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
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Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
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Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
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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
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Dr. Kyuchang Kang, ETRI, Korea
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
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Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
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Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2014 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2014 GiRI (Global IT Research Institute)

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

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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2014 GiRI (Global IT Research Institute)Volume 2, Issue 1

1

Research on the Power Information Collector of Optical Fiber Based on EPON Technology

122

Yannian Wu, Yinggang Zheng, Zhibin Zang, Xiaohui Song, Yongbo Liu
Beijing R&D department of Gridcom Co.,Ltd China

2

Performance Modeling and Evaluation of Millimeter-Wave Based WPANs

128

Tony Tsang
School of Professional Education and Executive Development (SPEED), Hong Kong Polytechnic University,Hong Kong

3

Wireless Harness Inside ICT Equipments

135

Narihiro Nakamoto*, Hiroshi Ban*, Tomohiro Oka*, Shoichi Kitazawa*, Kiyoshi Kobayashi*, Noriyasu Kikuchi**, Hironobu Hatamoto**, Satoru Shimizu**,and Minako Hara****ATR Wave Engineering Laboratories, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan, **OKI Electric Industry Co. Ltd., 3-4 Hikari-no-oka,
Yokosuka-shi, Kanagawa 239-0847, Japan, ***NTT Energy and Environment Systems Laboratories, 3-1 Morinosato Wakamiya, Atsugi-shi, Kagagawa
243-0198, Japan

4

Analysis of voice quality over infrastructured WLAN with Distribution System

144

Edgar da Silva Arrais*, Claudio de Castro Monteiro** Federal Institute of Education, Science and Technology, AE 310 South Avenue 05 LO, South Master Plan, Palmas, Tocantins - Brazil

5

A Hybrid Chaos- Fuzzy -Threshold Steganography Algorithm for Hiding Secure Data
Mazhar Tayel, Hamed Shawky, Alaa El-Din Sayed Hafez
Electrical Engineering Department, Faculty of Engineering, Alexandria University

156

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013122

Research on the Power Information Collector of
Optical Fiber Based on EPON Technology
Yannian Wu, Yinggang Zheng, Zhibin Zang, Xiaohui Song, Yongbo Liu
Beijing R&D department of Gridcom Co.,Ltd China
wuyannian@sgepri.sgcc.com.cn , zhengyinggang@sgepri.sgcc.com.cn , zangzhibin@sgepri.sgcc.com.cn ,songxiaohui@sgepri.sgcc.com.cn , liuyongbo@sgepri.sgcc.com.cn,
Ethernet
Abstract—EPON(
EPON(Ethernet
Passive
Optical
Network
Network))technology is a new kind of fiber optical broadband accessnetwork technology. The EPON system mainly includes OLT,ONU and ODN. It inherited the low cost of Ethernet and the highbandwidth of light network, so it has Broad prospects ofapplication in the Residential electricity information collection
field
fieldss. Based on above, this paper combines the characteristics of
EPON technology and the requirement of information collection
system in the Residential electricity
electricity,, developed a design andapplication methods of Optical Fiber collection terminal
equipment for the power consumption data acquisition system
based on EPON technology, which is an important application
part of EPON system. This paper mainly studies the hardware
and software design of Optical Fiber collector, and its applicationin the Lain Xiang Community in Beijing
Beijing..

Index Terms—EPON
EPON,, ONU, Optical Fiber Collector
ollector,, eCos
SystemSystem,, Smart community

I. INTRODUCTION
PON(Ethernet Passive Optical Network)is a newtechnology that adopts one-to-multi-points topology,
realizes data transmission on the physical layer by opticalfiber and optical passive devices, and provides various internet
accesses by Ethernet. It is comprised of OLT (Optical Line
Terminal), ONU (Optical Network Unit) on consumer side,
and ODN (Optical Distribution Network), and can be flexiblyorganized into tree topology, star topology, bus topology, etc(e.g. [1]).

E

Manuscript received Jan 28, 2013. This work was sponsored by the
National State 863 projects of China, the project ”Research and Develo
pment of Intelligence with power information and communications technology” Grant No.2001AA05A116, it was also supported by National Sta
te Grid Corporation.
Yannian Wu is with the Department of Beijing Research Center at c
ompany of China Gridcom. Beijing, China (Tel:86-13651374161; fax: 86-010-51964890; email: wuyannian@sgerpri.sgcc.com.cn).
Yinggang Zheng is with the Department of Beijing Research Centerat company of China Gridcom. Beijing, China (Tel:86-13105123823; fax:
86-010-51964890; email:zhengyinggang@sgepri.sgcc.com.cn).
Zhibin Zang is with the vice-general manger at company of China G
ridcom. Beijing, China (Tel:86-13501025419; fax: 86-010-51964800; ema
il: zangzhibin@sgepri.sgcc.com.cn).
Xiaohui Song is with the Department of Beijing Research Center atcompany of China Gridcom. Beijing,China(Tel:86-13843589756; fax: 86010-51964890; email: songxiaohui@sgepri.sgcc.com.cn).
Yongbo Liu is with the Department of Beijing Research Center at company of China Gridcom. Beijing,China(Tel:86-15810510126 fax: 86-01
0-51964890; email: songxiaohui@sgepri.sgcc.com.cn).

EPON technology combines the advantages of both passive
network and Ethernet technologies, with a transmission rate ofboth upstream and downstream reaching 1.25Gbps. It isfeatured with high-reliability, low maintenance cost, highcompatibility with multiple topologies and interfaces, hence
an ideal communication solution for the power system, which
satisfies the unique requirements of network structure,
security, maintainability and interface types for utilities.
Power consumption data collectors of traditional
communication mode are well developed and widely applied.
Collectors, concentrators and meters are integral parts oftraditional power consumption data acquisition system.
Differences between various solutions lie in the adoption ofcommunication methods, such as GPRS, CDMA, RS485,
carriers, and other integrated methods. The main problem
regarding PLC is the rapid attenuation of high-frequency
signal in low-voltage grids, while the GPRS and CDMA are
affected by package loss, limited maximum rate, longswitching delay, operation instability, uncertainty of fault
processing time (e.g. [2]). The communication architectureof the system consists of two layers: one is communication
between master station system and concentrators, called
remote communication; the other is the communication
between concentrators and meters, called local communication.
Since the collecting points are numerous and collecting
intervals short, large amount of data will come out. Thus it isideal to adopt EPON optical fiber technology with broad
bandwidth, low cost, and high scalability in the remotecommunication between master station system andconcentrators to collect real-time, accurate and reliable power
consumption data. In addition, EPON is compatible withcurrent Ethernet technology, and can be easily maintained andmanaged. This paper focuses on the design and developmentof ONU, which is the key part of power consumption dataacquisition system. By means of joint debugging of OLT andmaster station system, ONU has met its design requirements
and SGCC (State Grid Corporation of China) standards.
II. OPERATING PRINCIPLE AND APPLICATION SOLUTIONS OF
EPON SYSTEMThe EPON is the primary type of PON technology that
reduces Optical Fibre deployment dramatically while
preserving the merits of Ethernet networks. EPON is based on
the IP/Ethernet structure. Currently, the IP/Ethernet
implementation accounts for more than 95% of the total LAN

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

communications. With this cost-effective and efficient
structure, the EPON becomes the most effective way toconnect the end users to the access networks. The emergenceof the 10 Gb/s Ethernet backbones and the metropolitan arearings also makes EPON the optimal last-mile solution of the
future all-optical networks.

A. Operating Principle of EPON
In this paper, ONU refers to the equipment integrating
traditional ONU and power consumption data collector, whichis called optical fiber collector. Through single-mode bidirectional optical fiber, the upstream and downstream data of
EPON has realized full-duplex communication by WDM(Wavelength Division Multiplexing) with a data rate of 1.25 G
b/s. The downstream communication (from OLT to ONU)
adopts TDM (Time Division Multiplex) broadcasting mode.
The data is broadcasted from OLT down to all ONU byvariable length information packet, and the maximum lengthof information packet can be 1518 bytes. Based on LLID
(Logical Link Identifier), ONU receives packets that belongs
to it and abandons other packets (e.g. [3]). The wave length ofdownstream communication is 1490 nm. The upstream
communication shares the system by TDMA (Time Division
Multiple Access). For each ONU upstream data, a private
timeslot is allocated to ensure that only one ONU is sending
upstream data at a time. The procedure is as follows: all data
is marked with a unique LLID tag at ONU, then the OLT will
allocate each registered ONU with a LLID tag, and finally thedata is sent to OLT according to its timeslot. More details
about the registration phase can be found in the current
version of the IEEE 802.3ah standard as well as in [4]. The
wave length of upstream communication is 1310nm.
U ser side

NetWork
sid e

2 13
1

Optical Fiber
Collector (O N U )

1
1

Power
m eters

2 13
2

Optical Fiber
Collector (O N U )

2
2

Power
m eters

Optical Fiber
2 13
Collector (O N U )
3

3
3

Power
m eters

Downstrea
m
O LT

2 13
321

ODN

123

transmit them to the users while others frames are discarded.
The architecture of the EPON systems is illustrated in Fig.1(e.g. [5]).

B. Application Solution
With optical fiber communication, the dedicated network of
power system can be divided into two parts: the optical fiber
backbone network and the optical fiber access network. The
backbone network is comprised of SDH (Synchronous Digital
Hierarchy) equipment, while the access network is comprisedof EPON equipment. This article mainly focuses on the accessnetwork, which is directly applied in the local communicationof power consumption data acquisition system.
In communities, as network-side equipment, OLT is both aswitch or router and a multi-service platform which provides
EPON-oriented optical interfaces. Besides the network
assembling and access functions, OLT could also performbandwidth assignments, network security and management
configurations according to the customers’ different QoS/SLA
requirements. In electric power collection system OLT
directly gets access to the acquisition server (master station
system) by Ethernet in engine room, and works as a data
bridge between master station system and ONU, providing
PON interfaces for ONU, ODN is mainly composed of one ormore passive optical splitters which connects OLT and ONU,
and provides optical transmission channel between the twosides. It allocates the optical signal power, and split
downstream signal from one fiber into several fibers andcombine optical upstream signals from multiple fibres into
one, the optical ratio can be 1:8, 1:16, or 1:32, and can beconnected to each other. The longest distance the ODN cancover is 20 kilometres.
As the core part of power consumption acquisition system,
ONU reach meters through RS485 bus, and collects power
consumption information of end users. At present, each ONU
can collect data from 32 meters, and its basic functions are inline with the specifications of SGCC. In a power consumption
acquisition program based on EPON (e.g. Fig. 2), ONU
collector refers to the power optical Fibre collecting
equipment.

U p stream

Fig. 1. Typical network diagram of EPON

As the branch-side equipment of EPON network, OLT
aggregates data from ONU, which provides internet ports andother interfaces on consumer side. Each ONU transmits thecustomers’ Ethernet frames to the OLT in the transmission
timeslot which is assigned by the OLT. ODN is a 100%
optical network of different topologies enabled by splitters ofvarious split number and split ratio. EPON system adopts thebroadcast method in downstream direction. Ethernet frames ofdifferent ONU are transmitted in one downstream timeslot.
The packet which carries all the ONU data reaches different
ONU and each ONU only extracts its own data frames andIII. H&S DESIGN OF ONU
ONU adopts the idea of embedded and modular design.
Compared with upstream GPRS, CDMA and 230MHz
dedicated network of traditional concentrators, ONU takes
optical Fibre as its communication medium. This has greatly
improved the timeliness, stability and security of meterreading, hence an essential difference from traditional
concentrators in terms of hardware structure. This design is
expected to reach the goal is that the throughput performance,in multi ONU case, the upstream is greater than 90%, thedownstream is greater than 95%; The overload packet loss rate,
upstream is less than 10%, downstream is less than 5%; The
forward delay, downstream is within 500us ,while theupstream within 1.5ms or less.

