Zhang, J. F. Wu, H. J. Tan, J. Q. Ni, and Y. S. Hung,“On the Hardware/Software Design and Implementation of a HighDefinition Multiview Video Surveillance System,” IEEE Journal
on Emerging and Selected Topics in Circuits and Systems, Vol. 3,
No. 2, June 2013, pp. 248-262.
G. Scotti, A. Cuocolo, C. Coelho, and L. Marchesotti, “A Novel
Pedestrian Classification Algorithm for a High Definition Dual
Camera 360 Degrees Surveillance System,” The IEEE
International Conference on Image Processing (ICIP 2005), Genoa,
Italy, 11-14 Sept. 2005, Vol. III, pp. 880-883.
M. S. Sayed and J. G. R. Delva, “An Efficient Intensity Correction
Algorithm for High Definition Video Surveillance Applications,”
IEEE Transactions on Circuits and Systems for Video Technology,
Vol. 21, No. 11, Nov. 2011, pp. 1622-1630.
S. C. Jeng, “A GMM-based Method for Dynamic Background
Image Model Construction with Shadow Removal,” Master Thesis,
National Chiao-Tung University, ECE, June, 2005, pp. 83.
G. R. Bradski, “Real Time Face and Object Tracking as a
Component of a Perceptual User Interface,” The Fourth IEEE
Workshop on Applications of Computer Vision (WACV’98), 1998,
pp. 214-219.
G. Welch and G. Bishop. “An Introduction to the Kalman Filter,”Technical Report, TR95-041, University of North Carolina, Chapel
Hill, 2004, 16 pages.
J. S. R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference
System,” IEEE Transactions on Systems, Man and Cybernetics,
Vol. 23, 1993, pp. 665-685.

Hsien-Chou Liao received the B.S. and Ph.D.
degrees in Computer Science and Information
Engineering from National ChiaoTung University
in 1991 and 1998, respectively. He is a professor,
Department of Computer Science and Information
Engineering, at Chaoyang University ofTechnology, Taiwan, R.O.C. He is also a senior
member of IEEE. His research interests include
location-based service, smart technology, mobile
computing, computer vision, and pattern recognition.
Fig. 18. The distribution of time span between zoom adjustments

Po-Yueh Chen received his B.S, M.S., and Ph.D.
degrees in EE from National Taiwan University in
1988, National Chiao Tung University in 1990 and
University of Maryland at College Park in 1997
respectively. Currently, Dr. Chen is a faculty

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016876

member in Department of Computer Science and Information Engineering,
National Changhua University of Education, Changhua, Taiwan. His
research interests include image processing, FPGA implementations, VLSI
architecture design and digital IC design.
Zi-Jun Lin received the B.S. and M.S. in
Computer Science and Information Engineeringfrom the Chaoyang University of Technology in2014 and National Changhua University of
Education in 2016, respectively. His current
research interests are in the computer vision.

Zi-Yi Lim received the B.S. and M.S. in Computer
Science and Information Engineering from the
Chaoyang University of Technology, Taiwan,
Republic of China, in 2014 and 2016. His current
research interests are in the area of computer
vision.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

877

Analysis of Very High Throughput (VHT) at MAC
and PHY Layers under MIMO Channel in IEEE
802.11ac WLAN
Gul Zameen Khan∗ , Ruben Gonzalez∗ , Eun-Chan Park∗∗ , Xin-Wen Wu∗∗ School of Information and Communication Technology, Griffith University, Australia∗∗ Dept. of Information and Communication Engineering, Dongguk University, South Korea
gz.khan@griffithuni.edu.au, r.gonzalez@griffith.edu.au, ecpark@dongguk.edu, x.wu@griffith.edu.au

Abstract—This paper analyses the very high system throughputof IEEE 802.11ac by taking into consideration the key features of
MAC and PHY layers under a Multiple In Multiple Out (MIMO)
channel. Throughput at the MAC layer is calculated from thetransmission probability, contention window and transmission
stage. Likewise, the new critical attributes of 802.11ac PHY (i.e.
modulation and coding schemes, spatial streams, and channel
bandwidth) are used to determine the throughput at the PHY
layer. To this end, a theoretical model is formulated at the MAC
and PHY layers followed by a system model of MIMO multipath
fading channel for 802.11ac. The system model is verified by simulation analysis. The results compare theoretical and simulation
findings for different sets of parameters. Furthermore, important
trends and trade-offs are identified between system throughput
and (MAC + PHY) features as a function of number of contending
stations and payload size. The system throughput of 802.11ac
networks is significantly improved due to the addition of new
PHY features. However, the system may degrade upto 50% in
terms of symbol reception in case of a high error-prone MIMO
channel. The performance of 802.11ac systems is also analyzed
under different MIMO TGn channel models in terms of Packet
Error Rate (PER). Thus based on our simulation results, anappropriate channel model can be chosen for 802.11ac network
under a given configuration to achieve a better performance.
Keywords—Performance, analysis, throughput, MAC, physical,
MIMO, multipath, fading, transmission probability, contention
window, modulation, coding, spatial streams, channel bandwidth,channel model

I. I NTRODUCTION
Gigabit Wireless Local Area Networks (WLAN) is a stateof-the-art technology based on the emergence of new IEEE
standard i.e., 802.11ac [1]. The standard achieves Very HighThroughput (VHT) with the aid of efficient Modulation andManuscript received on March 10, 2016. This work is a follow-up of theinvited journal to the accepted out-standing conference paper of the 18th International Conference on Advanced Communication Technology (ICACT2016).
Gul Zameen Khan is with the School of ICT, Griffith University, Australia. (corresponding author, phone: +61450039010, email:gz.khan@griffithuni.edu.au)
Dr. Ruben Gonzalez is a senior faculty member, head of Intelligent Wireless
Technology and Applications research group, and director of the bachelor ofcomputer science program at School of ICT, Griffith University, Australia.
(email: r.gonzalez@griffith.edu.au).
Prof. Eun-Chan Park is with the Dept. of Information and Communication Engineering, Dongguk University, South Korea. (email: ecpark@dongguk.edu)
Dr. Xin-Wen Wu is with the School of ICT, Griffith University, Australia.
(email: x.wu@griffith.edu.au)

Coding Schemes (MCS) such as 256-Quadrature Amplitude
Modulation (QAM), explicit transmit beamforming, enhanced
Multiple Input Multiple Output (MIMO) technology, and large
bandwidth. Most of the leading vendors and manufacturers
have already implemented 802.11ac in their Wi-Fi chipsets[2]. 802.11ac operates in 5 GHz band and can support a highdata rate up to 6.933 Gb/s.
Currently, most of the literature available on 802.11ac
focuses on improving the underlying technology of 802.11ac.
This includes but is not limited to exploring Single User (SU)and Multi User (MU) MIMO, adding new Spatial Streams(SS), improving frame aggregation techniques, channel bonding, and incorporating advanced modulation and coding techniques in 802.11ac. In [3], a review of PHY layer featuresof 802.11ac is presented. The performance of MU-MIMO isanalysed via a testbed. However, the chips used in the experiment are based on 802.11n [4]. In [5], the authors present
a performance analysis of energy efficiency and interferencein 802.11ac. It is shown that larger channels consume more
power while the addition of more SS is energy efficient.
However, the performance measurements are not verified byany theoretical model. Although a comparison of 802.11ac
and 802.11n is drawn in [6] in terms of different frame
aggregation techniques, other key features of 802.11ac havenot been explored. Similarly, [7] discusses the requirementin MAC modifications and enhancements for downlink MUMIMO transmission. In particular, it introduces the techniqueof enhancing Transmit Opportunity (TXOP) and the revised
backoff procedures. In the same manner, the authors of [8]present a review of how the capacity can be estimated and optimized using Ekahau Site Survey tool in 802.11ac. Similarly,[9] presents a performance comparison between 802.11n and802.11ac in terms of throughput for three MAC frame aggregation techniques under constant PHY conditions. For the mostpart, 802.11ac outperforms 802.11n due to its larger frames inerror-free channel. On the contrary, the optimal frame size isdetermined by bit error in error-prone channel conditions. The
paper however, considers only the frame size instead of otherkey factors of MAC and PHY layers. Similarly, an overview ofstatic and dynamic channel selection methods is demonstrated
in [10] with the help of simulations. It is shown that dynamic
selection of a primary channel achieves high throughput for802.11ac when other stations operating in 802.11a/n occupy
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

the secondary channel. However, the simulation is performed
in an ideal environment without taking into consideration theeffects introduced by PHY and channel impairments.
Previous work addresses the individual features of 802.11ac.
However, a comprehensive performance analysis of 802.11ac
is important in order to estimate the achievable throughput.
This paper extends our previous work [11] and considers
several new features of 802.11ac (i.e., MCS, channel bandwidth, spatial stream) which can be a baseline to determine thesystem level throughput of an 802.11ac wireless network. To
this end, a theoretical analysis based on both MAC and PHY
layers is presented under a MIMO channel which is followedby simulation results. The paper thoroughly investigates theimpacts of different features of 802.11ac on system throughput
under different set of parameters. In addition, it provides
a baseline model to measure the MAC and PHY layers
performance of 802.11ac in terms of aggregate throughput.
The impact of TGn channel models on the performance of
802.11ac is also presented for a complete networked systemanalysis of VHT.
The remainder of this paper is organized as follows: A brief
overview of 802.11ac is discussed in section II. Section III
describes a theoretical system model, MIMO channel modelof 802.11ac and simulation set up. In Section IV, results anddiscussions are presented. Lastly, the paper is concluded in
Section V.
II. OVERVIEW OF IEEE 802.11 AC
There are three key features that lead to VHT in 802.11ac.
Each of them is described as follows:
A. More Spatial Streams(SS)802.11ac has increased the number of SS from 4 in 802.11n
up to 8 at PHY layer in MIMO Orthogonal Frequency Division
Multiplexing (OFDM). There are three main advantages ofusing more MIMO in 802.11ac namely:i.
Extending the rangeii.
Improving reliability
iii.
Achieving higher throughput
In addition to SU-MIMO, 802.11 WAVE-2 also supports down
link MU-MIMO in which an Access Point (AP) can send
multiple data frames in the form of Aggregated MAC Protocol
Data Unit (A-MPDU) to multiple receivers at the same time.
In practice, the current 802.11ac devices support 3 SS and 4
SS in 802.11ac WAVE-1 and WAVE-2, respectively. However,a total of 8 SS can be supported in the upcoming products [1],[12].
B. Modulation and Coding
A more advanced modulation i.e. 256 QAM has been added
to 802.11ac standard. This increases the number of bits per
sub-carrier of OFDM compared to 64 QAM. As a result, thePHY data rate raises up to 33% as compared to previous
802.11 standards. On one hand it significantly increases data
rate; on the other hand, however, it requires higher Signal toNoise Ratio (SNR) for receivers to correctly demodulate thesymbols. Similarly, 802.11ac supports various coding rate of
5
1 2 3
2 , 3 , 4 , and 6 [1].

878

C. Wider Channel BandwidthWith 5 GHz band, the bandwidth of 802.11ac has been
increased. In addition to 20 MHz and 40 MHs channels thatwere already available in 802.11n, wider channels of 80 MHz
and 160 MHz have been added in 802.11ac. Furthermore the
160 MHz channel can be established with two contiguous or
non-contiguous 80 MHz channels [1].
III. S IMULATION AND N UMERICAL A NALYSIS
In this section we describe our system model for MAC and
PHY layers and derive a theoretical formulation to calculate
the performance of MAC and PHY layers numerically. We also
formulate a MIMO channel model for 802.11ac and describe
our simulation environment.
A. MAC Layer Theoretical Analysis
We consider a single hop fully connected Single Basic
Service Set (BSS) with n Stations(STAs) and one AP. It is
assumed that each STA can sense transmission from every
other STA in the same BSS. All STAs operate in uplinksaturated mode i.e., they always have data to send to the AP.
A data transmission is considered successful if it is followedby an Acknowledgement (ACK) from the AP, otherwise it isretransmitted. The wireless channel is assumed ideal i.e., aframe is failed only due to collision that occurs when two or
more STAs access the shared channel simultaneously.
We consider the Markov model of [13] that represents theDistributed Coordination Function (DCF) as two stochastic
processes namely: b(t) and s(t) to model the backoff time
counter and the backoff stage, respectively. Without the lossof generality, let CWmin and CWmax represent minimum andmaximum contention windows, respectively. Let m indicatesmaximum backoff stage such that CWmax = 2m CWmin . Let
CWi represents the contention window of a STA at ith backoff
stage i.e., CWi = 2i CWmin where i ∈ [0, m]. Thus b(t)takes any random value from [0, CWi ] where i is modelled bys(t). For the sake of simplicity, we represent CWmin = W .
The key approximation is that collision probability is constantregardless of retransmission stage. This is a reasonable approximation as long as W and n get larger.
During a randomly selected slot, a STA senses the channel
in one of the three states namely: idle state (no transmission
activities), busy due to successful transmission, or busy due
to collision. Suppose a STA attempts to transmit a frame ina randomly chosen slot with probability τ . The system considers a Binary Exponential Backoff (BEB) mechanism which
doubles the contention window on collision. If p represents
the collision probability; then τ is given by [13]τ=

2(1 − p)(1 − 2p)(W − 1) + pW (1 − (2p)m )(1)

The probability of collision p can be calculated from thefact that collision occurs if at least one of the remaining n − 1
STAs starts transmission. Therefore, if 1 − τ is the probability
that exactly one STA is idle then (1 − τ )n−1 is the probability
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016that n − 1 STAs are idle. It follows that the probability that atleast one of n − 1 STAs transmits is given by [13]p = 1 − (1 − τ )n−1(2)