A. Hardware Design of the System

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

The hardware of ONU mainly consists of 4 parts: EPON
processor module, optical transceiver module, power supply
module, FE interface module, bus and storage module (e.g.
Fig. 3).
The core part of ONU is EPON processor module, which
adopts the PCC8016 chip solution of CORTINA. This kind ofchip is a single-chip solution functioned with traffic
management and dispatching. It has built-in frame buffer,
EPON processor

EPON port

E PON
Upstream
port

E PON
Optica lm o dule

E PON
SerDesData transfer
E ngineQo S Eng ine
L2 sw itc h
VLAN Engine
D a ta p a cket
cla ssifica tio n

E PON
M AC

FEC

2M
SR A MG E /F E M A
C

FE
PH
Y

FE userinterfa ce

SMF

CPU data
memo ryA R M C PU

SR A Mco ntro ller

Data cache

input laser light and 1310nm output laser light. The Opticaltransmission distance of this module can reach 20 kilometres.
The FE interface module for consumer business is a highspeed Ethernet interface, which provides operation control
platform for consumers and work as the debugging supportinginterface for maintainers in the operation of ONU.
In the process of power consumption data acquisition, as
the core equipment, ONU receives meter-reading order frommaster station and handles it. The upstream communication
uses optical fiber, while the downstream communication withmeters uses RS485 bus.

E M II// E M C
f ilte r

L aserDriverTx
Input

Back Face Diode1310 n m

1310 nm F -P
L aserBias Control

Tx Data
L V PE CL

Tx Disable
LVTTL

UART

8M
Fla shmemo ryR S 485
co ntro ller

R S 485t ransceiverTVS
Pro tectio nsurg eR S - 485 port
pro tectio n

R S 485t ransceiverTVS
Pro tectio n

R S -485 port
surg epro tectio n

Photo Detector
1490 n m

Surge
Pro tectiono v erer-voltage
pro tectio n

T IA

Low Pass
FilterRF
AMP

Rx Data
L V PE CL
Amplifier
LimiterLine Driver

R x _S D
LVTTL
ReceiverO N /O F F

pow erAC 2 2 0 V

F-P Laser Diode1310 n m
WDM

Data cachemanagement
M PC PE ngine124transform er

Rectifierfilte r

D C /D C

D C 3.3V

Fig. 4.

Schmatic of EPON Optical module

Tailored to the meter-reading system, a RS485 controller
chip MAX3082 is added to the PCCS8016 processing chip to
Gigabit PON interface, serials port, JTAG debugging interface, facilitate the switch from serial port to RS485 interface. For
FLASH and SRAM interfaces, and 5 volts power supply. And the bi-path RS485 interface, one path is used to connect
a built-in ARM-9 processor is responsible for management RS485 BUS of meters; the other is used as engineeringand control. As a standard IEEE 802.3ah compatible chip, it maintenance interface as well as meter connection. The
supports EPON Technological Specifications of China extended FLASH is used to solidify the bootstrap and store
Telecom and MPCP (one-to-multi-points control protocol). data document of automatic meter reading. It is also
The entire EPON system realizes the discovery and functioned with data freezing, historical and curve dataregistration of ONU by means of MPCP Protocol (e.g. [6]). storage. The JS28F128 FLASH has a capacity of 16M, which
The operation principle of the MPCP mechanism can be basically meets application requirement. The SDRAM
explained as follows: the whole available upstream channel (HY57V1601 by HYNIX, 16M) is mainly used for operating
bandwidth is divided into transmission units (typically termed system document copy from FLASH and caching ofslots) using the Time Division Multiplexing (TDM) technique, applications. The power supply module is also a key part ofwhich can be assigned to the active ONU (more specifically to the system. It not only supports EMC, but also provides surge
the respective LLID) based on the DBA mechanism under protection. With filter and transformer, it can provide a service
operation in the OLT central packet schedule (e.g. [7], [8]). voltage of 3.3 volts for the chip. In addition, the system has
On consumer side, PCCS8016 also provides wide range of the function of hard clock. It uses the SD2405, high-accurate
QoS (quality of service) and traffic management functions to and real-time clock chip of built-in battery to realize the clock
guarantee that the bandwidth, delay, and jitter of each ONU synchronization of the master station and collecting terminals
are satisfied when the traffic is allocated by SLA. The optical of power consumption data acquisition system.
module of this system adopts LTB34D2 and Optical B. Software Design of the Systemtransceiver provided by Hisense. The optical transmission
The software of ONU adopts embedded design idea. Thisdistance of this module can reach 20 kilometers. The FE
articlewill skip protocols relating the upper layer OLT andinterface module for consumer business is a high speed
focus
onapplications. Considering the real-time requirement
Ethernet interface, which provides operation control platformof
power
consumption data acquisition system, the operating
for consumers and work as the debugging supporting interface
platformof
ONU adopts the eCos (embedded Configurable
for maintainers in the operation of ONU.
operating system) by RedHat. This open source system is a
The Optical module of this system adopts LTB34D2 serials
provided by Hisense. The Schematic of Optical transceiver is configurable, portable, royalty-free, and real-time operating
illustrated by Figure 4. The module incorporates 1310 nm system for deeply embedded applications. Compared withburst-mode transmitter and 1490 nm continuous-mode Linux, the advantages of eCos lie in its flexible configurability,
receiver. An integrated WDM coupler can separate 1490nm and less occupied kernel space, which makes it suitable for the
Fig .3. Hardware schematic of Optical Fiber collector

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

cost-sensitive terminals of power consumption dataacquisition. Fig. 5 illustrates the software framework of thesystem. Similar to other embedded equipment, the bottom
layer of the system as BSP (Board Support Package) are
device drivers such as serial portal, internet port, clock,
FLASH, etc., enabling upper layer applications to access the
hardware. The eCos realizes thread management, thread
dispatch, interrupt and anomaly handling, and synchronizationof the system on the platform layer.
According to the requirements of power consumption dataacquisition system, the software adopts modular design. All
the modules are independent threads, and communicate bymessage mechanism such as mailbox, queue and shared data
structure. Cys_user_start(), the main function of applications,Application
la y er
Application
la y er

eC OS Operating
sy ste mFile system2)( y a ffs
ffs2

B S P ，Device
DeviceD river

Platformla y er

Device driver
la y er
Hardwarela y erARM/ o thers

R A M /R O M

Fig. 5.

NAND
F la shEthernet
c h ip125

Smart community contains the power informationcollection, interactive services, residential distribution
automatic, distributed power and energy storage of user side,
electric vehicles charging, smart home furnishing and some
new technological achievements. Smart community aims toachieve both economic growth and a low-carbon society. With
this convenience community can achieve reliable power
supply and the electricity consumption information iscollected automatically. The smart community management
system can promotes environmental quality improvement andenergy saving in diverse urban activities. Optical Fibre
Collectors make sure the power consumption information isreal-time acquisitied and make some guides to there users.

A. Deployment of ONU Collectors
Deployment of optical Fibre collectors as the key of smart
power consumption has been finished in Lain Xiang
Community, the first smart community of commercial
operation in Beijing.
Figure 6 illustrates the distribution of optical Fibre
collectors in 2 buildings of Lain Xiang Community. About 20
collectors were installed; each of them covers 2 floors. Data
from up to 700 meters is then time multiplexed by the splitterand transmitted to OLT and the master severs system in thebasement. The master system can read meters at real time andstore these data information into the local database, and themaster station system can be connected to the public network.
Figure 7 shows an on-site installation of Optical Fibre
Collector in Lain Xiang smart community. The Optical Fibreusing OPLC (Optical Fibre Composite Low-voltage Cable)
ensures high reliability of data transmission. With a smart
home gateway, consumers can gain access to the database andSoftware diagram of Optical Fiber Collector
2 4 th foo r

is responsible for creating main threads regarding to specifictasks, including：

2 3 th foo r

2 2 th foo r
Bu ild in g 2
2 1 th foo r

2 0 th foo r

1) ONU_heart_thread and Task_thread: Responsible forFreeze data of day, Freeze data of month and Freeze curve of
meters for one month.
2) Read_meter_rs485_thread: Responsible
reading by collector at fixed intervals.

for

1 9 th foo r
1 8 th foo r

1 7 th foo r

Bu ild in g 1

meter3) Rx_thread and term_sever_rx_thread: Responsible forsending data to RS485 meters from collectors and finish
relevant protocol conversion.
4) Protocol_Comm_thread: Responsible for the protocol
data analysis and processing for the master station and meters.

1 6 th fo o r

1 6 th foo r

1 5 th fo o r

1 5 th foo r

1 4 th fo o r

1 4 th foo r

1 3 th fo o r

1 3 th foo r

1 2 th fo o r

1 2 th foo r

1 1 th fo o r

1 1 th foo r

1 0 th fo o r

1 0 th foo r

9 th fo o r

9 th foo r

8 th fo o r

8 th foo r

7 th fo o r

7 th foo r

6 th fo o r

6 th foo r

5 th fo o r

5 th foo r

4 th fo o r

4 th foo r

3 th fo o r5) Term_client_thread: Responsible for the interaction
between data and the master station.
All the threads are interdependent and compose thesoftware of entire meter reading system.
COMMUNITY

2th foo r

1th fo o r

1 th foo r

7 0 1 Distrib u tio n ro o m
Basemen t
OLT

Fig. 6.

IV. APPLICATION OF OPTICAL FIBER COLLECTORS IN SMART

3th foo r

2 th fo o r

Basemen t

ODN

Deployment diagram of Optical Fiber collector in the L.X.Y
Community

check their daily or monthly power consumption information.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

B. Comparison of Collection Success Rate
Collection success rate of meter reading system is the main
purpose for electric power company, the ultimate goal is fullcoverage, full collection and full cost control. Base on above,
the Optical Fibre Collectors arise at the historic moment.
Table 1 show reading meters using Optical Fibre Collectors in

Fig. 7. On-site installation diagram in smart community

126

11/1

815

808

99.1%

9

6

66.6%

11/2

815

808

99.1%

13

13

100%

11/3
11/4
11/5

815
815
815

805
807
786

98.7%
99.0%
96.5%

13
9
13

13
7
10

100%
77.7%
76.9%

11/6
11/7
11/8
11/9
11/1
11/1

815
815
815
815
815
815

808
800
804
808
800
809

96.1%
98.2%
98.6%
96.1%
98.2%
99.2%

10
8
11
12
7
15

9
7
11
11
6
15

90%
87.5%
100%
91.6%
85.7%
100%

V. CONCLUSIONSIn this paper, we presented the development background of
EPON technology, studied the development present situationand the application situation at home and abroad, and thenproposed a new method for power information collection
system using Optical Fibre Collector based on EPON
technology. The implementation of power consumption
information acquisition system is a vital part of smart gridconstruction, and is in line with the development ofinternational grid technology. As a representative smart gridtechnology, power Fibre to home has been widely supportedby the government and State Grid. Power Fibre
communication network has covered more and morecommunities, providing residents with power consumption
information acquisition services. Optical Fibre collectors
based on EPON technology will be the mainstream of future
power consumption information acquisition system.