Transmission probability τ and collision probability p can becalculated numerically by solving Eq. 1 and Eq. 2 using somenumerical method (e.g., fixed point iteration). In addition, it
can be proved that this system of non linear equations has aunique solution [13]. We have used Maple 15 [20] to solve
Eq. 1 and Eq. 2.
We are interested in calculating throughput S of the system
which is expressed as a ratio of average payload informationtransmitted in a slot per average duration of a slot.
S=

E[D]
E[T ]

(3)

where E[D] is the expected value of data transmitted successfully in a randomly selected slot while E[T ] is the average
length of a time slot;
E[D] = Ptr Ps E[L](4)

where Ptr denotes the probability that there is at least one
transmission in the considered time slot. On the other hand,
Ps is the probability that the given transmission is successfuland E[L] denotes the average length of payload data. Consequently, Ptr can be calculated for n contending stations as
Ptr = 1 − (1 − τ )n(5)

Similarly, Ps can be calculated from the fact that a transmission is successful if and only if exactly one STA transmits
given that at least one STA transmits among n STAs, i.e.,Ps =nτ (1 − τ )n−1
1 − (1 − τ )n(6)

Accordingly, E[D] is calculated from Eq. 5 and Eq. 6. Inorder to calculate E[T ], let us denote T as a random variable
that indicates a randomly selected time slot. Moreover T takes
any of the following three values:σ if the medium is idle
(7)
T = Ts if successful transmission
Tc if there is collision
where σ is the duration of an empty slot while Ts and Tc are
the average time when the channel is busy due to successfultransmission and collision, respectively. The corresponding
probability for these three cases can be calculated as
if T = σ1 − PtrfT (t) = Ptr Ps(8)
if T = Ts
Ptr (1 − Ps ) if T = Tc
Using Eq. 7 and Eq. 8, E[T ] can be calculated as
X
E[T ] =
T fT (t)

(9)

∀t,T

Finally, the normalized throughput S is calculated by using
Eq. 4 and Eq. 9 in Eq. 3. As far as Ts and Tc are concerned,
they are calculated using PHY system model discussed in thefollowing sub section.

879

TABLE I
802.11 AC PHY F RAME
LSTF

LLTF

LSIG

VHTSIG-A

VHTSTFVHTLTFs

VHTSIG-B

Data

B. PHY Layer Theoretical Analysis
As discussed in Section II, the IEEE 802.11ac standard
can increase throughput with the help of wider Radio Frequency (RF) channel bandwidth, more spatial streams, MUMIMO, and advanced MCSs. The performance of PHY can bemodelled by taking into account the aforementioned features.
TABLE I shows a general format of a PHY layer frame [1].
Let Tx be the transmission time of a STA. We assume
that the frame aggregation is not employed. Then, Tx can bederived as follows [1].
Tx = TLEG−P REAM BLE + TL−SIG + TV HT −SIG−A+ TV HT −P REAM BLE + TV HT −SIG−B + TDAT A(10)TLEG−P REAM BLE = TL−ST F + TL−LT F(11)

TV HT −P REAM BLE = TV HT −ST F +NV HT LT F ×TV HT −LT F(12)where TL−SIG , TV HT −SIG−A , TV HT −SIG−B , TLT F , TST F ,TV HT −ST F , TV HT −LT F are fields of PHY frame shown in
TABLE II. NV HT LT F shows the number of long training
symbols which is determined from the number of space-timestreams [14]. Similarly,(
NSY M × TSY M
for long GI(13)
TDAT A =TSY M S ×NSY M
e for short GI
TSY M L d
TSY M L
where GI, TSY M , TSY M S and TSY M L indicate Guard Interval, symbol interval, short GI symbol interval, and long GI
symbol interval, respectively. Their values are listed in TABLE
II.
(
TSY M L for long GI
TSY M =
(14)
TSY M S for short GI
NSY M = mST BC × dMemST BC × NDBP SL(15)

where dxe = smallest integer ≥ x and NDBM S indicates thenumber of data bits per symbol.
M = 8 × AP EPLEN GT H + NService + Ntail × NES (16)(2 if STBC is usedmST BC =(17)
1 otherwise
where AP EPLEN GT H indicates the final value of A-MPDU
i.e., payload size and NES represents the number of Binary
Convolution Code (BCC) encoders. The value of NES depends
on the MCS and channel bandwidth and they are listed in
TABLEs IV-VII. Space-Time Block Coding (STBC) is an
encoding technique that greatly improves the reliability ofcommunication in 802.11ac. Tx is calculated by using Eq.
11-14 in Eq. 10.
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

TABLE II
MAC AND PHY PARAMETERS
Parameter
Slot time (σ)
LmacH
TV HT −ST F
TV HT −SIG−ATV HT −SIG−BTV HT −LT F

Value
9 µs
34 bits
4 µs
8 µs
4 µs
4 µs

Para
CWmin
CWmax
TSY M S
TL−SIGTSY M L
Nservice

Value
16
1024
3.6 µs
4 µs
4 µs
16 bits

Para
TDIF STSIF S
TST F
TLT F
Ntail
ρ

Value
34 µs
16 µs
8 µs
8 µs
6 bits
1 µs

Now, we calculate successful transmission time (Ts ) andcollision time (Tc ) for basic DCF i.e., without Ready To Send
(RTS)/Clear To Send (CTS).
Ts = TDIF S + Tx + ρ + TSIF S + TACK + ρ
Tc = TDIF S + Tx + ρ + TACK−T Out
where TDIF S , TSIF S , TACK , and ρ indicate DCF Inter-Frame
Spacing time, Short Inter-Frame Spacing time, transmission
time of ACK frame and propagation delay of 802.11ac frame,
respectively. Their values are listed in TABLE II. Similarly,
TACK−T Out represents the time out for ACK frame and is
calculated as
TACK−T Out = TACK + TSIF S + ρ
The Ts and Tc for DCF with RTS/CTS can be determined ina similar way.
C. Channel Model for 802.11ac
In order to evaluate the impact of the channel on MAC and
PHY layers of 802.11ac, we consider a set of channel models
that are designed for IEEE 802.11 WLAN [15]. Each model
is applicable to a specific environment with a set of 6 profiles,
labelled A to F. All these profiles cover different scenarios
as listed in TABLE III. Each channel model has a path loss
model including shadowing, and a MIMO multipath fading
model, which describes the multipath delay profile, the spatial
properties, the K-factor distribution shown as Rician K-factor
in TABLE III, and the Doppler spectrum.
Each channel model has a certain number of taps which
are associated with specific delays. Furthermore, each channel
model is comprised of a certain number of clusters. A cluster
is made up of a set of taps. The number of taps, Root Mean
Square (RMS) delay (σRM S ), maximum delay (σM ax ), thenumber of clusters and standard deviation of shadow fading
both in case of Line of Sight (LOS) and Non Line of Sight(NLOS) for each model are listed in TABLE III. The LOS
K-factor is applicable only to the first tap while all the othertaps K-factor remain at −∞ dB.
A set of spatial properties are defined for each cluster:i.
Mean Angle of Arrival (AoA)
ii.
Mean Angle of Departure (AoD)
iii.
Angular Spread (AS) at transmitteriv.
AS at receiver
These parameters determine the transmit and receive
correlation matrices associated with each tap delay. The LOS
component can only be present on the 1st tap. If the distance
between the transmitter and the receiver is greater than dBP

880

then LOS component is not present. Note that dBP is thebreak-point distance or distance of first wall (i.e., distance oftransmitter from the first reflector). The dBP for all channel
models (A-F) are listed in 5th column of TABLE III.
We consider a path loss model that takes into account thefree space loss LF S (log-distance model with the path-loss
exponent of 2) up to dBP and log-distance model with thepath-loss exponent of 3.5 after dBP [16]. For each of themodels, a different break-point distance dBP was chosen.
(
LF S (d)if d ≤ dBP(18)
L(d) =LF S (dBP ) + 35log10(d/dBP ) if d > dBP
where d is the distance between transmitter and receiver. The
other parameters of the path loss model are listed of TABLE
III. The standard deviations of log-normal (Gaussian in dB)
shadow fading are also included in 10th column in TABLE
III. The values were found to be in the range between 3 and14 dB [17].
Similarly, the zero-mean Gaussian probability distribution
is given by
x2
1
(19)
e[ 2σ2 ]p(x) = √2πσWe are interested in modelling the MIMO channel of
802.11ac. Thus the correlation between transmit and receive
antenna is an important aspect of the MIMO channel. To
this end, we follow a procedure based on the transmitterand receiver correlation matrices [18] to calculate the MIMO
channel matrix H for each tap, at one instance of time, in the
A-F delay profile models. The channel matrix H is derived
as a sum of two matrices namely: a fixed LOS matrix withconstant entries, and a Ryleigh NLOS matrix with variable
entries as follows
!
r
√
K
1
H= P
HF +
HV
(20)
K +1
K +1
where P shows the power of each tap which is obtainedby summing all the power of LOS and NLOS powers, andK is the Ricean K-factor. Eq. 20 can be expressed for any
number of transmitter and receiver for MIMO. If there are T
input antennas at the transmitter and R output antennas at thereceiver then HF and HV in Eq. 20 can be represented as jφ
e 11 ejφ12 . . . ejφ1T
 ejφ21 ejφ22 . . . ejφ2T 
HF =  .
..
.. 
..
 ..
.
.
. 
ejφR1

ejφR2

...

ejφRT



X11
 X21

HV =  .
 ..

X12
X22
..
.

...
...
..
.


X1T
X2T 

.. 
. 

XR1

XR2

...

XRT

where ejφij shows the constant elements of LOS matrix HF
and Xij represents the element of variable NLOS Rayleighmatrix HV between ith receiving and j th transmitting antenna.
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

881

TABLE III
PARAMETERS OF A-F PATHLOSS C HANNEL M ODELS 802.11 AC
ChannelnameUser casescenarioA

Flat Fading

B

Residential

Conditions

K (dB)

LOS
NLOS
LOS
NLOS
LOS
NLOS
LOS
NLOS
LOS
NLOS
LOS

0
−∞
0
−∞
0
−∞
3
−∞
6
−∞
6

NLOS

−∞

Residential/
small office
TypicalofficeLarge
officeLarge spaceindoors andoutdoorsCDEF

DBP (m)

No of
taps

σRM S

σM ax

No of
clusters

5

1

0

0

1

5

9

15

80

2

5

14

30

200

2

10

18

50

390

3

20

18

100

730

4

30

18

150

1050

6

It is assumed that Xij is a complex Gaussian random variablewith zero mean and unit variance.
In order to correlate the Xij elements of the matrix HV ,the following method is used1

1[X] = [Rrx ] 2 [Hiid ][Rtx ] 2(21)

where Rrx and Rtx are the receive and transmit correlation
matrices, respectively, and Hiid is a complex Gaussian randomvariable. All these Gaussian random variables are supposed tobe identically independent with zero mean and unit variance.
In addition, Rtx and Rrx are given by[Rtx ] = [ρtxij ](22)[Rrx ] = [ρrxij ](23)

where ρtxij are the complex correlation coefficients between
ith and j th transmitting antennas, and ρrxij are the complex
correlation coefficients between ith and j th receiving antennas.
Alternatively, we use another approach i.e., Kronecker product of the transmit and receive correlation matrices to calculate
X [18].
1[X] = ([Rtx ] ⊗ [Rrx ]) 2 [Hiid ](24)
It can be seen that Hiid is an array in this case instead ofmatrix. The Rtx and Rrx matrices are given as:1
ρtx12 ∗ . . . ρtx1T ∗ ρtx211. . . ρtx2T ∗ 
Rtx =  .
.
.. 
.
.
.
.
 .
.
.
. 



ρtxR1

ρtxR2

...

1

ρrx12 ∗1..
.

. . . ρrx1T ∗. . . ρrx2T ∗ 

.. 
..
.
. 
...
1

 ρrx21
Rrx =  .
 ..
ρrxR1

ρrxR2

1

The values of complex correlation coefficient ρ are calculated from power angular spectrum (PAS), AS, mean AoA,
mean AoD and individual tap powers [15], [19]. Consequently,

Shadow fadingstd. dev. (dB)before/after dBP(LOS/NLOS)
3
4
3
4
3
4
3
6
3
6
3
6

for the Uniform Linear Array (ULA), the complex correlation
coefficient at the linear antenna array is expressed asρ = RXX (D) + jRXY (D)(25)

where D = 2πd/λ (λ shows the wavelength in metre) , andRXX and RXY are the cross-correlation functions between
the real parts and between the real part and imaginary part,respectively, withZ π
RXX (D) =cos(D sin φ)P AS(φ)dφ
(26)
−π

Z π
RXY (D) =sin(D cos φ)P AS(φ)dφ

(27)

−π

We calculate the correlation coefficients matrices in three ways
namely: uniform, truncated Gaussian, and truncated LaplacianPAS shapes [15].
D. Simulation Environment
We have implemented MAC and PHY layers of 802.11ac in
matlab with given parameters as defined in [1]. TABLE II-VII
list the parameters that are used in our theoretical analysis
as well as simulation setup. We do not consider the frame
aggregation scheme for the sake of simplicity. To simulatedifferent features of PHY, we summarize the MCSs into four
tables i.e., TABLES IV-VII for 20 MHz, 40 MHz, 80 MHz,and 160 MHz channels, respectively. The values of MCSs arechosen such that we cover maximum modulation and coding
schemes.
We consider various modulation schemes namely: Quadrature Phase Shift Keying (QPSK), 16-QAM (Quadrature Amplitude Modulation), 64-QAM, and 256-QAM. The coding rate(R) is chosen as 12 , 23 , 34 , and 56 . In the same way, the numberof SS (NSS ) is selected to be 1, 2, 4, and 8. The data rateis calculated from Eq. 13 whereas ACK rate is fixed at basic
rate. We have run each simulation 20 times and have calculated
average values to show stable results.
In order to implement a TGn channel in matlab, we use theparameters from TABLE III. In addition, we use OFDM with
MIMO channel at our transmitter and receiver for our PHY
and channel analysis of 802.11ac.
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

882

60

TABLE IVMCS FOR 20 MH Z C HANNEL
R

1

1/2

QPSK

3

16-QAM

1/2

5
6
7
8

64-QAM
64-QAM
64-QAM
256-QAM

2/3
3/4
5/6
3/4

Nss NDBP S NES
1
4
8
1
4
8
1
1
1
1

52
208
416
104
416
832
208
234
260
312

1
1
1
1
1
1
1
1
1
1

Data rate (Mbps)
800 ns
400 ns
GI
GI
13
14.4
52
57.8
104
115.6
26
28.9
104
115.6
208
231.1
52
57.8
58.5
65
65
72
78
86.7

Total System Throughput (Mb/s)

MCS Modulation

QPSK with Nss = 4 sim
QPSK with Nss = 4 num

55
160 MHz50
45

80 MHz40
35

40 MHz30
25
20 MHz20
0

5

TABLE VMCS FOR 40 MH Z C HANNEL

10

15
20
Number of Stations

25

30

Fig. 1. Throughput for different channels as a function of number of STAs
MCS Modulation

R

1

QPSK

1/2

3

16-QAM

1/2

5
964-QAM
256-QAM

2/3
5/6

Nss NDBP S NES
1
2
4
8
1
2
4
8
1
1

108
216
432
864
216
432
864
1728
432
720

1
1
1
1
1
1
1
1
1
1

Data rate (Mbps)
800 ns
400 ns
GI
GI
27
30
54
60
108
120
216
240
54
60
108
120
216
240
432
480
108
120
180
200

TABLE VIMCS FOR 80 MH Z C HANNELMCS Modulation

R

1

QPSK

1/2

3

16-QAM

1/2

5

64-QAM

2/3

8

256-QAM

3/4

Nss NDBP S NES
1
4
8
1
4
8
1
1
4
8

234
936
1872
468
1872
3744
936
1404
5616
11232

1
1
1
1
1
2
1
1
3
6

Data rate (Mbps)
800 ns
400 ns
GI
GI
58.5
65
234
260
468
520
117
130
468
520
936
1040
234
260
351
390
1404
1560
2808
3120

TABLE VIIMCS FOR 160 MH Z C HANNELMCS Modulation

R

1

QPSK

1/2

3
4

16-QAM
16-QAM

1/2
3/4

5

64-QAM

2/3

8

256-QAM

3/4

Nss NDBP S NES
1
4
8
1
1
1
4
8
1
4
8

468
1872
3744
936
1404
1872
7488
14976
2808
11232
22464

1
1
2
1
1
1
4
8
2
6
12

Data rate (Mbps)
800 ns
400 ns
GI
GI
117
130
468
520
936
1040
234
260
351
390
568
520
1872
2080
3744
4160
702
780
2808
3120
5616
6240

IV. R ESULTS AND D ISCUSSIONS
We calculate aggregate throughput for different number of
STAs and payload size.
A. Wider Channels
In order to evaluate the effect of wider channels on systemthroughput, we calculate total throughput for 20 MHz, 40
MHz, 80 MHz, and 160 MHz channels. We simulate a casethat uses QPSK with Nss = 4, and GI = 400 ns. The
payload size is fixed at 1500 bytes. As shown in Fig. 1,
the total throughput decreases for the all cases of channel
bandwidths as the number of STAs increases. However, thetotal throughput increases with respect to the increase ofchannel bandwidth. Compared to the case of 20 MHz channel
bandwidth, the total throughput is increased by more than
10, 20, and 30 Mb/s in the cases of 40, 80, and 160 MHz
channel bandwidth, respectively. It is important to note that theincrease of total throughput is not proportional to the increaseof channel bandwidth, i.e., less than one quarter increase ofchannel bandwidth. The theoretical (num) results also show asimilar trend.
In a similar way, we observe the effects of available channel
bandwidth on throughput as a function of payload size. For
this purpose, we use a modulation of 16 QAM with one SSi.e., Nss = 1, and GI = 800 ns. In the case of 20/40/80/160
MHz channel, the coding rate of 16QAM can be 1/2 or 3/4(see Table IV-VII). The number of STAs is 20 (i.e., n = 20)
in this set up. Fig. 2 illustrates that total throughput increases
by nearly 10 Mbps as the channel width is doubled.
B. Modulation
The choice of a particular modulation scheme can greatly
affect the system throughput. To observe this result, we fix thenumber of SS to 1 in a 20 MHz channel with GI = 800 ns
and payload size of 1500 bytes. We calculate throughput fordifferent modulation schemes. As illustrated in Fig. 3, QPSK
gives the lowest throughput while 256 QAM produces the
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

60

40

QPSK sim
QPSK num
16 QAM sim
16 QAM num
64 QAM sim
64 QAM num
256 QAM sim
256 QAM num

45

Total Throughput (Mb/s)

50

Total Throughput (Mb/s)

50

16 QAM, Nss=1, 20 MHz sim
16 QAM, Nss=1, 20 MHz num
16 QAM, Nss=1, 40 MHz sim
16 QAM, Nss=1, 40 MHz num
16 QAM, Nss=1, 80 MHz sim
16 QAM, Nss=1, 80 MHz num
16 QAM, Nss=1, 160 MHz sim
16 QAM, Nss=1, 160 MHz num

30
20

883

40
35
30
25
20
15
10

10

5

0

0

500

1000

1500

0
0

2000

500

1000

1500

2000

Payload Size (Bytes)

Payload Size (Bytes)

Fig. 4. Throughput for different modulations as a function of payload size
Fig. 2. The effects of wider channels on throughput for different payload size

55
40

Nss=1, 20 MHz sim
Nss=1, 20 MHz num

Total Throughput (Mb/s)

35
30
64 QAM
25
16 QAM
20
15

QPSK

Nss=8

45
40

Nss=4

35
Nss=2

30
25

10
5

16 QAM in 40MHz sim
16 QAM in 40MHz num

50
Total Throughput (Mb/s)

256 QAM

20
0

5

10

15

20

25

30

Nss=1

0

5

Number of Stations

10

15
20
Number of Stations

25

30

Fig. 3. Throughput of different modulation schemes for different STAs

Fig. 5. The effects of SS on throughput for different number of STAs

highest total throughput. The improvement is 20 Mpbs fromQPSK to 256 QAM.
It is also worth noting that total throughput is greatly
affected by different modulation techniques under different
payload sizes. To notice this fact, the number of STAs is
fixed to 20 while Nss = 1 with QPSK in a 40 MHz
channel and GI = 800 ns. Fig. 4 represents that the total
throughput increases as the payload size is increased. In
addition, the higher order modulation schemes produce higher
throughput. The difference of throughput between different
modulation schemes is increased with increase in payload size.
For instance, when payload size reaches 2000 bytes, there isan elevation of 10 Mbps for each modulation scheme (i.e.,
QPSK, 16 QAM, 64 QAM and 256 QAM).

SSs, however, for thesake of simplicity and comprehension,
we consider Nss = 1, 2, 4, 8 . The results are depicted inFig. 5. It is clear that the total throughput decreases as we
increase the number of STAs for all SSs. Nonetheless, theoverall throughput for a particular number of SS increases by
10 Mbps as the number of SS is doubled. In the same manner,
Fig. 6 represents the total throughput for different antenna
streams. The modulation used is QPSK with GI = 800 ns in
40 MHz channel. The number of STAs is once again fixed at
20. As can be seen in Fig. 6, the total throughput increases as
we increase the payload size. In particular, the total throughput
increases by almost 10 Mbps for every 2Nss .

D. Coding Rate
C. Multiple Spatial Streams
Now, we focus on the effects of different number of transmitting antennas on total throughput. We consider a case of 16
QAM with 40 MHz channel, GI = 800 ns and fixed payload
size of 1500 bytes. Although 802.11ac may support 1 to 8

The relationship between total throughput and different
number of STAs is examined under various coding rate i.e.,
R = 23 , 43 , 56 . The other parameters are set as Nss = 1,
modulation = 64 QAM, and GI = 400 ns in 20 MHz channel.
Fig. 7 shows that the total system throughput increases by 1
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

60

70
QPSK, 40 MHz, Nss=1, sim
QPSK, 40 MHz, Nss=1, num
QPSK, 40 MHz, Nss=2, sim
QPSK, 40 MHz, Nss=2, num
QPSK, 40 MHz, Nss=4, sim
QPSK, 40 MHz, Nss=4, num
QPSK, 40 MHz, Nss=8, sim
QPSK, 40 MHz, Nss=8, num

40

16 QAM, 160 MHz, Nss=1, R=1/2 sim
16 QAM, 160 MHz, Nss=1, R=1/2 num
16QAM, 160 MHz, Nss=1, R=3/4 sim
16 QAM, 160 MHz, Nss=1, R=3/4 num

60

Total Throughput (Mb/s)

Total Throughput (Mb/s)

50

30
20
10
0

884

50
40
30
20
10

0

500

1000

1500

2000

Payload Size (Bytes)

0
0

500

1000

1500

2000

Payload Size (Bytes)

Fig. 6. Throughput for different antenna streams with variable payload size

Fig. 8. Throughput of different coding rate for different payload

34
64 QAM in 20MHz sim
64 QAM in 20MHz num

32

Total Throughput (Mb/s)

R=5/6
30
R=3/4

28
26
R=2/3

24
22
20

0

5

10

15

20

25

30

Number of Stations
Fig. 7. Throughput in different coding rate (R) for different STAs

Mbps as we move to the next available coding rate under the
aforementioned setup.
The influence of coding rate on total throughput as afunction of payload size is illustrated in Fig. 8. We choose
16 QAM in 160 MHz channel with Nss = 1. The number of
STAs is 20 while GI = 800ns. The total throughput increaseby 4 Mbps for R = 34 than R = 12 when the payload size is1500. However, the throughput rise reaches upto 9 Mbps for
R = 43 than R = 12 as payload size is increased to 2000 bytes.
Fig. 8 indicates that coding rate can tremendously affect total
throughput under variable frame size.
E. Analysis under TGn Channel
As illustrated in TABLE III, each TGn channel has adifferent profile. In order to investigate the impact of a channelon MCS, we simulate 802.11ac under channel D and see theperformance in terms of Symbol Error Rate (SER). For thispurpose, we use an OFDM system under a 40 MHz MIMO
multipath Rayleigh fading channel. Accordingly, a 128 point
Fast Fourier Transform (FFT) is used with 128 subcarriersfor 40 MHz.The bandwidth of each sub carrier is 312.5 KHz

for OFDM in 802.11ac. Out of 124 subcarriers, the 114
subcarriers (−58 to 2 and 2 to 58) are used for data andpilot (timing and synchronization). The indices used for pilot
subcarriers are −53, −25, −11, 11, 25, and 53. Subcarriers−1, 0, −1 are used as DC while the remaining 11 subcarriers
are used as left (6) and right (5) guard bands.
The SER of the system is calculated for BPSK, QPSK, 16QAM, 64-QAM and 256-QAM under different Energy per
Symbol (Es /No ) for OFDM transceiver under 40 MHz 2x2
MIMO TGn D channel as shown in Fig. 9. The SER decreases
as Es /No increases for all modulation schemes. However, inlower Es /No , the SER of BPSK is much lower as compared toother modulation schemes. Similarly, QPSK shows lower SER
at higher noise as compared to other high order modulation
schemes. A similar trend can be seen for 16-QAM and 64QAM as compared to 256-QAM. Thus in an error-prone
channel conditions, 802.11ac suffers from almost 50% SER
for 256-QAM as compared to BPSK.
Next, we consider the impact of all TGn channels i.e., (A toF) TABLE III on the performance of 802.11ac network. For
this purpose, we fix all other parameters except the number
of of transmit and receive antennas. This will give us an ideaof how 1x1, 2x1, and 4x4 MIMO changes the performanceof 802.11ac network under different TGn channels. We use
the MIMO OFDM system with 64 point FFT, 20 MHz
channel, 64-QAM, coding rate of 1/2 (i.e., MCS = 3) as
illustrated in TABLE IV. The packet length is set to 1500
bytes. We calculate the Packet Error Rate (PER) to estimate
the performance of MIMO OFDM 802.11ac under different
channels. We sent 1000 packets for each Signal to Noise Ratio(SNR) point that ranges from 0 dB to 50 dB. The distance
between transmitter and receiver is set to 10 m. We also add
Additive White Gussian Noise (AWGN) in order to make arealistic estimation of wireless environment.
We observe that the PER and SNR can be divided into threedifferent levels when the SNR of channel is changed from 0
dB to 50 dB for all the 6 channel models in 1x1, 2x2 and 4x4
MIMOs. This trend can be seen in different configurations oftransmit and receive antennas as shown in Fig. 10-15. Let us
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