Lain Xiang community and table 2 shows reading meters
using Power Line Carrier Collectors in Hue Xian community
similar as Lain Xiang community, from November 1th. to
15th. In 2011. In the table user num refers to the meter
numbers, collect num refers to the success reading numbers ofmeter, purchase num refers to the times of user purchasepower, and sending num refers to the master station sendpurchase power data to the meters. Comparison between the
two tables we can easily find the Optical Fibre Collectors have
ACKNOWLEDGMENT
outstanding collect rate than the Power Line Carrier Collectors.
This will be a trend by using ONU Collectors for electric
The authors would like express their gratitude to thepower company make a choice for future Collector purchase National State 863 projects, the project “Research andand deployment.
Development of Intelligence with power information andcommunications technology” with NO.2001AA05A116. The
TABLE 1. READING METERS USING OPTICAL FIBRE COLLECTORS
authors also would like express their gratitude to theEngineering Department of Gridcom, they provide over all
Optical Fibre Collector (or ONU)
project drawing paper.
PurchaSendi
Sendin
Date
User
Num

Collect
Num

Collect
Rate

694
694
694
694
694
694
694
694
694
694
694

694
693
694
694
694
694
694
694
694
694
694

100%
99.9%
100%
100%
100%
100%
100%
100%
100%
100%
100%

11/1
11/2
11/3
11/4
11/5
11/6
11/7
11/8
11/9
11/1
11/1

se
Num
68
17
8
23
21
16
13
9
5
15
11

ng
Num
68
17
8
22
21
16
13
9
5
15
11

g
Rate
100%
100%
100%
95.7%
100%
100%
100%
100%
100%
100%
100%

REFERENCES
[1]

[2]

[3]

[4]
[5]

TABLE 2.

READING METERS USING POWER LINE CARRIER COLLECTORS[6]

Power Line Carrier Collector (or PLC)
Date

User
Num

Colle
ct
Num

Collect
Rate

Purch
ase
Num

Sending
Num

Sending
Rate[7]F.J. Effenberger, T.S. El-Bawab, “Passive optical networks(PONs): Past, present, and future,” Optical Switching and Net- working,Vol.6, No.2, pp.143–150, 2009.
Gao Feng, Dong Yabo, “Signal Transmission Characterristic Analysis
In Low Voltage Power Line Carrier Communication,” Chinese Journalof Automation of Electric Power Systems, Vol.7, pp.15-19, 2008.
SUN Zhongwei, WU Juying, “Identity-based Access Control forDistribution Automation Using EPON,” Chinese Journal of Electronics,
Vol.20, No.3, 2011.
G. Kramer, Ethernet Passive Optical Networks, 1st ed., New York:
McGraw-Hill, 2005.
Zhang Hao, Bu XianDe, “Application of EPON Technology in Power
Consumption Information Collection System,” Chinese Journal ofAutomation of Electric Power Systems, Vol.213, pp.42–45, 2010.
S.S. Roh, S.H. Kim, “Security model and authentication protocol in EPON-based optical access network,” IEEE ICTON,
pp.99–102, 2003.
H.-J. Byun, J.-M. Nho, J.-T. Lim, “Dynamic Bandwidth Allocation
Algorithm in Ethernet Passive Optical Networks,” IEEE Electron.
Letters, vol. 39, pp.1001–1002, 2003.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

[8]

127

M.P. McGarry, M. Maier and M. Reisslein, “Ethernet PONs: A surveyof dynamic bandwidth allocation (DBA) algorithms”, IEEE Commun.
Mag., Vol.42, No.3, pp.8–15, 2004.

Yannian W u He was born in Liaoning Province,
China. He received his M.Sc. degree in 2006 andmajor in Communication and Information System
from
Beijing
University
of
posts
and
telecommunications. Since October 2009, he was an
Electrical Engineer at company of China Gridcom in
the Department of Beijing Research Center. His
current research interests include Modeling andcontrol of power electronics systems, Optical fiber
communication systems, Analysis and Control ofDynamic Systems, robust and adaptive controls, andPower and Control Systems technology.

Yinggang Zheng He was born in Shandong Province,
China. He received the B.Sc. degree in 2001 and major
in computer science technology and application. Since
October 2009, he was an Electrical Engineer atcompany of China Gridcom in the Department of
Beijing Research Center. His current research interests
include process modeling, system identification,
adaptive control, self-tuning control, optical fiber
sensing technology and power information collecting
technology.

Zhibin Zang He was born in Heilongjiang Province, China. He received the M.Sc. degree in Guanghua School of Management, Peking University. Now,
he was vice-general manager at company of China
Gridcom. His current research interests include Po
wer system automation, system identification, optic
al fiber sensing technology and power informationcollecting technology.

Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013128

Performance Modeling and Evaluation ofMillimeter-Wave Based WPANs
Tony Tsang
School of Professional Education and Executive Development (SPEED),
Hong Kong Polytechnic University,Hong Kong.
ttsang@ieee.org

Abstract—The large amount of unlicensed bandwidth available
in the millimeter-wave has enabled very high data rate wirelessapplications. The IEEE 802.15 Task Group 3c has completed
standardization efforts for multi-gigabit data rate communications on both the physical (PHY) and medium access control(MAC) layers. In this paper we use Performance Evaluation
Process Algebra (PEPA) to evaluate a typical WPAN system’s
performance. The approach is more convenient, flexible, andlower cost than the former simulation method which needs
develop special hardware and software tools. Moreover, we caneasily analysis how changes in performance depend on changes
in a particular modes by supplying ranges for parameter rate
values.
Index Terms—WPAN, IEEE 802.15.3c, Performance Evaluation Process Algebra, Performance Analysis, Formal Modeling.

I. I NTRODUCTION
IRELESS system designers dream of replacing all
cables for indoor data communication with high-speed
wireless connections. Unfortunately, the dedicated unlicensed
frequency spectrum for this purpose was insufficient until the
U.S. Federal Communications Commission (FCC) declaredthat the 57-64 GHz band could be used. Japan, in turn,allocated the 59-66 GHz band. With the latest adoption of
the European Telecommunications Standards Institute (ETSI)
57-66 GHz band, there is now a common continuous 5 GHzband available around 60 GHz in most of the major markets.
As the wavelength of a signal at 60 GHz is around 5 mm, it
is called millimeter wave (mmWave) band.
Another important breakthrough was the introduction ofrelative cheap and power-efficient complementary metal oxide
semiconductor (CMOS) processing for semiconductor manufacturing of 60 GHz band devices. As a result, the priceand power requirements for consumer devices were met. For
successful commercialization, the final need for developers
was a standard the would support almost all usage models.
The IEEE 802 LAN/MAN standards committee has manysuccess stories in developing global wireless standards, suchas 802.11 (WiFi)and 802.15.4 (Zigbee). Within IEEE 802,interest in developing an mmWave physical layer (PHY) began
in July 2003, with the formation of an interest group underW

Manuscript received on May 3, 2012.
Tony Tsang is with the School of Professional Education and Executive
Development (SPEED) of The Hong Kong Polytechnic University,Hong Kong.
Tel:(852) 98397311, E-mail:ttsang@ieee.org

802.15 working group for wireless personal area networks(WPANs). According to the IEEE 802 procedure, if the interest
group is successful, it is followed by a study group, which
decides the scope of the new standard. In March 2004, a
study group for mmWave PHY was formally created. The
study group members agreed that development fo new PHY
to transmit 1 Gb/s or higher data rates is feasible. It was
decided to reuse an existing medium access control (MAC)
layer (IEEE 802.15.3b), with necessary modifications andextensions. After the approval of the project authorization
request, a task group was created. The task group firest focusedon creating usage models, aa 60GHz indoor channel model.
and evaluation criteria. After two years of hard work, three
PHY modes and multiple MAC improvements were selected
to support different usage models. After various letter ballots
sponsor ballots, and resulting improvements, in September
2009 the IEEE-SA Standard Board approved IEEE 802.15.3c2009 [1, 2]. It took four and a half years for the task group
to complete the standard. Such a duration has been common
for many IEEE standards that provided new PHYs.
The rest of this article explains the salient features of thestandard, as well as some important outcomes. The organization of the article follows the order of the task group’s
standardization process. First, we provide channelization is
explained, which is common for all PHY modes. Details of
the three different PHY modes, new MAC layer features,and beamforming procedures are explained in the following
sections, respectively. Moreover, we propose a framework
for combined timed behaviors and stochastic process algebra.
This done with a modeling language, called Performance
Evaluation Process Algebra (PEPA), for describing the timed
stochastic behaviors of wireless networks. This methodology
can then be analyzed using an automatic tool. With thisperformance analysis methodology it is possible to obtain thedesign parameters of implementation using simulation with alower computational time and cost.
II. PHY L AYER D ESIGN IN 802.15.3 C
Due to conflicting requirements of different usage models(UMs), three different PHY modes have been developed:• Single carrier mode of the mmWave PHY (SC PHY)
• High-speed interface mode of mmWave PHY (HSI PHY)
• Audio/visual mode of the mmWave PHY (AV PHY)
The SC PHY is best suited for kiosk file downloading andoffice desktop usage models. The HSI PHY is designed mainly
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

for the bidirectional , non-line-of-sight (NLOS), low-latency
communication of the conference ad hoc model. The AV PHY
is designed to provide high throughput for vide signals invideo streaming usage models. The main difference between
the different PHYs is the modulation scheme. The SC PHY
uses single carrier modulation, whereas the AV PHY and
HSI PHY use the orthogonal frequency-division multiplexing(OFDM) modulation. In SC modulation, one symbol occupies
the whole frequency band, and thus its duration is very
short. In OFDM, the available frequency band is divided
into orthogonal subcarriers, and data symbols are sent using
those subcarriers. In general, SC modulation allows lower
complexity and low power operation, whereas OFDM suits
well in high spectral efficiency and NLOS channel conditions.
Orthogonality of the subcarriers in OFDM allows the use of
inverse fast Fourier transform (IFFT) at the transmitter andfast Fourier Transform (FFT) at the receiver.
All PHY modes have a typical signal frame format consisting of a preamble, header, and payload. The preamble is used
for frame detection, channel estimation, frequency recovery,
and time acquisition. The header contains essential informationsuch as payload size, modulation, and coding used in the
payload. the Pay load includes the data to be transmitted.
A. Audio / Visual Mode of the mmWave PHY
As video and audio devices could be designed only as awireless data source (e.g., DVD player) or only as a data
sink (e.g., HDTV), highly asymmetric data transmission ispossible; hence, the designers of the AV HPY mode created
two different sub-PHY modes: high-rate PHY (HRP) for video
transmission and low-rate PHY (LRP) for control signal. Bothof the sub-PHY modes use OFDM.
The HRP mode has an FFT size of 512 and uses all
the channel bandwidth available. There are three classes ofmodulation and coding schemes (MCSs) with equal error
protection, delivering data rates of 0.952, 1.904, and 3.807
Gb/s. There are two MCSs with unequal error protection and
two MCSs , in which only the most significant bits are sent.
On the other hand, the LRP mode occupies only 98 MHz
bandwidth, and three LRPs are arranged per HRP channel.
This allocation is to accommodate three different networks in
one channel, because the HRP modes are assumed to havehigh beamforming gains.
The AV PHY use Reed-Solomon(RS) block code as theouter code and convolutional coding as the inner code in the
HRP mdoe, whereas only convolutional coding is used in the
LRP mode. Modulation schemes used in the AV PHY arelimited to π/2 quadrature PSK (QPSK) and 16 quadrature
amlitude modulation (QAM).
III. MAC L AYER E NHANCEMENTS OF 802.15.3 C
Before going into the details of MAC layer enhancements,
we briefly introduce the IEEE 802.15.3c MAC. It is based on
the IEEE 802.15.3b standard. which itself is an improvement
over IEEE 802.15.3. In the standard a network is called apiconet, which is formed in an ad hoc fashion. Among a groupof devices (DEVs), one will act as the piconet coordinator