1

1

2x2 MIMO OFDM BPSK
2x2 MIMO OFDM QPSK
2x2 MIMO OFDM 16-QAM
2x2 MIMO OFDM 64-QAM
2x2 MIMO OFDM 256-QAM

0.9
0.8

0.6
0.5
0.4

1x1 MIMO Channel A
2x2 MIMO Channel A
4x4 MIMO Channel A

0.9
0.8
0.7

Packet Error Rate

0.7
Symbol Error Rate

885

0.6
0.5
0.4
0.3

0.3
0.2

0.2

0.1

0.1

0
0

0
0

5

10

15
EsNo (dB)

20

25

30

Fig. 9. Symbol Error Rate vs. EsNo for different Modulation schemes

call these three levels as initial level, intermediate level, andfinal level. The PER is explained with the help of Fig. 10-15
and TABLE VIII-X. As shown in Fig. 10-15 and TABLE VIIIX, the PER remains constant at initial and final levels while
it drops down over a range of SNR values at intermediatelevel for all the channel models under 1x1, 2x2 and 4x4
MIMOs. The range of SNR values for the three levels changes
differently for the 6 channel models under different MIMO
configurations.
As illustrated in TABLE VIII-X, the PER is 100% at initiallevel for all the channels and all cases. Thus the system
performance is worst at initial level. However, the range of
SNR points at initial value increases as we increase the numberof transmit and receive antennas. For example: the average
range of SNR (average of A-F) is 0 to 9.6 dB ( TABLE
VIII) , 0 to 14.3 dB ( TABLE IX), and 0 to 20.1 dB (TABLE X) for 1x1, 2x2, and 3x3 MIMOs, respectively. Itshows that the performance of 1x1 MIMO is better than thatof 2x2 MIMO and 4x4 MIMO. Similarly, the performanceof 2x2 MIMO is better than that of 4x4 MIMO in terms ofpacket reception at lower SNR values. In the same way, thePER remains constant at final level for all channel models
and all MIMO configuration. However, the values of PER andthe range of SNR are different for different channels under
different MIMOs. For example at final level, the PER and
SNR of channel model A remains 0%, 11.82%, and 28.94%
at 35-50 dB, 35-50 dB, and 44-50 dB for 1x1 MIMO, 2x2
MIMO, and 4x4 MIMO configurations, respectively. A similar
trend can be found for other channels. On the other hand forintermediate level, the PER drops down from higher values
to lower values over a range of SNR points. The slop andtrend of each drop is different for different channel models
and different MIMO configurations.
Based on our simulation results, we find interesting performance patterns for 802.11ac under different channel models.

5

10

15

20

25

30

35

40

45

50

SNR (dB)
Fig. 10. Packet Error Rate vs. SNR of Channel A under different MIMO
configurations

As shown in Fig. 10-15 and TABLE VIII, the PER of 802.11ac
is 100% at initial level and 0% final level for channel models
A, B, and C under 1x1 MIMO. The PER is 2% and 6%
for channel models D and E, respectively. However, the PER
is 11.70% in case of channel model F which is the worst
performance for 1x1 MIMO for a given configurations.
Similarly, under 2x2 MIMO, the system performs best forchannel models B, C and D where the PER remains at 2% atfinal level for these three channel models as shown in Fig. 1015 and TABLE IX. However, the system performance degrades
to some extent for channel models A, E, and F where the
PER is 11.82%, 6.1%, and 18.03%, respectively. The PER forchannel model F is the maximum (18.03%) as compared toother models. Channel model B relatively provides the best
results under 2x2 MIMO as shown in Fig. 10-15.
The PER of 802.11ac network remains lower for channel
models B, C, and D under 4x4 MIMO as illustrated in Fig.
10-15 and TABLE X. In this case, the performance is worst
for channel model A where PER remains 28.94% at final
level. The system achieves relatively the best performance forchannel model D as compared to the other channel models
under 4x4 MIMO configuration. This can be seen in TABLE
X where the PER is 0%, 84.61%-0.26%, and 0.20% for initial,
intermediate, and final levels, respectively.
V. C ONCLUSION
This paper investigated the performance of 802.11ac interms of system throughput under its several new key features.
A theoretical model was presented that is based on MAC and
PHY layer parameters. It was shown through simulation andtheoretical analysis that the choice of a particular modulationand coding scheme, number of spatial streams, and channel
size can greatly affect the total throughput of system. A MIMO
multipath fading channel model was formulated to further
investigate the effects of new features of 802.11ac. Although
802.11ac increases throughput many fold due to high-order
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

11
1x1 MIMO Channel B
2x2 MIMO Channel B
4x4 MIMO Channel B

0.9

1x1 MIMO Channel D
2x2 MIMO Channel D
4x4 MIMO Channel D

0.9

0.8

0.8

0.7

0.7

Packet Error Rate

Packet Error Rate

886

0.6
0.5
0.4

0.6
0.5
0.4

0.3

0.3

0.2

0.2

0.1

0.1
0

0
0

5

10

15

20

25

30

35

40

45

0

50

5

10

15

20

SNR (dB)
Fig. 11. Packet Error Rate vs. SNR of Channel B under different MIMO
configurations

30

35

40

45

50

Fig. 13. Packet Error Rate vs. SNR of Channel D under different MIMO
configurations
11
1x1 MIMO Channel C
2x2 MIMO Channel C
4x4 MIMO Channel C

0.9
0.8

0.8

0.7

0.7

0.6
0.5
0.4
0.3

0.6
0.5
0.4
0.3

0.2

0.2

0.1

0.1

0
0

5

10

15

20

25

30

35

40

45

1x1 MIMO Channel E
2x2 MIMO Channel E
4x4 MIMO Channel E

0.9

Packet Error Rate

Packet Error Rate

25

SNR (dB)

50

SNR (dB)

0
0

5

10

15

20

25

30

35

40

45

50

SNR (dB)

Fig. 12. Packet Error Rate vs. SNR of Channel C under different MIMO
configurations

Fig. 14. Packet Error Rate vs. SNR of Channel E under different MIMO
configurations

TABLE VIIIPER LEVELS FOR A RANGE OF SNR OF 1 X 1 MIMO-OFDM 802.11 AC
WLAN SYSTEMTABLE IXPER LEVELS FOR A RANGE OF SNR OF 2 X 2 MIMO-OFDM 802.11 AC
WLAN SYSTEM

Channel
model
A
B
C
D
E
F

Parametername
SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)

Initial
level
0-7
100
0-7
100
0-13
100
0-7
100
0-11
100
0-13
100

Intermediate
level
8-34
91.66-2
8-25
91.66-2
14-26
68.75-2
8-25
91.66-8
12-40
91.66-3.2
14-20
68.75-14.10

Final
level
35-50
0
26-50
0
27-50
0
26-50
6
41-50
2
21-50
11.70

Channel
model
A
B
C
D
E
F

Parametername
SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)

Initial
level
0-14
100
0-9
100
0-17
100
0-15
100
0-16
100
0-15
100

Intermediate
level
15-34
78.57-12.79
10-34
91.66-4
18-33
91.66-2.32
16-28
78.57-1.8
17-36
78.57-3.4
16-33
91.66-19.29

Final
level
35-50
11.82
35-50
2
34-50
2
29-50
2
37-50
6.1
34-50
18.03

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

1
1x1 MIMO Channel F
2x2 MIMO Channel F
4x4 MIMO Channel F

0.9

Packet Error Rate

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

5

10

15

20

25

30

35

40

45

50

SNR (dB)
Fig. 15. Packet Error Rate vs. SNR of Channel F under different MIMO
configurations
TABLE X
PER LEVELS FOR A RANGE OF SNR OF 4 X 4 MIMO-OFDM 802.11 AC
WLAN SYSTEM
Channel
model
A
B
C
D
E
F

Parametername
SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)SNR (dB)PER (%)

Initial
level
0-20
100
0-24
100
0-24
100
0-19
100
0-16
100
0-18
100

Intermediate
level
21-43
91.66-32.35
25-35
91.66-4.2
25-35
91.66-1
20-27
84.61-0.26
17-28
84.61-6.3
19-33
84.61-10.78

Final
level
44-50
28.94
36-50
0
36-50
0.8
28-50
0.20
29-50
5.8
34-50
9.32

modulation scheme, more bandwidth and spatial streams, theperformance can also be degraded drastically in an error-prone
channel. We also investigated the performance of 802.11ac
under different TGn channel models to find the performancepatterns of different channel models.
R EFERENCES[1] IEEE 802.11ac-Enhancements for Very High Throughput for operation
in bands below 6 GHz, IEEE P802.11ac/D5.0, 2013.
[2] Wi-Fi
Alliance,
Wi-Fi
certifiedproducts
finder.[Online].
Available:https://www.wi-fi.org/product-finder[3] V. Jones, H. Sampath, “Emerging technologies for WLAN,” IEEE
Communications Magazine, vol. 53, no.3, pp. 141–149, March 2015.
[4] IEEE 802.11n-Part 11: Wireless LAN Medium Access Control (MAC)and Physical Layer (PHY) Specifications: Enhancements for Higher
Throughput, IEEE 802.11n-2009.
[5] Z. Yunze, P.H. Pathak, and P. Mohapatra, “A first look at 802.11ac
in action: Energy efficiency and interference characterization,” IFIP
Networking 2014 Conf., 2-4 June 2014, pp. 1–9.
[6] E. H. Ong et al., “IEEE 802.11ac: Enhancements for very high throughput WLANs,” Proc. IEEE PIMRC, Sep. 2011, pp. 849-853.
[7] C. Zhu et al., “Mac enhancements for downlink multi-user mimotransmission in next generation wlan,” IEEE Consumer Communications
and Networking Conf. (CCNC), Jan. 2012, pp. 832–837.
[8] Timo Vanhatupa, Wi-Fi Capacity Analysis for 802.11 ac and 802.11 n:
Theory and Practice, Ekahau Inc., 2013.

887[9] Oran Sharon, Yaron Alpert, “MAC level Throughput comparison:802.11ac vs. 802.11n,” Physical Communication, vol. 12, September
2014, pp. 33-49.
[10] Minyoung Park, “IEEE 802.11ac: Dynamic Bandwidth Channel Access,”in IEEE International Conference on Communications (ICC) 2011,
pp.1-5, 5-9 June 2011.
[11] Gul Zameen Khan, Ruben Gonzalez, Eun-Chan Park, “A performance
analysis of MAC and PHY layers in IEEE 802.11ac wireless network,” in18th International Conference on Advanced Communication Technology(ICACT), Pyeongchang Kwangwoon Do, South Korea, pp.20-25, 31 Jan- 3 Feb 2016.
[12] Aruba
Networks,
802.11ac
In-Depth.[Online].
Available:
http://www.arubanetworks.com/pdf/technology/whitepapers/
WP 80211acInDepth.pdf[13] G. Bianchi, “Performance analysis of the IEEE 802.11 distributed
coordination function,”IEEE J. Sel. Areas Commun., vol. 18, no.3,
pp. 535–547, March 2000.
[14] Yong Soo Cho, et al., MIMO-OFDM wireless communications with
MATLAB, John Wiley and Sons, 2010.
[15] G. Bianchi, IEEE P802.11 Wireless LANs, TGn Channel Models, IEEE
802.11-03/940r4, 2004-05-10.
[16] V.J. Rhodes, Path loss proposal for the IEEE 802.11 HTSG channel
model Ad Hoc group, April 22, 2003.
[17] J.B. Andersen, T.S. Rappaport, and S. Yoshida, “Propagation measurements and models for wireless communication channels,” IEEE
Commun. Mag., Jan. 1995, pp. 42-49.
[18] L. Schumacher, K. I. Pedersen, and P.E. Mogensen, “From antenna
spacings to theoretical capacities guidelines for simulating MIMO
systems,” in Proc. PIMRC Conf., vol. 2, Sept. 2002, pp. 587-592.
[19] A. Perahia and R. Stacey, Next Generation Wireless LANs - Throughput,
Robustness, and Reliability in 802.11n, Cambridge, 2008.
[20] A. Heck, Introduction to Maple, 3rd ed., New York: Springer-Verlag,
2003.
Gul Zameen Khan received a Bachelor degree in
Computer Systems Engineering from University ofEngineering and Technology Peshawar Pakistan in2007 and a Master degree in Computer Engineeringfrom Hanyang University South Korea in 2011.
He is currently working as a researcher with theIntelligent Wireless Technology and Applications
research group at Griffith University Australia..
He has worked as Lab Engineer in Ghulam Ishaq
Khan Institute of Engineering Sciences and Technology Pakistan. In addition, he has worked as lecturer
in Sarhad University of IT Peshawar Pakistan and COMSATS Institute
Abbottabad Pakistan. He has also worked as a Research Assistant at Wireless
Networks Lab, Dongguk University, South Korea. His areas of interest are
MAC and PHY layers analysis of 802.11, multicast in Wi-Fi Direct, andwireless sensor networks.
Mr. Gul Zameen Khan is a member of IEEE and Institute for Integrated andIntelligent Systems, Griffith University Australia. He is also a professional
member of Pakistan Engineering Council.
Dr. Ruben Gonzalez received a B.E. and PhDfrom the University of Technology, Sydney (UTS)
and is currently a senior Lecturer in the Schoolof ICT, at Griffith University Australia. He is also
the director of the bachelor of computer science at
Griffith University.
Previously he was founder and CTO of ActiveSky
Inc, a wireless media technology company and has
also held research positions at Wollongong University, OTC Ltd Research Labs and UTS. He has
also held software engineering positions at various
private enterprises.
Dr. Ruben Gonzalez is a member of the Institute for Integrated and Intelligent
Systems, Griffith University Australia. He has over 80 refereed publications
and a number of patents.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

888

Prof. Eun-Chan Park received the B.S., M.S.,
and Ph.D degrees from the School of ElectricalEngineering and Computer Science, Seoul National
University, Seoul, Korea, in 1999, 2001, and 2006,
respectively.
He worked for Samsung Electronics, Korea, as asenior engineer from 2006 to 2008. He is currently
an Associate Professor in the Department of Information and Communication Engineering, Dongguk
University-Seoul, Korea. He is also the head of
the Wireless Networks Lab, at Dongguk University
South Korea. His research interests include performance analysis, resource
allocation, quality of service, congestion control, and cross-layer optimizationin wired and wireless networks.
Dr. Eun-Chan Park is a member of IEEE Communications Society.
Dr. Xin-Wen Wu received the Ph.D. degree fromthe Chinese Academy of Sciences, Beijing, China.
He was with the Chinese Academy of Sciences;
the University of California at San Diego, La Jolla,
CA, USA, as a Post-Doctoral Researcher; and the
University of Melbourne, Parkville, VIC, Australia,
as a Research Fellow. He was with the faculty of
School of Information Technology and Mathematical
Science, University of Ballarat, VIC, Australia. He iscurrently with the faculty of School of Information
and Communication Technology, Griffith University,
Gold Coast, QLD, Australia.
Mr. Xin-Wen Wu is a senior member of IEEE. His research interests include
cyber and data security, coding and information theory and their applications,communications and networks. He has co-authored over 70 research papers,
book chapters and two books in the above-mentioned areas.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016889

A Speculative Execution Strategy Based on
Node Classification and Hierarchy Index
Mechanism for Heterogeneous Hadoop Systems
Qi Liu*, Weidong Cai*, Jian Shen*, Zhangjie Fu**, Xiaodong Liu***, Nigel Linge*****Nanjing University of Information Science and Technology, 219 Ningliu Road, Nanjing, Jiangsu, 210044, China** Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science andTechnology, Nanjing, Jiangsu, China***School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK****The University of Salford, Salford, Greater Manchester, M5 4WT, UK
qi.liu@nuist.edu.cn, caiweidongsuzhou@163.com, s_shenjian@126.com, wwwfzj@126.com,
x.liu@napier.ac.uk,n.linge@salford.ac.uk
Abstract—MapReduce (MR) has been widely used to process
distributed large data sets. MRV2 working on Yarn, as a moreadvanced programing model, has gained lots of concerns.
Meanwhile, speculative execution is known as an approach for
dealing with same problems by backing up those tasks runningon a low performance machine to a higher one. In this paper, wehave modified some pitfalls and taken heterogeneousenvironment into consideration. Besides, Node classification isused and a novel hierarchy index mechanism is created. We also
have implemented it in Hadoop-2.6 and the strategy above iscalled Speculation-NC while optimized Hadoop is called
Hadoop-NC. Experiment results show that our method cancorrectly backup a task, improve the performance of MRV2 anddecrease the execution time and resource consumption
compared with traditional strategies.
Keyword—MapReduce; Speculative Execution; Time
Prediction; Node Classification; Hierarchy Index Mechanism———————————————————————
Manuscript received March 23, 2016. This work is a follow up of theinvited journal of the accepted conference paper for the 18th International
Conference on Advanced Communication Technology. This work is
supported by the NSFC (61300238, 61300237, 61232016, U1405254,
61373133), Marie Curie Fellowship (701697-CAR-MSCA-IFEF-ST), Basic
Research Programs (Natural Science Foundation) of Jiangsu Province(BK20131004), Scientific Support Program of Jiangsu Province(BE2012473) and the PAPD fund.
Q. Liu is with the College of Computer and Software, Nanjing Universityof Information Science and Technology, Nanjing, 210044, China (e-mail:qi.liu@nuist.edu.cn).
W. Cai is with the College of Computer and Software, Nanjing Universityof Information Science and Technology, Nanjing, 210044, China(corresponding author to provide phone: +8615251708925; fax:
+86-15251708925; e-mail: caiweidongsuzhou@163.com).
S. Jian is with the College of Computer and Software, Nanjing Universityof Information Science and Technology, Nanjing, 210044, China (e-mail:s_shenjian@126.com).
Z. Fu is with the Jiangsu Engineering Centre of Network Monitoring,
Nanjing University of Information Science and Technology, Nanjing,
210044, China (e-mail: wwwfzj@126.com).
X. Liu is with School of Computing, Edinburgh Napier University, 10
Colinton Road, Edinburgh EH10 5DT, UK (E-mail: x.liu@napier.ac.uk).
N. Linge is with the School of Computing, Science and Engineering,
University ofSalford,Salford,
M5 4WT, UK (E-mail:n.linge@salford.ac.uk).

I. INTRODUCTION

T

HE time that data grow bigger and bigger is coming. The
fast development of Cloud Computing makes that big
data can be faster and more easily processed [1]. Nimbus
provides a high quality of extensible architecture, which
supports Xen and KVM virtual machine, and can be used as a
PBS resource scheduler. OpenStack is an open source cloudcomputing platform designed by Rackspace and National
Aeronautics and Space Administration (NASA) for public
and private cloud. It is compatible with Amazon's EC2 and S3,
and can provide the same service as the Amazon [2]. Sector
and Spheres [3] is another open source platform designed
implemented by Gu, which is used to process geographicallydispersed large data sets in parallel. Hadoop cloud computing
is one of the most popular open source cloud platforms, it ismainly supported by Yahoo, and applied to the Facebook andAmazon, Twitter, Baidu and other companies like IBM andthe New York times. At present, in the latest Release version
is 2.7.1. In 2010, Facebook announced that it has the largest
Hadoop cluster. In 2011, its data storage space has reached 30
PB.
MapReduce, proposed by Google, which is one of the mostimportant parts in Hadoop, has been the most popular
distribute programming model in a cloud environment. With
map and reduce procedures used in the cloud infrastructuretransparently, those datasets can be processed easily andefficiently.
Many enterprises and companies obtain a large amount ofbusiness profits through analyzing and dealing with a lot of
new data in time [4]. Data analysis application may havedifferent complexity, resource requirement and data delivery
deadlines. As a result, this diversity creates new requirementof job scheduling, workload management and program design.
Although different users have different goals, a similar goal is
to improve the performance of the framework. Several
projects have been launched to relieve the difficulty in writing
complex data analysis or data mining programs, e.g., Pig [5]and Hive [6] built above the MapReduce engines in the
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

Hadoop environment.
However, cloud systems suffer from poor load-schedulingstrategies, which can consume much more execution time andtemporary space than expected. While theoretically infinite
computing resources can be provided in a cloud,unreasonable increment of mappers/reducers cannot achieve
process efficiency, and may waste more storage to complete.
Many optimization schemes have been proposed [7-13].
II. RELATED WORK

Hadoop-2.6, the biggest pitfall is that situation ofheterogeneous environment is not well concerned.
III. OUR STRATEGY
A. Remaining Time Estimation of Current Task
Remaining time of the current task can be calculated byadding the remaining time of current phase and following
phases. Detailed method is shown in (1) (2) and (3). factord
represents the volume of current input data and average valueof historical tasks. where Trem represents the remaining timeof current task, which consists of the remaining time ofcurrent phase and following phases, marked as Tcp andT fp . Avg p includes the average consumption of shuffle
phase, sort phase and reduce phase on some node. While thetype of current task is map task, the remaining time only
includes map phase.

Trem  Tcp  Tfp
Trem 

rem _ datacp
bandwidthcp

(1)

 Avg * factor(2)

datainput

(3)

p

p in

factord pfp

dataavg

While there is no historical information on the node, global
historical data is used to calculate the average finishing timeof current phase
B. Time Prediction of a Backup Task
When a task finishes, our strategy automatically stores taskrunning time on some node. If current task is a map task,
stored data would be hostname and average duration.
Otherwise, while it is a reduce task, duration of per-phasewould be written down and the duration of whole phase would
be recorded at the same time. We have established a storage
mechanism to help us store information while having a low
space occupation. The detailed storage method is shown in
Fig.1.

Data Sets

Node 1
Node 2Map
ReduceMap
Reduceshufflesort
reduceNode 3...

General solutions on performance evaluation and load
efficiency in a cloud system have been examined andpresented. PQR2, an approach to accurate performance
evaluation of distributed application in a cloud was presented[8]. Jing et al. presented a model that can predict resource
consumption of MapReduce processes based on aclassification and regression tree [9].
Mars were developed to run on NVIDIA GPUs, AMD
GPUs as well as multicore CPUs and implemented in Hadoop.
Mars hides the programming complexity of GPUs behind thesimple and familiar MapReduce interface, and automaticallymanages task partitioning, data distribution, andparallelization on the processors. Six representative
applications also have been implemented on Mars The
experimental results show that integrating Mars into Hadoop
enabled GPU acceleration for a network of PCs [10].
However, the implementation is much more complex while ina cluster, the performance of GPUs and CPUs.
PrIter, a distributed computing framework was developed.
The prioritized execution of iterative computations is
supported in it. PrIter either stores intermediate data inmemory for fast convergence or stores intermediate data infiles for scaling to larger data sets. PrIter achieves up to 50 ×speedup over Hadoop for a series of iterative algorithms. In
addition, PrIter is shown better performance for iterative
computations than other state-of-the-art distributed
frameworks such as Spark and Piccolo [11]. But, the biggest
pitfall is that it only adapts to iterative algorithms and cannot
be applied to all algorithms.
Besides the above methods, some researchers are studying
optimizing the speculative execution strategy in MapReduce.
Zaharia et al. [12] proposed a modified version of speculative
execution called Longest Approximate Time to End (LATE)
algorithm that uses a different metric to schedule tasks for
speculative execution. Instead of considering the progress
made by a task so far, they compute the estimated timeremaining, which gives a more clear assessment of a
straggling tasks’ impact on the overall job response time. But
the time every stage occupies is not stable and the std
representing standard deviation used in LATE cannot
represent all cases. To solve the disadvantages in LATE,
MCP [13] was proposed by QI CHEN et al. MCP uses
average progress rate to identify slow tasks while in reality the
progress rate can be unstable and misleading. Straggles can beappropriately judged when there is data skew among the tasks.
However, there are still a lot of pitfalls in MCP. Moreover, it
can only be used in MR, not including MRV2.
All research works above have devoted themselves toparticular and/or comprehensive load and usage efficiency ina distributed environment. However, in a Heterogeneousenvironment, reasonable scheduling is still a hard problem. In
LATE, MCP or the newest speculative execution strategy in

890

Node nFig.1 Hierarchy Index Mechanism
As show in Fig.1, a data set is found according to hostname
at the beginning of storage procedure. In a same data set, data
are collected from the task running on the same host. Everystorage area is further divided in two sub storage areas called
Map and Reduce. If the task finished just now is map type, therunning time is directly recorded in Map. Moreover, while itis a reduce task, detailed running information, containing
shuffle time, sort time and reduce time, is respectively written

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

in 3 sub blocks. Finally, summary information of last level(shuffle sort or reduce) is stored in its up level: Reduce. We
need to search some data, a binary search is started based on
index mechanism.
While detailed average running time of a task on somenode needs to be known (such as Avg shuffle , Avg sort or

Avg reduce ), hostname is first gotten. If a data set hostname
mapping to is empty, overall running time of different hosts
would replace the detailed one. Otherwise, average time ofsome phase is directly obtained. Detailed algorithm is shownin Algorithm 1.
Algorithm 1 Get the estimated running time of a backup task
Input: TaskId Tid ,NodeId Nid ,TaskType Type
1 Get hostname according to Tidof current task
2 Get the data set that hostname mapping to
3 If the data set is empty
4
If Type equals Map

6
7

Get the average running time of different hosts, Recorded as
Avg map , Tback  Avg map
Else if Type equals Reduce
Get the per phase average running time of different hosts,
Recorded as Avg
Avg and Avg
shuffle

T

 Avg

 Avg

personal computers is equipped with 12GB of memory, asingle 500GB disk and four Intel(R) Core(TM) 2.4GHz
two-core processors. On the server, we run eight virtual
machines and each virtual machine is given different amountsof memory and different number of processors. The detailed
information is shown in Table I in Section IV.
Then, the virtual machines run as the data nodes and theserver run as the name node. To evaluate the performance ofload strategy, we use WordCount, Sort and Grep. They arecommon applications used in MRV2 to evaluate strategy. The
Purdue Benchmarks Suite provides us with this application
workload and we use the free datasets [14] as the workloads
input. Detailed inputs of these applications are shown in
Table II.
TABLE ITHE DETAILED INFORMATION OF EACH VIRTUAL MACHINE
NodeIdMemory(GB)Core processors

Output: Running time of a backup task Tback

5

891

Node1

10

8

Node2

8

4

Node3

8

1

Node4

8

8

Node5

4

8

Node6

4

4

Node7

18

4

Node8

12

8

TABLE IIINPUT OF THESE APPLICATIONS
WordCount Sort
50
30
Input(GB)Grep30

reduce

sort

 Avg

back
shufflesort
reduce8
9
End If
10 Else11 If Type equals Map
Get the average running time of the same host as Avg

map

and

Tback  Avg map
13 Else14
Get sum of per phase average running time of same host, Recorded
as Tback
15 End If
16 End If
17 Return Tback

C. Node Selection and Task selection
Node selection is import for that data-local would help
framework execute job faster. Besides, node to be selectedshould have relatively good performance at that time. The
object of task selection is judging whether a task runs on apoor performance node. Also, in our strategy, the condition ofstart a back task is continuously changing according to (4).
That is to say, if current progress is 50% and Trem equals 15
seconds, Tback must smaller than 10, then the backup task
would start. Once a backup task starts, we kill the native task
immediately.