129(PNC) to provide the piconet’s synchronization and to manage
access of rest of the DEVs. The necessary control information
is embedded in beacons. Upon receiving a beacon from a PNC,
the DEVs become aware of the existence of piconet. Beacons
provide information about when and how DEVs can accessthe network.
During network operation, time is divided into sequential
superframes (SFs). Each SF has three segments: a beacon
period, a contention access period (CAP), and a channel time
allocation period (CTAP). During the beacon period, the PNC
sends one or multiple beacons. The CAP is reserved mainly
for command and control communication between PNC and
DEVs. Since such a communication is mainly asynchronous,a suitable access method is selected, carrier sense multiple
access with collision avoidance (CSMA/CA). The remaining
time of an SF includes the CTAP, which provides timedivision multiple access (TDMA) communications. The CTAP
is composed of multiple channel time allocations (CTAs). Each
CTA is a time slot granted by the PNC for a certain pairof DEVs. Time-sensitive applications such as AV streaming
use the CTAP for guaranteed data transmission. With these
specifications, system designers had already developed anefficient and well structured MAC layer that only required
improvements in three major areas:
Providing coexistence among different PHYs and avoiding interference from hidden devices• Improving transmission efficiency to enable MAC SAP
rates over 1 Gb/s and providing low-latency transmission
for delay-sensitive applications• Supporting directivity inherent to 60 GHz signals andbeamforming antennas•

In the next two paragraphs, we explain enhancements in thefirst two areas.
To achieve better coexistence among DEVs using different
PHY modes, a sync frame is introduced. A sync frame
includes information about the duration of the SF and timing
information of the CAP and each CTA. Sync frames are modulated using the common mode signaling (CMS) mentioned
in the previous paragraph. According to 802.15.3c rules, it ismandatory for all PNC-capable DEVs to transmit a sync framein every SF. In addition, any PNC-capable DEV shall be able
to receive and decode sync frames and other command frames
modulated with CMS.
As a result, any PNC-capable DEV, regardless of its PHY
mode in operation, will be informed about the existence of
nearby piconets. It will then have the opportunity to join
one instead of starting another independent piconet. The sync
frame transmission can thus be seen as an effective coexistence
method to mitigate potential co-channel interference fromother piconets. Apart from the rule for PNC-capable DEVs, an
optional rule related to non-PNC-capable DEVs is also definedin the standard: Any DEV capable of transmitting a sync frame
may do so in the first granted CTA in an SF and in every
predefined number of SFs.
This rule is intended to further extend the coverage area ofthe sync frame. It allows non-PNC-capable DEVs to participate in the sync frame transmission.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

In high-speed WPAN and WLAN systems transmission
efficiency decreases with the increase in transmission speed
due to the increased ratio of overhead time to payloadtransmission time. To improve transmission efficiency andthroughput performance, frame aggregation can be employed.
The basic idea of frame aggregation is to reduce the overhead,such as the preamble and PHY/MAC header, by concatenating
multiple MAC service data units (MSDUs) to form a framewith a long payload. In the IEEE 802.15.3c standard, twonovel aggregation methods are specified standard aggregationand low-latency aggregation.
Standard aggregation is designed to support transmissionof uncompressed video streaming. The MAC layer of thetransmitter, upon receiving an MSDU from the upper layer,
divides the MSDU into small pieces of data blocks if thelength of the MSDU exceeds a predefined threshold. This
process is called fragmentation. The MAC attaches a frame
check sequence (FCS) to each data block to form a subframe.
For each subframe, there is a subheader (Sh) created tocarry information needed for the receiver to decode individual
subframes, such as subframe length, MSDU sequence number,and used MCS. The MAC header, on the other hand, carries
high-level control information applicable to all the subframes,such as source and destination addresses. All the subheaders
are placed back-to-back and attached to a single header checksequence (HCS) to form a MAC subheader. The MAC layerthen transfers the subframes, MAC subheader, and MAC
header to the PHY layer. The PHY layer performs channel
coding and modulation, and delivers the data to the receiver
over the wireless channel afterward. An important aspect of
this method is that instead of distributing the subheaders
between the subframes, all the subheader are concatenatedand put in front of the subframes. The reason for such adesign is that video streaming contains both data and control
information, which should be treated with different priorities.
Changing MCS over subframes is a common approach tosupport priority. However, when operating at a speed ofgigabits per second, timely changing of the MCSs subframeby subframe can be difficult for the receiver. However, putting
the subheaders in front enables the receiver to know the MCS
of each subframe in advance, helping to realize timely MCS
switching. It was reported that over 80 percent efficiency
improvement and above 4 Gb/s throughput are achieved withstandard aggregation.
IV. P ERFORMANCE M ODELING AND T OOLS
Performance Evaluation Process Algebra (PEPA), developed
by Hillston in the 1994s [4, 5], is a timed and stochastic
extension of classical process algebras such as Communication
Sequential Process (CSP) [6]. It describes a system as aninteraction of the components and these components engage
in activities. Generally, components model the physical or
logical elements of a system and activities characterize thebehavior of these components. An exponentially-distributed
random variable is associated with each activity specifies theduration of it, that leads to a clear relationship between themodel and a Continuous Time Markov Chain (CTMC) process.

130

Via this underlying Markov process performance measures canbe extracted from the model. The PEP A formalism provides asmall set of operators which are able to express the individual
activities of components as well as the interactions between
them. We provide a brief summary of the operators here, moredetails about PEPA can be found in [3, 4].
Prefix: (a, r).P The component will subsequently behave as
P after it carries out the activity (a, r), a represents the action
type and r represents a duration which satisfies exponential
distribution with parameter r.
Choice: P + Q The component represents a system which
may behave either as P or as Q. the choice depends on which
activity is completed first.
◃▹
Cooperation: P L1 Q The component represents the interaction between P and Q. the set L is called the cooperation
set and denotes a set of action types that must be carried out
by P and Q together.
Probabilistic Choice: P⊕r Qdenotes the probabilistic choicewith the conventional generative interpretation, thus with probability r the process behaves like P and with probability 1 − rit
behaves like Q .
Hiding: P / L Hiding makes the activities whose action
types in L invisible for external observer.
Constant: P = Q The equation gives the constant P the
behavior as the component Q.
A. PEPA Eclipse Plug-in Tools
The PEPA is a language for modelling systems in which a
number of interacting components run in parallel, and whose
behaviour is stochastic. The core semantics of PEPA is interms of Continuous Time Markov Chains (CTMCs), andan alternative semantics in terms of Ordinary Differential
Equations (ODEs) has also been developed. PEPA has been
applied in practice to a wide variety of systems, and its
success as a modelling language has been largely down to
its extensive tool support. Most recently, the PEPA Plug-in
Project [7, 8] has integrated a range of analysis techniques
based on both numerical solution and simulation into a single
tool built on top of the Eclipse platform [9]. As with allcompositional Markovian formalisms, however, PEPA suffersfrom the state space explosion problem. A model can havean underlying state space that is exponentially larger thanits description, meaning that it can be infeasible to analyse.
Fluid flow approximation using PEPAs ODE semantics cansolve this problem if we are only interested in the averagebehaviour of the system over time. However, if we want toreason over all possible behaviours of the model for example,
the probability that an error occurs within some time interval
then we must consider the CTMC semantics. In this paper,
we present a new extension to the PEPA plugin, in which amodel can be abstracted by combining, or aggregating, states.
To safely over-approximate the behaviour of the original model(for any aggregation of its states), we use two abstraction
techniques - abstract CTMCs (a type of Markov decision
process with infinite branching), and stochastic bounds. We
provide a model checker for the three-valued Continuous
Stochastic Logic (CSL), which computes from the abstraction
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013a safe bound of the probability of a quantitative property
holding in the original model X if the actual probability isp, then the model checker will return an interval I = [p1, p2]
such that p 2 I. The current version of the PEPA plug-in isavailable from http://www.dcs.ed.ac.uk/pepa/tools/plugin, andprovides several views:
Abstract Syntax Tree View
The Abstraction View is a graphical interface that shows thestate space of each sequential component in a PEPA model.
It provides a facility for labelling states (so that they can bereferred to in CSL properties), and for specifying which states
to aggregate.
Model Checking View
The Model Checking View is an interface for constructing,editing, and model checking CSL properties. The property
editor provides a simple way to construct CSL formulae, byreferencing the labels given to states in the abstraction view.
It ensures that only syntactically well-formed CSL formulae
can be constructed.
State Space View
The State Space View is linked to the active PEPA editor
and provides a tabular representation of the state space of theunderlying Markov chain. The table is populated automatically when the state space exploration is invoked from thecorresponding top level menu item. A row represents a stateof the Markov chain, each cell in the table showing the local
state of a sequential component. The order in which sequential
components are displayed corresponds to the order in which
they are found in the cooperation set by depth-first visit of thecooperations binary tree. A further column displays the steadystate probability distribution if one is available. A toolbar menu
item provides access to the user interface for managing state
space filters. When a set of filter rules is activated,the excluded
states are removed from the table. The probability mass ofthe states that match the filters is automatically computed
and shown in the view. Filter rules are assigned names andmade persistent across workspace sessions. From the toolbar
the user can invoke a wizard dialogue box to export thetransition system and one to import the steady-state probability
distribution as computed by external tools. The view also hasa Single-step Debugger, a tool for navigating the transition
system of the Markov chain. The debugger can be opened
from any state of the chain and its layout is as follows. In
an external window are displayed the state description of thecurrent state and two tables. The tables show the set of states
for which there is a transition to or from the current state.
The tables are laid out similarly to the views main table. In
addition, the action types that label a transition are shown in afurther column. The user can navigate backwards and forwards
by selecting any of the states listed.
Performance Evaluation View and Graph View
Performance Evaluation View and Graph View A wizard
dialogue box accessible from the top-level menu bar guides theuser through the process of performing steady-state analysis
on the Markov chain. The user can choose between an array of
iterative solvers and tune their parameters as needed. Performance metrics are calculated automatically and displayed in
the Performance Evaluation View. It has three tabs showing the131

results of the aforementioned reward structures (throughput,
utilisation, and population levels). Throughput and populationlevels are arranged in a tabular fashion, whereas utilisation isshown in a two-level tree. Each top-level node corresponds toa sequential component and its children are its local states.
The Performance Evaluation View can feed input to theGraph View, a general-purpose view available in the plug-in
for visualising charts. Throughputs and population levels areshown as bar charts and a top-level node of the utilisation tree
is shown as a pie chart. As with any kind of graph displayedin the view, a number of converting options is available. The
graph can be exported to PDF or SVG and the underlying data
can be extracted into a comma separated value text file.
Experimenting with Markovian Analysis
An important stage in performance modelling is sensitivity
analysis, i.e. the study of the impact that certain parameters
have on the performance of the system. A wizard dialogue
box is available in the plug-in to assist the user with the
set-up of sensitivity analysis experiments over the models.
The parameters that can be subjected to this analysis are therate definitions and number of replications of the array ofprocesses in the system equation. The performance metrics
that can be analysed are throughput, utilisation, or populationlevels. If the model has filter rules defined, the probability
mass of the set of filtered states can be used as a performanceindex as well. The tool allows the set-up of multiple experiments of two kinds: one-dimensional (performance metric vs.
one parameter) or two-dimensional (performance metric vs.
two parameters changed simultaneously). The results of theanalysis are shown in the Graph View as line charts. For
example, a parameter that may have an important impact on
the performance of the system is the reset delay of the CPU.
Time Series Analysis
When performing a time-series analysis there are three basic
steps to complete; component selection, solver selection andsolver parameterization, all of which are handled by thetime-series analysis wizard. Rather than simply observing allcomponents, the wizard allows the modeller to select only
those components that are of interest. This becomes more
pertinent as either the number components in the system or
number of observed time points increase - one limitationof the current time-series solvers is that all data is held
in memory, and only written out to disk when exportingfrom the graph view. Solver selection and parameterization
are self-explanatory, with the list of visible parameters being
dynamically linked to the currently selected solver. In keepingwith the rest of the UI, the selections across all three steps are
persistent across invocations. Likewise, each unique parameteris stored only once, meaning parameters such as start and stop
times are persistent over all solvers. Lastly, the parameters,
including selected solver, are attached to the results in thegraph view for future reference. Currently this meta data canonly be seen when the data is exported. The last feature of the
wizard is the ability to export the model in alternative formats,such as Matlab.
Copyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

MSB
Data

Splitter andScrambler
LSB

to RF

Fig. 1.