Trem Condition  1  TaskProgress
Tback

Number of Mappers

200

200

200

Number of Reducers

16

15

15

The evaluation method is this paper can be expressed as theEq. (5), which represents the different between anotherstrategy and our Hadoop-NC divided by another strategy.

Improvement 

OtherStrategy  NC
OtherStrategy(5)

A. Evaluation of Node Classification
To prove Node of classification is reasonable, we first ran
WordCount for 5 times under the circumstance that
speculative execution is disable. Then we collected therunning information of map phase, and average consuming
time was calculated.
Fig.2 shows average consuming time of each node to finish
a task. Difference is obvious and tasks running on node3
consume much longer time than others. This proves that it isunreasonable to use mean consuming time to calculate.

(4)

IV. EXPERIMENT AND ANALYSISIn order to test the performance and benefits of our loadbalancing strategy, we simulate a real environment. Test-bed
is made up of a personal computers and a server. The server isequipped with 288 GB of memory, a 10 TB SATA driver. The
Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016892

B. Evaluation of Hierarchy Index Mechanism
In this part, we will evaluate it from the prosperity of timecomplexity.
If we put all running information into a same list, then,when we search the list for a piece of data, the timecomplexity of search would be O(k) (k represents the numberof all pieces of data). However, in our storage mechanism, if
we successfully find data, average search time can be cansimplified as log2(n)+k/n (n is the number of nodes). So theaverage time complexity can be simplified as O(log2(n)).
Obviously, our hierarchy index mechanism can get a better
performance, which has a smaller time complexity.
Fig.2 Average Consuming Time of Each NodeFig.3 shows detailed running time of tasks on node1 andnode8. This picture is depicted by 24 groups of sample s fromall samples. Although lots of peaks in the figure, if we replacethese data with mean value, these 2 lines would be relatively
smooth. Those points have been smoothed can be seen as
tasks generated by soft straggler, as shown in Fig.4.

C. Overall Evaluation of PerformanceFig.5 shows for WordCount case, NC finishes jobs 12%
faster than Hadoop-Original and 20% faster than
Hadoop-None on average.
Fig.6 shows that on average, NC finishes jobs 3% fasterthan Hadoop-Original and 19% faster than Hadoop-None. Itis very terrible that Original speculative execution strategy
has poor performance, but our Hadoop-NC still can reduce
the execution time.
Fig.7 shows for Grep case, NC finishes jobs 18% fasterthan Hadoop-Original and 10% faster than Hadoop-None on
average.

Fig.3 Detailed Running Time of Tasks on node1 and node8

Fig.4 Data After Smoothing
Concluded from Fig.3 and Fig.4, peaks are in Fig.3 canbeen seen as software straggles. And in figure.4, we assume
that this the straggles have been processed and these tasks
have been transferred in other nodes. Then, the tendency oftime that tasks running on the same node is relative stable. So,
we our method classify the time according to node isreasonable. Based on these, further experiments wereoperated, we used WordCount and Sort to evaluate our
performance, which can respectively represent 2 types ofapplication that one consumes CPU and the other is moresensitive to Memory.

Fig.5 Job Execution Time of WordCountTo analyze the reason why Original strategy has worse
performance, we calculated the running information of these
two applications under different strategies and drew a table.
As shown in Table III. For WordCount, the backup successrate of Hadoop-NC is 20% and 16.7% higher than
Hadoop-Original for map and reduce tasks. For Sort, thebackup success rate of Hadoop-NC is 24.3% and 30.7%
higher than Hadoop-Original. For Grep, the backup successrate of Hadoop-NC is 30% higher than Hadoop-Original.
Also, resource consumption in cloud environment is also
an important indicator to evaluate the performance of PaaS
platform. Traditional speculative strategies only evaluate theperformance from the prosperity of job execution time.
However, resource consumption is usually ignored. In thispaper, resource consumption is represented the number ofcontainers and the time of the container occupied. So an Eq. isgotten as shown in (6).

Consumption  Containers * Seconds(6)

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

893

TABLE IIIDETAILED COMPARISON

Workloads
WordCount
Sort
Grep

Sum of Backed-up

Sum of Successful

Map Tasks

Backed-up Map Tasks

Hadoop-Original

85

40

47.1

Hadoop-NC

55

38

69.1

Hadoop-Original

84

24

28.6

Hadoop-NC

70
68
35

37
32
27

52.9
47.1
77.1

Strategy

Hadoop-Original
Hadoop-NC

Backup success rate (%)

Fig.8 Resource consumption
Fig.6 Job Execution Time of Sort

I. CONCLUSION
In this paper, a new strategy called Speculation-NC isintroduced and implemented in Hadoop-2.6. Experimentresults have shown that our method can relatively save timeand resource for WordCount sample. However, there is stillmuch work can be done to improve the performance of MRV2for Sort sample further, both in execution and resource
consumption.
REFERENCES[1]

Fig.7 Job Execution Time of Grep
When it comes to resource consumption, shown in Fig.8,
our strategy has an obvious improvement for WordCount
samples, it save about 10% and 13% than Hadoop-None andHadoop-Original. However, for Sort samples, although ourstrategy has an improvement over Hadoop-Original, moreresource than Hadoop-None is consumed. It is very normalthat if the speculative strategy cannot find straggle on time.
For Gerp, because it consumes little time and the saved
resource is not very obvious, but our strategy still has an
improvementcomparedwith
Hadoop-NoneandHadoop-Original.

[2]

[3]

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

[8]

M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A. Konwinski
and M. Zaharia, " A view of cloud computing," Communications of theACM, vol. 53, no. 4, pp. 50-58, 2010.
X. Wen, G. Gu, Q. Li, Y. Gao and X. Zhang, “Comparison of
open-source cloud management platforms: OpenStack andOpenNebula,” In Proceedings of the 2012 9th International
Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.
2457-2461, 2012.
Y. Gu and L. R. Grossman, “Sector and Sphere: the design andimplementation of a high-performance data cloud,” PhilosophicalTransactions of the Royal Society of London A: Mathematical,
Physical and Engineering Sciences, vol. 367, no. 1897, pp. 2429-2445,
2009.
J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processingon Large Clusters,” Communications of the ACM, vol. 51, no.1, pp.
107-113, 2008.
Apache pig. <http://pig.apache.org/> [accessed on 14.09.15].
Apache hive. <https://hive.apache.org/> [accessed on 14.09.15].
Z. Fu, X. Sun, Q. Liu, L. Zhou and J. Shu, “Achieving Efficient Cloud
Search Services: Multi-keyword Ranked Search over Encrypted Cloud
Data Supporting Parallel Computing,” IEICE Transactions on
Communications, vol. 98, no. 1, pp. 190-200, 2015.
A. Matsunaga and J. Fortes, “On the use of machine learning to predict
the time and resources consumed by applications,” Proceedings of the10th IEEE/ACM International Conference on Cluster, Cloud and Grid
Computing, pp. 495-504, 2010.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016
W. Fang, B. He, Q. Luo and N. K. Govindaraju, "Mars: accelerating
mapreduce with graphics processors," IEEE Transactions on Paralleland Distributed Systems, vol. 22, no. 4, pp. 608-620, 2010.
[10] Y. Zhang, Q. Gao, L. Gao and C. Wang, “Priter: a distributed
framework for prioritizing iterative computations,” IEEE Transactions
on Parallel and Distributed Systems, vol. 24, no. 9, pp. 1884-1893,
2014.
[11] Piao, J. Tai and J. Yan, “Computing resource prediction for mapreduce
applications using decision tree,” In Web Technologies andApplications, pp. 570-577, 2012.
[12] M. Zaharia, A. Konwinski, A. Joseph, R. Katz and I. Stoica,“Improving
Mapreduce
Performancein
Heterogeneous
Environments,” OSDI, vol. 8, no. 4, 2008.
[13] C. Qi, C. Liu and Z. Xiao, “Improving MapReduce performance using
smart speculative execution strategy,” IEEE Transactions
on Computers, vol. 63, no. 4, pp. 954-967, 2014.
[14] F. Ahmad, S. Chakradhar, A. Raghunathan and T. Vijaykumar,“Tarazu: optimizing MapReduce on heterogeneous clusters,” ACM
SIGARCH Computer Architecture News, pp.61-74, 2012.

894

[9]

Nigel Linge received his BSc degree in Electronics fromthe University of Salford, UK in 1983, and his PhD in
Computer Networks from the University of Salford, UK,
in 1987. He was promoted to Professor of
Telecommunications at the University of Salford, UK in
1997. His research interests include location based andcontext aware information systems, protocols, mobile
systems and applications of networking technology inareas such as energy and building monitoring.

Qi Liu (M’11) received his BSc degree in Computer
Science and Technology from Zhuzhou Institute ofTechnology, China in 2003, and his MSc and PhD in
Data Telecommunications and Networks from the
University of Salford, UK in 2006 and 2010. His
research interests include context awareness, data
communication in MANET and WSN, and smart grid.
His recent research work focuses on intelligent
agriculture and meteorological observation systems
based on WSN.
Weidong Cai received her bachelor's degree in
Software Engineering from Nanjing University ofInformation Science and Technology in 2014, and heis pursuing a master's degree in software engineering at
the Nanjing University of Information Science andTechnology. Hi research interests include Cloud
Computing, Distributed Computing and Data Mining.

Jian Shen received his bachelor's degree in Electronic
Science and Technology Specialty from Nanjing
University of Information, Science and Technology in2007, and he received his masters and PhD in
Information and communication from CHOSUN
University, South Korean in 2009 and 2012. His
research interests includes Computer network security,
information security, mobile computing and network,
wireless ad hoc network.
Zhangjie Fu received his BS in education technology
from Xinyang Normal University, China, in 2006;
received his MS in education technology from theCollege of Physics and Microelectronics Science, Hunan
University, China, in 2008; obtained his PhD incomputer science from the College of Computer, Hunan
University, China, in 2012. Currently, he works as an
assistant professor in College of Computer and Software,
Nanjing University of Information Science and Technology, China. His
research interests include cloud computing, digital forensics, network andinformation security.
Xiaodong Liu received his PhD in Computer Sciencefrom De Montfort University and joined Napier in 1999.
He is a Reader and is currently leading the Software
Systems research group in the IIDI, Edinburgh Napier
University. He was the director of Centre for Information& Software Systems. He is an active researcher insoftware engineering with internationally excellent
reputation and leading expertise in context-aware
adaptive services, service evolution, mobile clouds,
pervasive computing, software reuse, and green software engineering. He
has meanwhile a successful track record of teaching in a number of software
engineering courses which are widely informed by his research activities.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

895

A Comparison Study of the Quick Removing(QR) Approach (HSR Mode X) under
Cut-Through and Store and Forward Switching
Modes
Saad Allawi NSAIF *, Semog KIM*, Jong Myung RHEE**Dept. of Information and Communications Engineering, Myongji University, Yongin-si, South Korea
saad.allawi1@gmail.com, kimsemog@empas.com, jmr77@mju.ac.kr
Abstract—We previously introduced a quick removing (QR)
approach (currently part of the IEC 62439-3 standard under theterminology of “HSR Mode X”) to improve the high-availability
seamless redundancy (HSR) protocol’s traffic performance. The
idea of the QR approach is to remove the duplicated frame
copies from the network when all nodes have received one copyof the sent frame and begin to receive the second copy.
Therefore, forwarding the frame copies until they reach thesource node, as occurs in a standard HSR, is not needed in a QR.
In our previous paper, we applied the store and forward
switching mode, whereas in this paper, we present theperformance of the QR approach using a cut-through switchingmode. The performance analysis showed a reduction percentage
in frame latency that reaches about 49% compared to the store
and forward switching mode. Consequently, this will improve
network performance, free more bandwidth, and quickly deliver
sent frames to their required destinations, which is a firm
condition in many industrial and automation applications.
Keyword— HSR protocol, QR approach, HSR Mode X, HSR
traffic performance, cut-through, seamless redundancy, IEC
62439-3.