Guard
Interval
Insertion

ChannelEncoder

EEP/UEP
MUX

Bit
Interleaver

Tone
Interleaver

SymbolMapperChannelEncoder

IFFT

The block diagram of the transmitter

V. PEPA R EPRESENTATION
The mmWave PHY operates within the 57.24-65.88 GHz
band as allocated by the regulatory agencies worldwide, andthe spectrum is then equally divided into four channels.
This high spectrum leads to dense frequency reuse, smaller
sizes of radio frequency (RF) components, high antenna gain
and secure data transmission. However, the high attenuation
resulted by oxygen absorption, path loss and multi-path effects
make the high data rate transmission difficult to deploy. In
baseband, modulation methods are of critical challenge formmWave communications. The PHY layer of both the HSI
and AV modes are very similar and are based on the use of
OFDM.
Figure 1 describes the block diagram of the transmitter for
the IEEE 802.15.3c WPANs. Data are firstly split into upperand lower branches. The scrambled data are encoded with an
LDPC encoder in the HSI mode, whereas for the AV mode,
a Reed- Solomon (RS) code and a convolutional encoder are
implemented. For the unequal error protection (UEP) modulation and coding schemes (MCSs), different coding rates areapplied to the most significant bits (MSBs) and least significant
bits (LSBs). The binary serial input data is then mapped
to data symbols according to the modulation schemes. A
normalization factor is multiplied to each modulation schemein order to achieve the same average power. Overall theequal error protection (EEP) modulation dependent parameters
and the data rates. A tone interleaver is applied before themodulated data are sent to the OFDM modulator. 512 sized
IFFT is implemented to form one OFDM symbol, and 336 outof 512 subcarriers are data carriers.
◃▹
Encoder0 := < listen, λ0 > .Scrambler0 L0 < get, λ0 >.Channel − Encoder0 ;◃▹EEP0 := < listen, λ0 > .EEP1 L0 < get, λ0 > .Bit −Interleaver0 ;◃▹Interleaver0 := < listen, λ0 > .Symbol − Mapper0 L0 <get, λ0 > .Tone − Interleaver0 ;◃▹OFDM −Modulator0 :=< listen, λ0 > .IFFT0 L0 < get, λ0 >.Interval − Insertion0 ;PHY Layer equation defines how the components interact
with each other. According to the working cycle and thedefinitions of model’s components we give before, the PHY
Layer equation is show below:
PHY := Encoder0 < λ1 > EEP0 < λ2 > Interleaver0 <λ3 > OFDM − Modulator0
A frame header is added to the resulting payload to convey
information in the PHY and the Medium Access Control

132(MAC) Layer headers necessary for a successful decoding of
the frame. A PHY preamble is added prior to the frame header
to aid receiver algorithms related to frame detection, frequency
recovery, frame synchronization, and channel estimation. The
format of a PHY frame includes PHY preamble, frame header
and payload.
In IEEE 802.15.3c, a hybrid multiple access of contentionbased CSMA/CA (carrier sense multiple access with collision
avoidance) and contention-free TDMA (time division multiple
access) is used. The CSMA/CA is mainly used for control
signal transmission, while the TDMA is used for high-speed
data transmission. This hybrid multiple access can reduce thedata transmission collisions, and also maximize throughput by
applying an optimum access time. The MAC layer throughput
is determined by the amount of information bits exchanged
between the transceiver MAC, and the duration needed forsuccessfully delivering the information. Sources of overhead
include gap time, preamble, header fields for the PHY and
MAC layers, and ACK frames. The length of the ACK frames
depends on the type of ACK, and there are five types of ACK
defined for transmitting data frames in the piconet: no ACK(no-ACK), immediate ACK (Imm-ACK), delayed ACK (DlyACK), implied ACK (Imp-ACK), and block ACK (Blk-ACK).
◃▹Interface0 := PHY L0 MAC
MAC0 := < Type − ACK. λ0 > . MAC0
The overall system becomes Interface and User :◃▹User0 := Interface0 L0 User0
Usern := User0 < λ1 > User1 < λ2 > User2 < λ3 > • • • •VI. P ERFORMANCE E VALUATION
A. State Space
According to the actions of each action and interactions
among them, we can solve out the possible states. And according to the parameters setting of each action, we can derive a
corresponding continuous time Markov chain (CTMC) of thismodel, which allow us to solve out each state’s steady-state
probability. The state space and steady state probability of thisPEPA model is shown in Table I.
TABLE IS TATE S PACE AND STEADY-S TATE P ROBABILITY
No.
1
2
3
4
5
6
7
8
9
10
11
12

State
(1,1,1,1,1)(1,2,1,1,1)(1,3,2,1,1)(1,3,3,1,1)(1,3,4,1,1)(1,5,1,1,1)(2,3,5,1,1)(2,3,6,1,1)(2,4,1,1,1)(2,3,1,2,1)(2,3,1,1,2)(2,3,1,1,3)

Steady-state probability
0.0749611139221529
0.05996889113772232
0.09994815189620386
2.9984445568861153E-4
0.09944841113672284
3.12337974675637E-4
0.044751785011525276
0.06961388779570597
0.12431051392090355
0.2088416633871179
0.08701735974463247
0.13052603961694872

The sets III the colunm state stand for the whole states ofmodel, for example the set (2, 3, 1, 2, 1) represents the state( PHY, Encoder, EEP, Interleaver, OFDM − Modulator ).
However, the probability distribution of steady state is often
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

SC ModeHSI Mode
AV Mode2

Probability Density

not the ultimate goal of performance analysis. Sometimes thedesigners are more concerned about the throughput, responsetime or utilization of the systems. In the following sections,
we use the tool PEPE plug-in and Imperial PEPA Compiler(ipc) to evaluate the model’s performance metrics of thosecharacters.

133

1

B. Throughput Analysis
The study of how changes in performance depend onchanges in parameter mode values is known as sensitivity
analysis. We can vary some parameter’s value a little, and see
its influence degree to the model’s performance, for example,
the throughput or response time. Throughput is an actionrelated metric showing the rate at which an action is performed
at steady-state. In other words, the throughput represents theaverage number of the activities completed by the system
during one unit time.
From Figure 2, it can be observed that the impact of thenumber of devices on the throughput of transmit is moresensitive than the SC Mode. If we could make some efforts
to optimize the cache, and raise the HSI Mode form 0.3 to
0.7 or even more high value, the throughput of transmit could
greatly improved.
SC ModeHSI Mode
AV Mode0.9

Mean Throughput

0.7

0.5

0.3

0.1
1

Fig. 2.

10
Number of Devices

100

Throughput versus Number of Devices

C. Response Time Analysis
Form the perspective of the WPAN system, the responsetime is the interval between the receiving of a request fromthe client and the transmission of the feedback content to theclient. It is an important index of the system performance.
The response time of WPAN system is relative to a lot offactors, for example, the configuration of system, the capabilityof components, the interaction processes among components,
the health of the channel, etc. Figure 3 shows the cumulative
distribution function of the response time between request andtransmit actions. From the figures, it is clearly that when thepossibility of hitting the cache parameter mode up from 0.01 to
0.8, the more possibility the response time would finally be alower value, which is coincident with the result of throughput
analysis we have got before.

00Fig. 3.

0.2

0.4

0.6

0.8

1
Time

1.2

1.4

1.6

1.8

2

Throughput versus Number of Devices

VII. C ONCLUSION
In this paper, we have presented a detailed study of theIEEE 802.15.3c standard. The system throughput was studied
by simulating both the SC, HSI and AV modes over a 60 GHz
typical channel model. The link adaptation mechanism wasdescribed and the link throughput results were presented. The
achievable number of devices was also investigated. The theoretical MAC throughput for different frame sizes and ACKs
was calculated. The maximum achievable MAC throughput
also relies on the link quality conditions. A typical PWANs
system and the interactions within it, we have investigated itusing the highlevel modeling formalism PEPA. This allows
early analysis of potential designs and configuration to assess
its performance measure. The system considered is simple, but
this has allowed us to have a new idea about evaluating theperformance of the way system in its design phase. Moreover,the method we used is more convenience, flexible, and lower
cost than the former simulation method using special hardware
and software tools. For example, when a new part is added
into the system, designers just need specify a new component
to the system model and express it using PEPA language,
enabling designers to evaluate its impact to the system’s
performance. But the size of the state space of the underlying
Markov chain would increase sharply with the increasing ofcomponents within system, which may lead to the problem of
the state space explosion and cannot be computed easily. We
can use two ways to deal with this problem at present, namely,
aggregation and ordinary differential equations (ODEs), details
can refer to [4, 5].

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

134

R EFERENCES[1] Baykas, T.; Chin-Sean Sum; Zhou Lan; Junyi Wang; Rahman, M.A.;
Harada, H.; Kato, S.K., “IEEE 802.15.3c: the first IEEE wirelessstandard for data rates over 1 Gb/s”, IEEE Communications Magazine,
Volume:49, Issue:7, pps. 114-121, July 2011.
[2] IEEE Std 802.15.3c-2009 (Amendment to IEEE Std 802.15.3-2003),“IEEE Standard for Information Technology - Telecommunications andInformation Exchange between Systems - Local and Metropolitan Area
Networks - Specific Requirements. Part 15.3: The ultimate purposeof the 60-GHz WPAN systems is to deliver MAC throughput of theorder of multi-Gb/s over a reasonable range. To accomplish this, systemdesigners have to increase the transmission range, especially in nonline-of-sight channels. IEEE Communications Magazine E July 2011
121 Wireless Medium Access Control (MAC) and Physical Layer (PHY)
Specifications for High Rate Wireless Personal Area Networks (WPANs)
Amendment 2: Millimeter - Wave-Based Alternative Physical Layer
Extension”, pps. c1-187, Octaber. 2009.
[3] Tony Tsang, “Performance modelling and evaluation of OFDMA based
WiMAX systems using RT-SPA”, Proceedings of the International Conference on Computer and Communication Engineering 2008 (ICCCE08),
Kuala Lumpur, Malaysia, pps. 180- 186, May 13-18, 2008.
[4] J. Hillston,“A Compositional Approach to Performance Modelling”, PhD
Thesis, The University of Edinburgh, 1994.
[5] J. Hillston, “Fluid flow approximation of PEP A models”, Proceedingsof the Second International Conference on the Quatitative Evaluation of
Systems, IEEE Computer Society Press, pps. 33-41, 2005.
[6] C.A.R.Hoare,“Communicating Sequential Process”, Prentice-Hall, 1985.
[7] Micheal J.A. Smith, “Abstraction and Model Checking in the PEPA
plug-in for Eclipse”, Seventh International Conference on the Quantitative Evaluation of Systems, pps. 155-156, 2010.
[8] M. Tribastone, A. Duguid, and S. Gilmore. “The PEPA Eclipse Plug-in”,
Performance Evaluation Review, 36(4):28-33, March 2009.
[9] The Eclipse platform. http://www.eclipse.org.

Tony Tsang received the BEng degree in Electronics & Electrical Engineering with First Class
Honours in U.K., in 1992. He received the Ph.D
from the La Trobe University (Australia) in 2000.
He was awarded the La Trobe University Postgraduation Scholarship in 1998. He is a Lecturer
at the Hong Kong Polytechnic University. Prior
to joining the Hong Kong Polytechnic University,
Dr. Tsang earned several years of teaching andresearching experience in the Department of Computer Science and Computer Engineering, La Trobe
University. His research interests include mobile computing, networking,protocol engineering and formal methods. Dr. Tsang is a member of the ACM
and the IEEE.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013135

Wireless Harness Inside ICT Equipments
Narihiro Nakamoto*, Hiroshi Ban*, Tomohiro Oka*, Shoichi Kitazawa*, Kiyoshi Kobayashi*,
Noriyasu Kikuchi**, Hironobu Hatamoto**, Satoru Shimizu**, and Minako Hara****ATR Wave Engineering Laboratories, 2-2-2 Hikaridai, Seika-cho, Kyoto 619-0288, Japan**OKI Electric Industry Co. Ltd., 3-4 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-0847, Japan***NTT Energy and Environment Systems Laboratories, 3-1 Morinosato Wakamiya, Atsugi-shi, Kagagawa
243-0198, Japan
Nakamoto.Narihiro@ap.MitsubishiElectric.co.jp, hban@atr.jp, Oka.Tomohiro@db.MitsubishiElectric.co.jp,
kitazawa@atr.jp, kobayashi@atr.jp, kikuchi434@oki.com, hatamoto247@oki.com, shimizu706@oki.com,
hara.minako@lab.ntt.co.jp
Abstract—We have been investigating point-to-multipoint
wireless communication suitable for the use inside Information
and Communication Technology (ICT) equipments toaccommodate demands for weight saving, environmental lifecycle
CO2 reduction and improvement of assembly/maintenanceefficiency by means of replacing communication wire harnesses
with wireless technology, that is wireless harness. Through theradio propagation measurements inside four equipments ofautomatic teller machine (ATM), ticket vendor, vending machine,and printer, a consistent specification applicable to them has been
determined. Based on this specification, a test wireless system has
been developed. It showed a good performance even inside the
ATM that has the most lossy environment among the four
equipments we tested.
Keyword—2.4 GHz ISM band, ICT equipment, radio
communication equipment, radio propagation, wireless harnessO

I. INTRODUCTION

NE of recent trends in wireless technology ismachine-to-machine communication (M2M) using shortrange devices. As an expansive application of M2M, wirelesscommunication inside machines has been recently receivingmuch attention aiming for the improvement ofassembly/maintenance and weight savings. Several works havefocused on these benefits of replacing wire harnesses withwireless communication, that is wireless harness, and reported
Manuscript received July 6, 2012. This work was supported by Japan
Ministry of Internal Affairs and Communications with the fund of “Promotion
program for Reducing global Environmental loaD through ICT innovation” .
N. Nakamoto was with ATR Wave Engineering Laboratories, Kyoto
619-2688, Japan. He is now with Mitsubishi Electric Corporatio, Kanagawa
247-8501,
Japan(phone:+81-467-41-2533;
e-mail:nakamoto.narihiro@ap.mitsubishielectric.co,jp).
H. Ban, S. Kitazawa, and K. Kobayashi are with ATR Wave Engineering
Laboratories, Kyoto 619-2688, Japan.
T. Oka was with ATR Wave Engineering Laboratories, Kyoto 619-2688,
Japan. He is now with Mitsubishi Electric Corporation, Hyogo 661-8661,
Japan.
N. Kikuchi, H. Hatamoto, and S. Shimizu are with OKI Electric Industry Co.
Ltd., Kanagawa 239-0847, Japan.
M. Hara is with NTT Energy and Environment Systems Laboratories,
Kanagawa 243-0198, Japan.

its potential applicability in various vehicles and equipments
such as automotive [1], [2], aircraft [3], spacecraft [4], [5],personal computers [6], and so on, through fundamental radio
propagation measurements and feasibility tests.
Such utility of wireless harness also seems effective as agreen technology to reduce CO2 emissions of machines. The
reduction of CO2 emission is a big issue regarding with theglobal warming and has been challenged at all the sectors andvarious scenes in Japan. In accordance with these movements
we have been studying the application of wireless harness toInformation and Communication Technology (ICT)
equipments that have a considerable amount of wire harnesses
inside them. In addition, our study also covers the clarificationof how effective the wireless harness of ICT equipments is on
the reduction of CO2 emission. As a method to estimate theCO2 emission, we here employ a lifecycle assessment
methodology that converts both the consumption of resourcesand energy into the amount of CO2 emissions during the whole
lifecycle of the equipment. In our study, we have been focusing
on the wireless harness of mechatronics ICT equipments. We
have reported a fundamental study on measurements andmodeling of radio propagation in four mechatronics ICT
equipments; automatic teller machine (ATM), ticket vendor,
vending machine, and printer [7], [8].
In this paper, we first show briefly the concept of thewireless harness of the mechatronics ICT equipments andreview the radio propagation characteristics inside the above
four equipments. Secondly, we show our dedicated wirelesscommunication test system for mechatronics ICT equipments
and discuss its performances inside the mechatronics ICT
equipment. In order to understand an effect of the wirelessharness on the reduction of CO2 emission, a trial estimation of
CO2 emissions related to wire and wireless harness in the caseof ATM is also presented.

II. CONCEPT OF WIRELESS HARNESS
Figs. 1(a) and (b) show the conceptual picture of the wire
harness and the wireless harness communication inside the ICT

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

136

(a)

Fig. 2. Setup for measurements of the propagation loss and delay inside ICT
equipment.

(b)Fig. 1.
Conceptual pictures of wire harness and wireless harness
communication inside ICT equipment. (a) Wire harness communication and(b) wireless harness communication.

transmit antennas of terminal modules connected to sensors anda receive antenna of the access point on the control board. The
application of the wireless harness into mechatronics
equipments is very effective in assembly/maintenance load
reduction as well as weight saving. As a whole, these must
contribute to the reduction of the lifecycle CO2 emissions.

III. RADIO PROPAGATION INSIDE TESTED EQUIPMENT
equipment, respectively. The ICT equipment, especiallymechatronics ICT equipment, has a lot of sensors inside itself
so as to monitor mechanical states in various positions such astransfer tracks and stockers in real time. As shown in Fig. 1(a),all these sensors are connected to a control board with wire
harnesses in the present equipment. For example, ATM has
about 200 sensors for monitoring and the total weight of theharnesses connecting sensors to control boards and a mainboard goes up to more than a couple of tens kilograms. Since
the internal structure of ATM is so complicated and densely
packed, the harnesses are swirled along with the frames andmodules inside the chassis, resulting much longer than thestraight-line distance. This increases technical difficulties in theassembly/maintenance processes. As shown in Fig. 1(b), thewire harnesses communication between sensors and the control
board is replaced with wireless communication between

The radio propagation environment inside densely packed
mechatronics ICT equipments is significantly different fromconventional outdoor and indoor ones. Through the challenge
to apply wireless harness to mechatronics ICT equipments, wefound out that the radio channel inside the equipment closely
depends on its internal structure more than we expected. Inorder to understand the radio propagation characteristics insidesuch equipments, we measured propagation losses and delays
inside four mechatronics ICT equipments [7], [8]. Now weshow the typical examples of those measurement results.
The measurement setup for the radio propagation is shown in
Fig. 2 Both transmit (Tx) antenna and receive (Rx) antenna arecommercial 2.4GHz chip antennas (TAIYO YUDEN
AH-316M245001) or ultra wide band (UWB) antennas(TAIYO YUDEN AH-086M555003). Tx antennas are placedat various positions near the sensor positions and the Rx one is
fixed near the control board. The number of the measured Rx

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

137

where PRx is the received signal power in decibels and0

Propagation loss [dB]LTx, ref and LRx ,ref are the mismatch losses of Tx antenna and
20

40

60

80

1

2

3

4
5
6
Frequency [GHz]7

8

Fig. 3. Measured effective propagation loss in ATM. Tx antenna is located on
NLOS from Rx antenna.

Cumulative probability

100

10-1

Measured
Theory

10-2

80

60

40

20

0

Propagation loss [dB]Fig. 4. Cumulative probability of measured effective propagation losses from
2.2 to 2.6 GHz in ATM. Tx antenna is located on NLOS from Rx antenna.

positions is about 40 to 70. The S-parameters of two ports aremeasured by a vector network analyzer (VNA). The frequency
range is set from 1 to 8 GHz.
A. Frequency Characteristics of Propagation Loss
Fig. 3 shows an example of measurement results of theeffective propagation loss in ATM as a function of thefrequency. In Fig. 3, the Tx antenna is located on a
non-line-of-sight (NLOS) from the Rx antenna. The effective
propagation loss PLdB in decibels is defined by

PLdB   PRx  LTx, ref  LRx,ref(1)

Rx one in decibels, respectively. The gain and the reflection
characteristics of the antenna may be changed when theantenna is located near metallic or dielectric components. The
gains of antenna are involved in the effective propagation loss.
This is because it is difficult to remove the effective gains fromthe measured data at each measurement point. The reflection
characteristics of the antennas are de-embedded from PRx as
shown in (1). As seen in Fig. 3, a lot of deep nulls of effective
propagation loss are observed in the measured frequency range.
Frequencies and depths of these nulls depend on and are
sensitive to the position of the antennas.
The cumulative distribution function of effective
propagation losses from 2.2 GHz to 2.6 GHz in ATM is shownin Fig. 4. In Fig. 4, the Tx antenna is located on a NLOS fromthe Rx antenna. The theoretical curve is the Rayleighprobability distribution. A good agreement between themeasurement and theory is observed.
Such measured characteristics show that the inside of the
ATM are typical multipath-rich environments and the
propagation loss distribution for NLOS obeys the Rayleighmodel. We also confirmed that these propagation loss
characteristics are almost same in ticket vendor and vending
machine. Though we omit the detailed measurement results
here, in the case of printer, the propagation loss distribution for
NLOS obeys the Nakagami-Rice model because the printer has
a fewer components causing the shading and reflection of theradio wave inside itself than the other equipments. This
indicates that the multipath waves are not always the dominantcomponents inside equipment with few components causing
the shading of the radio wave and the stationary wave withlarger amplitude to be the dominant component exists among
the multipath waves.
B. Propagation Delay
Fig. 5 shows examples of the measurement results of the
propagation delay inside four equipments. As shown in Fig. 5,
the complex multipath propagations are observed because theinsides of these equipments are very complicated andmultipath-rich environment as mentioned before. The observed
propagation delays are less than 50 ns in various positions inATM, ticket vendor, and vending machine. Their chassis aremainly made of metal. However, they have slits and windows,
so that the radio wave easily dissipates from the inside. This
helps the propagation delay be in the same range of thatobserved in a chassis of personal computer [6]. On the other
hand, the printer that has a plastic chassis shows a shorter
propagation delay, and which is less than 30 ns. This indicatesthe dissipation of the radio wave for printer is larger than theother equipment. The measurement results and the models ofradio propagation in these equipments are summarized in Table
I. Based on these observations we decided to treat ATM as arepresentative of mechatronics ICT equipment whileconsidering broader applicability to other equipments.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