I. INTRODUCTIONM

ODERN control and time-critical systems typicallyrequire seamless communication networks. This meansthat some form of fault tolerance in the communications
network is required to achieve satisfactory system———————————————————————
Manuscript received April 28, 2016. This work was supported by theTechnology Innovation Program (No.10058109) and was funded by the
Ministry of Trade, Industry, and Energy (MI, Korea). It was also supportedby the Basic Science Research Program through the National Research
Foundation of Korea (NRF) and was funded by the Ministry of Education(No. NRF-2015R1D1A1A02059506).
Saad Allawi NSAIF is with the Information and Communications
Engineering department, Myongji University, yongin, Gyeonggi-do
449-728, South Korea. (Phone/fax: +820-31-330-6824; e-mail:saad.allawi1@gmail.com)
Semog KIM is with the Information and Communications Engineering
department, Myongji University, yongin, Gyeonggi-do 449-728, South
Korea. (Phone/fax: +820-31-330-6824; e-mail: kimsemog@empas.com)
Jong Myung RHEE is with the Information and Communications
Engineering department, Myongji University, yongin, Gyeonggi-do
449-728, South Korea. (Corresponding author, Phone:+820-31-330-6886;
fax: +820-31-330-6824 ;e-mail: jmr77@mju.ac.kr)

availability. High availability, which can be achieved throughredundancy and fault tolerance, can prevent a system frommomentary interruptions that may be considered inconvenientand may cause critical stoppage in some industrial
applications.
Generally, seamless communication with fault tolerance is
one of the key requirements for Ethernet-based
mission-critical systems, such as substation automation
system (SAS) networks. A fault-tolerant Ethernet (FTE)eliminates the single point of failure and therefore improves
the overall system availability [1]. Since the standard Ethernet
does not provide a fault-tolerance capability, various FTE
protocols for power applications have been developed [2].
Among these, only two protocols, the parallel redundancy
protocol (PRP) and the high-availability seamless redundancy(HSR) protocol, which is standardized as IEC 62439-3, aresuitable for seamless communication [2-4]. Both HSR and
PRP are based on the same principle of active redundancy by
duplicating the information frames, therefore resulting in azero-delay reconfiguration in the event of a switch or link
failure; however, only the HSR protocol is addressed in thispaper because it is generally accepted as the developed
version of PRP. The HSR protocol is a redundancy protocol
for Ethernet-based networks, and it provides duplicated frame
copies for each frame sent. In other words, the HSR protocol
provides two frame copies for the destination node, one fromeach side, enabling zero-fault recovery time in case one of theframe copies is lost. This means that even in the case of a node
failure or a link failure, there is no stoppage of network
operations. If both sent copies reach the destination node, thenode will take the fastest copy and discard the other copy.
This feature of the HSR protocol makes it very useful fortime-critical and mission-critical systems.
Generally, the HSR-based network has four types of nodes[5]:
x A doubly-attached node for HSR (DANH), which has
two HSR ports sharing the same medium access control(MAC) and Internet protocol (IP) set addresses. This allows
the address management protocols, such as the address
resolution protocol (ARP), to operate as usual without
modification, which simplifies network engineering. Each
DANH node will duplicate a non-HSR frame that is generated
at the upper layer into two frame copies. It will then append an

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

HSR tag to each copy and send two copies out through DANH
ports, one in a clockwise direction and the other in acounter-clockwise direction;x A single-attached node (SAN), which is a non-HSR
device, such as a commercial printer, server, or laptop. It
cannot be directly inserted into the HSR networks because it
lacks the forwarding capability of an HSR node and does notsupport the HSR tag. They must be attached through aredundancy box (RedBox) node type;x A RedBox node, which has three ports. Two ports are
HSR ports, and the third one is an Ethernet port that any SAN
device, such as a PC, can be plugged into to be engaged with
the HSR network. The RedBox node forwards the frames overthe HSR network like any other HSR node and acts as a proxy
for all SANs that access it. Thus, it must keep track of all
traffic on behalf of the SANs. The RedBox can also act as aswitch for the SANs. Therefore, it is somewhat more complex
than HSR nodes;
x A quadruple port device (QuadBox node), which has
four HSR ports. Each pair shares the same MAC and IP
addresses. This type of node is used to connect two rings or
networks. The QuadBox node removes duplication andperforms additional tasks, such as multicast and virtual local
area network (VLAN) filtering.
The primary drawback of the HSR protocol is theunnecessary traffic due to the duplicated frames that are
generated and circulated inside the network. If a typical HSR
is applied, traffic will be doubled at each QuadBox node type.
The situation becomes worse when the HSR is used for ageneric object-oriented substation event (GOOSE) andsampled values (SV) messages’ transmissions, which
generate large amounts of traffic. This downside will degrade
network performance and may cause network congestion oreven stoppage. Therefore, data traffic control is essential for
maximizing network operational performance [6]; however,
due to the potential advantages of HSR, several works havealso focused on implementing the HSR [7-9] and improving
its performance by reducing its unnecessary redundant traffic[10-15]. To address this issue, we have proposed an HSR
approach called quick removing (QR) [10] to remove theunnecessary redundant traffic from the network instead ofallowing it to circulate inside the network and consume its
bandwidth. The idea of the QR approach was to remove theduplicated frame copies from the network when all nodes
have received one copy of the sent frame and began to receive
the second copy. Due to the high efficiency of our QR
approach, other papers and research studies have referred andreported on the QR approach, such as [16,17]. Consequently,
the International Electrotechnical Commission (IEC) has
included the QR approach in the newest edition of the IEC
62439-3 standard, Ed. 03- 2016, under the terminology of
HSR mode X.
Nevertheless, in [10], we only considered the store andforward switching mode in which the entire frame needs to be
stored in the ingress of the node before the node can forward
it. Obviously, the node will require more time to forward theframe because the CRC code must be calculated andcompared with the code appended to the frame to ensure thatthe received frame is error-free before sending it to the upper
layers or forwarding it to the next nodes. In this case, as soon
as the receiving node realized that it is the destination node of896

the received frame, the receiving node will wait to receive andread the source MAC address of the received frame. Then, thenode will check its received frames table to see whether it has
already received a copy from that frame. If the node does notfind the source MAC address in its table, then it will buffer all
received bytes until the reception of all is complete, and then,calculate the CRC code and compare it to the frame’s CRC. Ifthey are equal, the node will send that frame to the upper
layers and update its received frames table; otherwise, it will
delete it.
If the node does find the received source MAC address in
its received frames table, then it will clearly, under this type ofswitching node, the node will require less time to forward theframe compared to the time used for the store and forward
type. In other words, the cut-through mode will be much fasterthan the store and forward mode [18].
In this paper, we discuss the HSR node’s behavior underthe QR approach using the cut-through switching mode.
Previously, we introduced this in [19], but in this paper, we
add more details to the performance analysis of the QR
approach under this type of switching mode.
The remainder of the paper is organized as follows. In
Section II, we introduce our QR approach behavior under the
cut-through switching mode. In Section III, we describe theanalytical performance analysis of the QR approach under the
cut-through switching mode. Section IV shows the results ofthe simulation analysis. Finally, in Section V, we summarize
our work and present our conclusions.

II. QR APPROACH UNDER THE CUT-THROUGH
SWITCHING MODE
QR is an approach used to improve the traffic performanceof the standard HSR protocol. It can be applied to any
closed-loop network topology for all traffic types, especiallythe multi/broadcast type. The QR approach aims to remove
the redundant frame copies from the network when all nodes
have received one copy of the sent frame and begin to receive
the second copy; however, the QR approach will only beapplied to the data HSR frame types. Exempt from that the
HSR supervisory frame type to allow the network nodes tocheck the network status of redundancy. The HSR node willrecognize whether the received frame is a supervisory or adata frame through the bit pattern of the 4-bit path field of thereceived frame. The bit pattern of the supervisory frame typeis 1111, whereas the data HSR frames have a bit pattern in therange of 0001-1001.
The QR approach does not need to use any special control
frames; however, in [10], we discussed our QR approach
using the store and forward switching mode only, whichshowed that the QR approach can reduce the unnecessary
redundant network traffic in HSR networks up to 50%, as
illustrated in Fig. 1.
In this paper, the application of the cut-through switchingmode in the QR approach is described, which provides fastertransmission and frees more bandwidth. Under the store andforward switching mode, the QR nodes wait to receive all of
the frame’s bytes and to calculate the CRC code to ensure thatthe received frame is error-free before deciding to send it to

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

Fig. 1 Traffic reduction percentage (RP) of QR to the standard HSR
protocol with respect to the number of nodes in a single ring.

the upper layers or forward it to the next nodes; however,under the cut-through switching mode, when a node receives aframe, it reads the first 6 bytes, or the destination MAC
address. Thereafter, the node takes the following actions
according to the type of destination MAC address.
A. Unicast MAC Type (The Receiving Node is the
Destination Node for The Received Frame)
In this case, as soon as the receiving node realized that it isthe destination node of the received frame, the receiving node
will wait to receive and read the source MAC address of thereceived frame. Then, the node will check its received frames
table to see whether it has already received a copy from thatframe. If the node does not find the source MAC address in its
table, then it will:
1) Buffer all received bytes until the reception of all iscomplete, and then,2) Calculate the CRC code and compare it to the frame’s
CRC. If they are equal, the node will send that frame tothe upper layers and update its received frames table;
otherwise, it will delete it.
If the node does find the received source MAC address in
its received frames table, then it will:
3) Wait to receive and read the HSR frame’s header tocheck the sequence number if it is in the received frames
table. If it is, then the node will delete the bytes that havebeen received thus far and all remaining bytes that will bereceived sequentially; otherwise, it will:
4) Buffer all received bytes until the reception of all iscomplete, and then,5) Calculate the CRC code and compare it to the frame’s
CRC. If they are equal, the node will send that frame tothe upper layers and update its received frames table;
otherwise, it will delete it
B. Unicast MAC Type (The Received Frame is Heading toother Node)If the received frame is a unicast type and is heading to anode other than the receiving node, or in other words, theframe is passing through the receiving node and going toanother node, the receiving node will:
1) Look for that destination MAC address of the received
frame in its forwarded frames table. If the address is notin the table, then the node will sequentially forward all897

received bytes into the other node’s ports and then update
its forwarded frames table.
2) If the destination MAC address is in the table, the node
will wait to receive and read the source MAC address of
the received frame and look up the table again for thatpair of addresses. If the address pair does not exist, then
the node will sequentially forward all received bytes into
the other node’s ports and then update its forwarded
frames table; however, if that address pair is in the table,
the node will wait to receive and read the HSR header ofthe frame to see whether it has already forwarded thesame frame. If the node finds that it has forwarded aframe with the same destination, source MAC address,and HSR header, then the node will delete the received
bytes and all remaining bytes of that frame as soon as
they reach the node. Otherwise, the node willsequentially forward all received bytes into the othernode’s ports and then update its forwarded frames table.
C. Multi/broadcast MAC TypeThe receiving node will wait to receive and read the source
MAC address to determine whether the node has alreadyreceived a copy of that frame. If the source node of that frame
is not in the received frames table, then the node will:
1) Make a copy of each received byte, buffer it, and thenforward the original bytes into the other ports.
2) When all bytes of that frame are received, the node willcalculate the CRC code and compare it to the frame’s
CRC code. If they are equal, the node will send that frameto the upper layers and update its received frames table.
Otherwise, it will delete it.
If the source MAC address is shown in the received frames
table, the node will wait to receive and read the HSR frame’s
header. If the node shows that it has already received a copyof that frame, then the node will delete the received bytes andstop making a copy of each received byte of that frame; but it
will only forward these bytes into the other ports if the node
has not did that for that received frame.
If the source MAC address is not shown in the received
frames table, then the node will:
1) Make a copy of each received remaining byte of thatreceived frame, buffer it, and then forward the original
bytes into the other ports.
2) When all bytes of that frame are received, the node willcalculate the CRC code and compare it to the frame’s
CRC code. If they are equal, the node will send that frameto the upper layers and update its received frames table.
Otherwise, it will delete it.
Fig. 2 includes a flow chart that summarizes the QR steps
under the cut-through switching mode.

III. ANALYTICAL PERFORMANCE ANALYSIS
In this section, we will demonstrate the performance of the
QR approach under the cut-through switching modecompared to the store and forward mode from the point ofview of the frame latency using the analytical approach. Let us
consider the network shown in Fig. 3 as an example for our
performance analysis; however, assume that node C has sent a
unicast frame to node G. The following expression represents
the latency of the frame from the time at which the sending

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

898

§f· ª
§ h · § f ·ºl ¨¨ ¸¸  « n  2 ¨¨ ¸¸  ¨¨ ¸¸»E© ¹ ¬© U ¹ © U ¹¼

tc

(1)

where l is the number of network links, f is the frame size
in bits, E is link capacity in bits per second, n is the numberof nodes in the path of the sent frame, h is the frame header
size in bits, and U is the node processing rate. h might be 48
bits when the received node only reads the destination MAC
address of the received frame, or it might be 96 bits or even144 bits if the destination MAC, the source MAC, and the
HSR frame header are read.
The term (n  2) represents the exclusion of the source anddestination nodes from the calculation of the delay time on the
§f·
¨¨ ¸¸

path of the sent frame, whereas © U ¹ represents the delay timeof the destination node.
On the other hand, the latency under the store and forward
switching mode can be determined as follows:§ f ·º§f· ªl ¨¨ ¸¸  « n  1 ¨¨ ¸¸»© U ¹¼©E ¹ ¬t sf(2)(n  2) represents the exclusion of the source node from theFig. 2 Flow chart of the HSR node under the operation of the QR
approach using the cut-through switching mode.

node sent its bits until the destination node has received all of
the bits:tc

W r W p W n

where t c is the frame latency under the cut-throughW
switching mode, W r is the transmission delay time, p is the
propagation delay time, which is assumed to be zero because
the network example has short link lengths, and W n is the node
processing rate. Assume the queuing delay time is zero.
The above expression can be written as:

calculation of the delay time on the path of the sent frame.
Assume that the frame size that node C has sent is 1522
bytes, each of the network links’ capacity is 100Mb/s, and thenode processing rate is 100Mb/s. Thereafter, the latency
under both the cut-through and store and forward switchingmodes will be determined using (1) and (2) as follows:§ 48 · § 1522 u 8 ·º
§ 1522 u 8 · ª
tc 4 ¨¨ « 52 ¨
6 ¸
6 ¸
6 ¸»
100
10
u
© 100 u 10 ¹ © 100 u 10 ¹¼¹ ¬©
0.610 ms
Note that h 48 bits, which is the size of the destination

MAC address of the sent frame, because each node canrecognize that the frame is a unicast frame and is sent to aunique address. Therefore, the nodes do not need to readadditional bits from the frame.
t§ 1522 u 8 · ª§·º¸  « 5  1 ¨ 1522 u 8 ¸»
¨
¨
6¸
6¸
© 100 u 10 ¹ «¬© 100 u 10 ¹»¼

4¨

sf

0.974 ms

Let us find the latency reduction percentage with respect tothe network size, or in other words, with respect to the numberof network nodes. This can be represented in the following
expression:§t ·lim ¨1  c ¸ u 100%
¨ t ¸
n of
sf ¹
©
where H is the latency reduction percentage of the
lim H
n of

cut-through mode compared to the store and forward mode.
lim H
n of

Fig. 3

An HSR example network for analytical analysis.