TABLE IMEASUREMENT RESULTS AND MODELS OF RADIO PROPAGATION IN FOUR ICTEQUIPMENT

1

Relative amplitude

ATM
0.8
0.6
0.4
0.2
0
0

10

20

30

40

50

60

70

138

80

Equipment

ATM

Ticket Vendor

Size (cm)(width  length  height)
Chasis
Propagation loss
for distance < 1 m
Propagation delay
Propagation loss
model at far positions

57  104  120

50  90  150

Metal

Metal

20 – 70 dB

20 – 50 dB

 50 ns

 50 ns

Rayleigh

Rayleigh

Vending
machinePrinter

70  70  180

48  48  55

Time [ns](a)Equipment
Size (cm)(width  length  height)
ChassisPropagation loss
for distance < 1 m
Propagation delay
Propagation loss
model at far positions

1

Relative amplitude

Ticket vendor
0.8
0.6
0.4

Metal

Plastics

20 – 60 dB

20 – 60 dB

 50 ns 30 ns

Rayleigh

Nakagami-Rice

0.2
0
0

10

20

30

40

50

Time [ns]

60

70

80

(b)

IV. SYSTEM DESIGN AND SPECIFICATIONS

1

Relative amplitude

Vending machine
0.8
0.6
0.4
0.2
0
0

10

20

30

40

50

Time [ns]

60

70

80

(c)
1

Relative amplitude

Printer
0.8
0.6
0.4

Although there were a couple of candidates amongcommercialized products such as Bluetooth [9], ZigBee [10],and UWB [11] systems and actually some of them were tested
inside our equipments at an early stage of our work [12], we
decided to make a custom system based on a commercializedradio frequency integrated circuit (RF-IC) running at 2.4 GHz
Industry, Scientific, and Medical (ISM) band. Some of thereasons for this are to satisfy our primary requirements that thesystem shall simultaneously collect status data from as many as
200 sensor transmitters, each of which runs with 1 kHz sensing
frequency and with a system latency of 1 ms. These
requirements are mainly derived from a current wired system of
ATM as a case reference. The existing technologies such
Bluetooth and ZigBee seemed hardly applicable to meet withthese requirements without deep level modification. Though
UWB is a fascinating high-speed technology, we have toconsider power consumption of sensor transmitters and theradio wave regulation of Japan that limits UWB to indoor use atpresent. Thus we decided to design a point-to-multipoint
custom system. Table II summarizes our system specifications.

0.2

V. ANTENNA

0
0

10

20

30

40

50

Time [ns]

60

70

80

(d)
Fig. 5. Measured propagation delay inside the four ICT equipments: (a) ATM,(b) ticket vendor, (c) vending machine, and (d) printer.

Since a lot of metals and dielectric components are closely
arranged inside mechatronics ICT equipment, extra free spaces
available for additional placement of wireless modules are notso large. The distances between adjacent components look 2 to
5 cm at best. In some places aerial slits are as narrow as lessthan 1 cm. Antennas on the wireless modules inside equipment,

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139

TABLE IISYSTEM SPECIFICATION
Num. of sensor modules (transmitters)
up to 200
Sensor data
0/1 (bit datum)
Sensing frequency at each sensor
1 kHz
Radio communication frequency
2.4 GHz ISM band
Modulation
GFSK
Access protocol
FTDMACommunication speed
1 Mbpstherefore, have to be inevitably placed close to the components.
In order to know how the antenna performance is affected byadjacent metals, we first measured variations of the reflection
coefficient (S11) by employing a setup shown in Fig. 6. An
antenna under test was placed on the surface of one aluminum
plate and the other aluminum plate was brought to closer to theantenna. Fig. 7 shows the measured reflection coefficient of acommercial 2.4 GHz chip antenna with various separations
between two aluminium plates. As shown in Fig. 7, thereflection characteristic of the chip antenna easily varies anddegrades when the aluminum plate was closely placed.
Compared to the free space condition, the reflection coefficient
at 2.45 GHz got worse to -10 dB at 20 - 30 mm and -5 dB at 15mm. Though these results are specific examples of the antenna
performance degradations, we also observed almost sameantenna performance degradations inside the mechatronics ICT
equipments.
In order to obtain stable antenna performance insidemechatronics ICT equipments, we designed a dedicatedantenna to have better performance when a metal plate exists ataround 20 mm and to be insensitive to the environmental
change. Fig. 8 shows a picture and a configuration of thedeveloped antenna. We adopt the shorted microstrip antenna(shorted MSA) in our wireless harness communication systembecause it may be suitable for use in a narrow space such as theinside of mechatronics ICT equipment because of its
low-profile structure. Moreover, shorted MSA has a groundplane on its back face and is expected to be less sensitive to theproximity of the metals to the ground plane side. The
dimensions of the antenna are 25 mm  30 mm  2.5 mm.
The wide and depth are same as the board size of terminal
module described later. The height of the antenna is about 1/50of the wavelength at the center frequency 2.44 GHz. In order toenhance the bandwidth of the antenna, we use the double
folded L-probe proximity feeding structure [13] as shown in
Fig. 8(b).
Fig. 9 shows the measured reflection coefficient of adeveloped antenna with various separations between two
aluminium plates. As shown in Fig. 9, the antenna exhibitedmuch more stable characteristics than the conventional one
shown in Fig. 7. The averaged reflection coefficient keeps
better than -11 dB from the free space to the distance of 15 mm
in the range of 2.4 to 2.5 GHz.

Fig. 6. Measurement setup of antenna characteristics at close positions tometals.

Fig. 7. Measured reflection coefficient of a commercial 2.4 GHz chip antenna.

(a)

(b)
Fig. 8. Configuration of the developed antenna. (a) Photo. (b) Configuration.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

140

Fig. 11. A pair of a wireless module and an antenna. The right photo shows anassembled state.

Averag mismatch loss [dB]Fig. 9. Measured reflection coefficient of a developed antenna with various
separations between parallel aluminium plates.

0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
-1

Fig. 12. FTDMA protocol sequence using four slots at each frequency withcheckerboard packet configuration.

0

5

10 15 20 25 30 35 40 45 50 55 60

Separation between plates [mm]Fig. 10. Measured average mismatch loss of a developed antenna.

Fig. 10 shows the measured average mismatch loss (averaged
between 2.4 and 2.5 GHz) of our developed antenna. As seen in
Fig. 10, the average mismatch loss becomes the minimum at the
separation d = 10 mm and is less than -0.4 dB for d < 20 mm. As
can be seen from these results, our developed antenna has astable and good antenna performance between the metallic
plates. Though these measurement results only show theantenna performances in the ideal situation such as in theparallel metallic plate, we observed our developed antenna also
shows a good performance in the mechatronics ICT
equipments.

VI. WIRELESS COMMUNICATION SYSTEM
A. Wireless Modules
To match the specifications listed in Table II, we made acustom wireless module based on a commercially-available
RF-IC produced by Nordic Semiconductor. Fig. 11 shows aterminal module coupled with an antenna developed. It has twoconnector ports to sensors and a board size of 25 mm  30 mm.
In this module custom protocols described next are embedded.
The combined wireless module shown in Fig. 11 is a kind offunctional test version.

B. Protocols
Fig. 12 shows a frequency and time division multiple access(FTDMA) protocol sequence which is implemented on the
RF-IC as an access protocol inside mechatronics ICT
equipments. The system is designed to utilize 73 frequency
channels with 1 MHz bandwidth in the range of 2400 MHz to2480 MHz and each channel has four time slots. A guard time isset for compensating the processing delay of RF-IC and guard
frequency band is prepared to avoid the interference fromadjacent frequency channels. Therefore, the guard time isallocated to the transmission time of both the adjacentfrequency channels in order to use frequency band efficiently.
The allocation of frequency to each terminal module ispre-processed at initial setting to avoid severe multi-pathinterference and functionalize frequency diversity effect. The
allocation of frequency is decided based on the following
evaluation value V of n th terminal module at m thfrequency channel:

V n, m   log 10 PERn, m 
1 N ch  log10 PERn, k 
N ch k 1(2)

where N ch is the total number of frequency channels andPER(n, m) is the packet error rate (PER) of the n th terminal
module at m th frequency channel. The denominator of (2) is
PER characteristics averaged over all frequency channels and
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

141

Packet error rate

1.E+00Resource
extraction

1.E-01

Production ofmaterials

1.E-02
1.E-03
1.E-04<Input>Energy resource

Manufacturingof parts

Developed Antenna
Conventional Antenna

Manufacture
1.E-05
2400

2410

2420

2430

2440

2450

2460

2470

2480

Frequency [MHz]Fig. 13. Comparison of packet error rates between our developed antenna andthe conventional antenna.

becomes smaller with the worse communication quality at all
frequency channels. On the other hand, the numerator of (2) is
PER characteristics at a certain frequency channel. Therefore,
higher V is obtained when the communication quality is badover all frequency channels and comparatively good at a certain
frequency channel. The frequency channels are allocatedaccording to the magnitude of V , in order from the terminal
module with highest evaluation value V . Currently we utilizeda pre-installed packet format for communication, and thus put acouple of sensor data into one payload to transmit. Though thepacket has a length of 72 bits, that is too long for datum lengthof each sensor and even for a couple of sensors, the total data
volume of 200 sensors with 1 kHz repetition can be
successfully transmitted to the access point (Rx) with using
FTDMA, running at 1 Mbps in each channel.
C. Performance
Performances of our developed wireless module and antenna
were measured in ATM. An access point was placed near thebackside of the front panel, and the terminal module was placed
close to the rear panel. The straight-line distance of them wasmore than 50 cm. The arrangement was where the propagation
obeys Rayleigh distribution and the propagation loss was about
60 dB. Fig. 13 shows the measurement results of PER against 7
frequency channels from 2400 MHz to 2480 MHz. In Fig. 13,
green and red dots mean performances of our developed
antennas and conventional 2.4 GHz meander-line inverted-F
antennas that the RF-IC supplier recommended, respectively.
As shown in Fig. 13, PER with the developed antennas
achieved 10-5 level in the wide range of channels. The
degradation of PER at 2460 MHz seems to be due to the severe
multi-path interference and a steep increase of propagation loss,
which is sensitive to the position of the terminal module. As
shown in Fig. 13, average to say, our developed antenna
showed better performance in ICT equipment.
In our early measurement of bit error rates (BER) at various
positions inside ATM, about 10% of the positions exhibitedvery lossy conditions probably due to the environmental reason<Output>
CO2

Assembly
Transportation
Maintenance

Use

Power
consumption
Transportation
Waste treatment /Recycling
Fig. 14. Evaluation boundary of our case study on the trial estimation of CO2
emissions.

and multi-path interference and suggested difficulty to achieve
BER being 10-6 level or better. Fig. 13 is only one example andwe can also achieve BER < 10-6 (corresponding PER is about
8.1  10-5) at dozens of sensor positions we measured inside
ATM by adopting our developed antenna and FTDMA based
channel allocation.