§º·ª¨ l §¨ f ·¸  « n  2 §¨ h ·¸  §¨ f ·¸» ¸
¨
¸
¨
¸
¨
¸
¨
©E ¹ ¬© U ¹ © U ¹¼ ¸
lim ¨1 ¸ u 100%ªn of§·§ f ·ºf¨¸l ¨¨ ¸¸  « n  1 ¨¨ ¸¸»¨¸©E ¹ ¬© U ¹¼©
¹

Assume that E U . In Fig. 3, the path from nodes C to G
has a number of links equal to the number of nodes ‒1, or it
can be said that l n  1 .
Thus, we can rewrite the above expression as follows:

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

lim H
n of

899

§
ª
º·
¨ n §¨ f ·¸  §¨ f ·¸  « n  2 §¨ h ·¸  §¨ f ·¸» ¸¨U¸ ¨U¸¨U¸ ¨U¸ ¸¨© ¹ © ¹ ¬© ¹ © ¹¼lim ¨1 ¸ u100%
ª
n of
§
·
§
·
§ f ·ºff¸¨
n ¨¨ ¸¸  ¨¨ ¸¸  « n  1 ¨¨ ¸¸»¸¨©U¹ ©U¹ ¬© U ¹¼¹©

§ n f  nh  2h ·¸ u100%lim ¨¨1 
2nf  2 f ¸¹
nof
nof ©
§
§ §
h · ··¨¨ n ¨ f  h  2 ¸ ¸¸
n ¹ ¸¸
u100%
lim H ¨¨ lim 1  lim ¨ ©
f · ¸¸
§
nof
nof
nof ¨
¨¨
¨ 2n ¨ f  ¸ ¸ ¸¸n ¹ ¹¹
©
©
©

lim H

lim H
n of

§
f h·
¸ u 100%lim ¨¨1 
2 f ¸¹n of ©

(3)

Fig. 5 Frame latency under the cut-through and store and forward
switching modes using the QR approach.

It is obvious from (3) that H is not affected by the numberof nodes when this number becomes large; on the contrary, itvaries inversely with respect to the frame size. Fig. 4 showsthat H increases when the frame size increases, and the
maximum reduction percentage that can be achieved is 49.4%
when h 144 and f 1522 bytes; however, the lowest H value
is about 35% when the frame size is 64 bytes. It is also shownin Fig. 5 that the cut-through mode offers less latency than thestore and forward mode, which in turn improves network
performance and delivers the sent frames quickly.

IV. SIMULATION ANALYSIS
To illustrate the performance of the QR approach in a wider
network, the network shown in Fig. 6 was selected as a
network example for simulating the QR operation under boththe cut-through switching and the store and forward modes.
Then, the results were compared. For this purpose, theOMNeT++ Simulator version 4.6 [20] was used in two
scenarios. In these scenarios, the frame latency of all
travelling frames in the network was accumulated and thencompared under both switching modes. Assume that the frame
size is 1522 bytes, all channels have a capacity of 100Mb/s,and all nodes have a processing rate of 100 Mb/s.
A. First scenario
In this scenario, each source node of the connection pairs
listed in Table I sent 10 frames to each corresponding node inits pair (source-destination). In other words, this scenario wasFig. 6

An example network for simulation analysis.

used for testing the QR approach with the cut-throughswitching mode under a unicast traffic type.
B. Second scenario
Table II shows the details of the connection pairs for this
scenario in which each source node sent 10 multi/broadcast
frame types.
For both scenarios, the simulation tests were conducted
TABLE IDETAILS OF THE CONNECTION PAIRS FOR THE FIRST SCENARIO
Source node

Destination node

Node 1

Node 142

Node 2

Node 103

Node 11

Node 3

4

Node 9Node 135

Node 15

Node 126

Node 16

Node 4

7

Node 3

Node 15#18Node 4

Node 99Node 13

Node 10

10Node 14

Node 2

11

Node 10

Node 112Node 12

Node 16

Fig. 4 Percentage of latency reduction versus frame size.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

TABLE IIDETAILS OF THE CONNECTION PAIRS FOR THE SECOND SCENARIO
Group number

Source node

Destination nodeGroup 1

Node 1

Nodes 3, 4 and 16

Group 2

Node 10

Nodes 11, 13 and 12

Group 3

Node 13

Nodes 3, 9, and 14

Group 4

Node 2

Nodes 10, 15, 16 and 2

Group 5

Node 15

Broadcastunder the cut-through and store and forward switching modes.
The results are shown in Figures 7 and 8. The results illustratethat the QR approach has a better performance under the
cut-through switching mode with respect to the frame latency
parameter.

more than 49% compared to the store and forward mode
when h 144 and f 1522 bytes. In general, a reduction
percentage ranges from 35% to 49% with respect to the frame
size. The simulation analysis also showed a superior
performance of the QR approach under the cut-through modefor both of the proposed scenarios, or in other words, under
any traffic type.
ACKNOWLEDGMENT
This research was supported by Basic Science Research
Program through the National Research Foundation of Korea(NRF) and funded by the Ministry of Education (No.
NRF-2015R1D1A1A02059506).

REFERENCES
[1]

[2]

[3]

[4]
Fig. 7 Frame latency under the cut-through and store and forward
switching modes for the first scenario.

[5]

[6]

[7]

[8]

[9]
Fig. 8 Frame latency under cut-through and store and forward switchingmodes for the second scenario.
[10]

V. CONCLUSIONSIn this paper, we describe HSR node behavior when it
receives a frame under the QR approach (HSR mode X) using
the cut-through switching mode as well as which actions it
will take to receive or forward the received frame dependingon the destination MAC, source MAC, and HSR header. We
also simulated the QR performance from the point of view ofthe frame latency because the cut-through and the store andforward switching modes will not differ from the traffic
reduction aspect. In a single ring network topology, the QR
approach under the cut-through switching mode was shown tohave reached a reduction percentage in the frame latency of

900

[11]

[12]

[13]

D. H. Lee, H. A. Pham, and J. M. Rhee, “SAFE: A Scalable
Autonomous Fault-Tolerant Ethernet Sch eme for Large-Scale Star
Networks,” Proc. IEICE Transactions on Communications, vol. 95,
no.10, pp.3158-3167, Oct. 2012.
IEC 62439-1 Ed. 1.1, “IEC Industrial communication networks –High availability automation networks – Part 1: General concepts andcalculation methods,” The International ElectrotechnicalCommission: Geneva, Switzerland, 2013.
IEC 62439-3 Ed.03, “IEC Standard. Industrial Communication
Networks - High availability automation networks - Part 3: Parallel
Redundancy Protocol (PRP) and High-availability Seamless
Redundancy (HSR),” The International ElectrotechnicalCommission: Geneva, Switzerland, 2016.
IEC 61850-90-4, “IEC Standard. Network Engineering Guideline forCommunication Networks and Systems in Substations,” TheInternational Electrotechnical Commission: Geneva, Switzerland,
2013.
S. A. Nsaif and J. M. Rhee, “DVP: A Novel High-Availability
Seamless Redundancy (HSR) Protocol Traffic-Reduction Algorithm
for a Substation Automation System Network,” Proc. Energies,
vol. 7, no. 3, pp.1792-1810, 2014
J.C. Tan and W. Luan, “IEC 61850 based substation automation
system architecture design,” Proc. IEEE Power Energy Soc. General
Meeting, pp.1-6, San Diego, CA, USA, July 2011.
J. A. Araujo, J. Lázaro, A. Astarloa, A. Zuloaga, and J. I. Garate, “PRP
and HSR for High Availability Networks in Power Utility
Automation: A Method for Redundant Frames Discarding,” Proc.
IEEE Transaction on Smart Grid, vol. 6, no. 5, pp.2325-2332, Jan.
2015
A. Astarloa, J. Lazaro, U. Bidarte, J. A. Araujo, and N. Moreira,“FPGA implemented cut-through vs store-and-forward switches forreliable Ethernet networks,” Proc. IEEE conference on design ofcircuits and integrated circuits (DCIS), pp. 1-6, Madrid, Spain, Nov.
2014.
J. A. Araujo, J. Lazaro, A. Astarloa, A. Zuloaga, and N. Moreira,“Duplicate and circulating frames discard methods for PRP and HSR(IEC62439-3),” Proc. IECON 2013-39th Annual Conference of theIEEE in Industrial Electronics Society, pp.4451-4456, Vienna,
Austria, Nov. 2013.
S. A. Nsaif and J. M. Rhee, “Improvement of high-availability
seamless redundancy (HSR) traffic performance for smart gridcommunications,” Proc. Journal of Communications and Networks,
vol.14, no.6, pp.653-661, Dec. 2012.
S. A. Nsaif and J. M. Rhee, “RURT: A novel approach for removing
the unnecessary redundant traffic in any HSR closed-loop network
type,” Proc. International Conference on ICT Convergence (ICTC),
pp.1003-1008, Jeju, South Korea, Oct.2013.
S. A. Nsaif and J. M. Rhee, “RMT: A novel algorithm for reducing
multicast traffic in HSR protocol networks,” Proc. Journal ofCommunications and Networks, vol.18, no.1, pp.123-131, Feb. 2016.
N. X. Tien, S. A. Nsaif, and J. M. Rhee, “High-availability Seamless
Redundancy (HSR) Traffic Reduction Using Optimal Dual Paths(ODP),” Proc. International Conference on Green and Human
Information Technology (ICGHIT), pp.37-40, Da Nang, Vietnam,
Feb. 2015.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 5, Issue 4, July 2016

[14]

[15]

[16]

[17]

[18]

[19]

[20]

N. X. Tien and J. M. Rhee, “FHT: A Novel Approach for Filtering
High-Availability Seamless Redundancy (HSR) Traffic,” Proc.
Energies 8, no. 7, pp.6249-6274, 2015.
I. R. Altaha, J. M. Rhee, and H. A. Pham, “Improvement ofHigh-Availability Seamless Redundancy (HSR) Unicast Traffic
Performance Using Enhanced Port Locking (EPL) Approach,” Proc.
IEICE Transactions on Information and Systems, vol. 98, no. 9,
pp.1646-1656, 2015.
H. D. Ngo and H.S. Yang. “Latency and Traffic Reduction for
Process-Level Network in Smart Substation based on
High-availability Seamless Redundancy”, Proc. IEEE Transaction
on Industrial Electronics Society, pp. 2181 - 2189, 2015.
J. A. Araujo, J. Lazaro, A. Astarloa, A. Zuloaga, and A. Garcia. “PRP
and HSR version 1 (IEC 62439-2), improvements and a prototype
implementation”, In Industrial Electronics Society, IECON
2013-39th Annual Conference of the IEEE, pp. 4410-4415. IEEE,
2013.
Cisco white paper, “Understanding Switch Latency,” June 2012.
Available:
http://www.cisco.com/c/en/us/products/collateral/switches/nexus-30
00-series-switches/white_paper_c11-661939.html
S.A. Nsaif, S. Kim, and J. M. Rhee. "Quick removing (QR) approach
using cut-through switching mode." In 2016 18th International
Conference on Advanced Communication Technology (ICACT), pp.
170-174. IEEE, South Korea, 2016.
http://omnetsimulator.com/

901

Saad Allawi NSAIF received his B.Sc. degree inElectrical Engineering from University of Baghdad,
Iraq, in 1999 and his M.Sc. degree in Computer andControl Systems from the same university in 2002.
After graduation, he joined the University of Baghdad
as an Assistant Lecturer. Later, he joined the Iraqi
Ministry of Defense in 2004. He was the Director of
the Command and Control Systems (C2) for 7 years.
His contribution in designing, developing, andestablishing the Iraqi C4I systems is well-known, especially with the Iraqi
Defense Network (IDN). In Feb. 2015, he received his Ph.D. in Information
and Communications Engineering from Myongji University, South Korea.
Currently, he is a Research Assistant Professor at Myongji University. His
current research interests are in ubiquitous networks, layer 2 switching,industrial Ethernet, ad hoc networks, and smart grid communications. He is
also a member of IEEE-SA and Myongji working group of IEEE and IEC
standardization project.

Semog KIM received his B.Sc. degree in ElectricalEngineering from Pukyong National University,
South Korea, in 1998. In 1999, he joined Dist. ofMotorola Broadband tech part as an HFC Network
Engineer until 2010. He received his Ph.D. in
Information and Communications Engineering from
Myongji University in 2013. Currently, he is a
researcher at Myongji University. His interests are inubiquitous networks, smart grid communications,and HFC networks.

Jong Myung RHEE received his PhD from North
Carolina State University, USA, in 1987. After 20
years at the Agency for Defense Development in
Korea, where he made noteworthy contributions to
C4I and military satellite communications, he joined
DACOM and Hanaro Telecom in 1997 and 1999,
respectively. At Hanaro Telecom, which was thesecond largest local carrier in Korea, he served as
Chief Technology Officer (CTO) with a senior
executive vice-president position. His main duty at Hanaro Telecom was a
combination of management and new technology development for
high-speed Internet, VoIP, and IPTV. In 2006, he joined Myongji University,and currently, he is the Vice President of Myongji University as well as a full
professor in the Information and Communications Engineering Department.
His recent research interests are centered on military communications andsmart grids, including ad-hoc and fault-tolerant networks. He is a member ofIEEE and IEICE.

Copyright ⓒ 2016 GiRI (Global IT Research Institute)