VII. GREEN EFFECT
The benefit of the wireless harness in terms of lifecycle CO2
reduction has been estimated [14]. The difficulties in such
estimation at an early stage of test development phase include
the collection of appropriate primary units of elemental parts
and the framework of evaluation.
Fig. 14 shows the evaluation boundary of our case study.
The manufacture process includes the resource extraction, theproduction of materials, and manufacturing of parts (cables andconnectors / wireless modules). The assembly process means
assembling the equipment with wire harnesses or wirelessmodules. In the use process, power consumptions by wirelessmodules and by the movement of workers for maintenance areincluded. In the waste treatment and recycling process,shredding process of equipment is only evaluated. In our study,
the transportation of products and wastes is only considered
and that of resources, materials, and parts is neglected.
Table III summarizes the trial estimation of CO2 emissions

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

TABLE IIICALCULATED CO2 EMISSION(a) Wire harness. (b) Wireless harness. (Kg-CO2/unit)
Lifecycle process(a)(b)
Manufacture of cables and connectors
Manufacture of wireless modules
Assembly
Transportation
Use
Waste treatment and recycling

288
0
74
69
12
3

0
145
41
54
0
2

Total

444

241

related to wire and wireless harness in the case of ATM. In theestimation of the wireless harness, CO2 emissions arecalculated in a case where the wireless harness is applied to thewire harness not only between sensors and the control board
but also between the control board and the main board of ATM
taking into account the future expansion of applicability of ourwireless harness technology.
In the case of wire harness, the major process of the CO2
emission is found to be the manufacture of cables andconnectors. It occupies 65% in the total CO2 emission. In thecase of wireless harness, it is found that the manufacture of
wireless modules process occupies 60% of the total CO2
emission and the CO2 emission of the use process, that is CO2
emission due to the power consumption by wireless modules, isnegligibly smaller than that of the other processes. This result
indicates that the manufacture of cables and connectors andwireless modules are more important than other factors.
Although the assembly, transportation, use and waste treatment
and recycling processes have been addressed as distinct loads
and speculated to have considerable CO2 emissions, theyoccupies 35% to 40% of the total.
Though these estimated values seem to have considerable
errors at current stage, the estimated amount of CO2 reduction
by replacing the harness with wireless harness becomes 203 kg
CO2 per a unit and is not negligible. This indicates the wirelessharness almost certainly has a very good effect to the reductionof CO2 emissions when it is applied to ICT equipments thathave a lot of wire harnesses in them.

142

assembly/maintenance, and reduction of lifecycle CO2
emission as a result.
REFERENCES[1]

G. Leen and D. Heffernan, “Vehicles without wires,” Computing Control
Engineering Journal, pp. 205-211, Oct. 2001.
[2] T. Kobayashi, “Measurements and characterization of ultra wideband
propagation channels in a passenger-car compartment,” IEICE Trans.
Fundamentals, E-89, pp. 3089-3094, Nov. 2006.
[3] R. M. Harman, “Wireless solutions for aircraft condition based
maintenance systems,” in Proc. IEEE Aerospace Conf., vol.6, pp.6-2877 6-2886, 2002.
[4] J. Liu, I. Demirkiran, T. Yang, and A. Helfrick, “Communication schemes
for aerospace wireless sensors,” in Proc. IEEE/AIAA 27th Digital
Avionics Sys. Conf., pp. 5.D.4-1 to 5.D.4-9, Oct. 2008.
[5] A. Matsubara, T. Ichikawa, A. Tomiki, T. Toda, and T. Kobayashi,“Measurements and characterization of ultra wideband propagation
within spacecrafts,” in Proc. Antennas Propagat. Conf., Loughborough,pp. 565–568, Nov. 2009.
[6] S. Redfield, S. Woracheewan, H. Liu, P. Chiang, J. Nejedlo, and R.
Khanna, “Understanding the ultrawideband channel characteristics within
a computer chassis,” IEEE Antennas and Wireless Propag. Lett., pp.
191-194, 2011.
[7] M. Ohira, T. Umaba, S. Kitazawa, H. Ban, and M. Ueba, “Experimental
characterization of microwave radio propagation in ICT equipment for
wireless harness communications,” IEEE Trans. Antenna Propag., vol. 59,
no. 12, pp.4757-4765, Dec. 2011.
[8] S. Kitazawa, M. Ohira, T. Umaba, H. Ban, and M. Ueba, “Investigation ofradio propagation in narrow space in ICT equipments,” (in Japanese)
IEICE Tech. Rep., vol. 110, no. 75, AP2010-32, pp.1-6, June 2010.
[9] P. Bhagwat, “Bluetooth: technology for short-range wireless apps.,” IEEE
Internet Comput., vol. 5, no. 3, pp.96-103, 2001.
[10] Zigbee Alliance. (2005, June)., “ Zigbee specification,” [Online].
Available: http://www.zigbee.org/Specifications.aspx[11] A.W. Astrin, H.B. Li, and R. Kohno, “Standardization for body area
networks,” IEICE Trans. on comm., vol.E92-B, no.2, pp.366-372, Feb.
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[12] H. Hatamoto, N. Kikuchi, K. Okuyama, S. Nakabayashi, S. Shimizu, T.
Miyashita, and I. Takizawa, “A study on characteristics of wirelesscommunication in narrow and complicated spaces,” (in Japanese) IEICE
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[13] K. Ikeda, K. Kagoshima, and S. Obote, “Bandwidth enhancement of alow-profile microstrip antenna electromagnetically coupled with a folded
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[14] M. Hara, K. I. Takahashi, M. Nakamura, T. Kunioka, J. Nakamura, S.
Shimizu, and M. Ueba, “A case study analysis of the CO2 emission
reduction potentiality of the wireless harness within ICT equipment”, in
Proc. 7th Int. Symp. On Environmentally Conscious Design and Inverse
Manufacturing, Kyoto, Japan, Nov-Dec., 2011.

VIII. CONCLUSION
Based on the measurement results of radio propagation
inside ICT equipments, we settled down a consistentspecification to fit for use of sensor data acquisition, and built
up a test system of point-to-multipoint wireless communication.
The system runs with 73 channels using 2.4 GHz ISM band. It
enables to connect 200 sensor-transmitter modules and retrieve
sensor data with dedicated packet protocols. The test system
exhibited good performance even inside ATM, the inside ofwhich is the most severe environment for radio propagation
among the equipments we tested. The application of wirelessharness to ICT equipments seems to offer wider variety of
merits such as weight saving, slim-design, abatement of

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

Narihiro Nakamoto (M’08) was born in Hyogo,
Japan, in 1981. He received the B.E. and the M.E.
degrees in electrical engineering from Kyoto
University, Kyoto, Japan, in 2005 and 2007,
respectively.
In 2007, he joined Mitsubishi Electric
Corporation, Kanagawa, Japan. From 2010 to 2012,he was with ATR Wave Engineering Laboratories,
Kyoto, Japan, and was engaged in research on
microstrip antennas and wireless communication
system inside equipment. In 2012, he returned to Mitsubishi Electric
Corporation. Since then, he has been engaged in research and development onarray antennas for satellite communications and radar systems.
Mr. Nakamoto is a member of the IEICE of Japan.
Hiroshi Ban (M’11) received the B.E., M.E., andD.E. degrees in polymer science and technology fromthe Tokyo Institute of Technology, Tokyo, Japan, in
1982, 1984, and 1991, respectively.
In 1984, he joined Ibaraki ElectricalCommunications Laboratories, Nippon Telegraphand Telephone Corporation, and started his career in
research on photosensitive materials andmicrolithographic technologies. Since 2001, he has
expanded his interest to sensor network systems,
environmental assessment methodologies, and environmental ICT technologies.
He moved to ATR in 2009, and is currently a head of the Department ofEnvironment Communications of ATR Wave Engineering Laboratories. He
received the Photopolymer Science and Technology Award from the
Photopolymer Conference in 1998.
Dr. Ban is a member of the IEICE of Japan, JSAP, and SPSJ.
Tomohiro Oka was born in Kanagawa, Japan, on
August 1968. He received the B.E., and M.E.
degrees from Hosei University, Japan, in 1991 and
1993, respectively.
In 1993, he joined Mitsubishi Electric
Corporation, where he engaged in design anddevelopment of communication systems forgovernment and municipal offices. Since 2010, he
has been engaged in research and development of thewireless communication system inside ICT
Equipment and the future mobile satellite communication systems at ATR
Wave Engineering Laboratories, Kyoto, Japan. He received the prize forscience and technology of the commendation for science and technology by theminister of education, culture, sports, science and technology, in 2009.
Mr. Oka is a member of the IEICE of Japan.
Shoichi Kitazawa received the B.E. and M.E.
degrees from Kinki University, Osaka, Japan, in
1991 and 1993, respectively, and the D.E. degreefrom Osaka Prefecture University, Osaka, Japan, in
2007.
In 1993, he joined Matsushita Nittoh Electric Co.,
Ltd., Kyoto, Japan, where he has been engaged indevelopment on microwave filters. Since 2005, hehas been with ATR Wave Engineering Laboratories,
Kyoto, Japan. His current research interest includes
microwave and millimeter-wave devices.
Dr. Kitazawa is a member of the IEEJ and IEICE of Japan.

143

Kiyoshi Kobayashi (M’94) received the B.E., M.E.
and Ph.D. degrees from Tokyo University of Science,
Japan, in 1987, 1989 and 2004, respectively.
He joined NTT Radio Communication Systems
Laboratories in 1989. Since then, he has been engaged
in the research and development of digital signal
processing algorithms and their implementation
techniques including modulation / demodulation,synchronization control and diversity for satellite andpersonal wireless communication systems. Currently
he is the director of ATR Wave Engineering Laboratories at Advanced
Telecommunications Research Institute International, where he is engaged inresearch on advanced technologies for wireless communications. From 2012,he has been a visiting professor at Muroran Institute of Technology.
Dr. Kobayashi is a member of the AIAA and IEICE of Japan.
Noriyasu Kikuchi was born in Shizuoka, Japan, in
1976. He received the B.E. degree from Yokohama
National University, Japan, in 2000, and the M.E.
degree from Tokyo Institute of Technology, Japan, in
2002.
In 2004, he joined the Oki Electric Industry Co.,
Ltd. His research interests include wirelesscommunication systems.
Mr. Kikuchi is a member of the IEICE of Japan.

Hironobu Hatamoto was born in Hyogo, Japan, in
1981. He received the B.E. degree in electrical andelectronic engineering from Shizuoka University,
Japan, in 2004, and the M.E. and Ph.D. degrees from
Osaka University, in 2006 and 2011, respectively.
In 2009, he joined the Oki Electric Industry
Company Limited. His research interests include
digital signal processing and wireless communication
systems.
Dr. Hatamoto is a member of the IEICE of Japan.
Satoru Shimizu was received the B.E. degree inelectronics engineering from Kyoto University, in
1987 and Ph.D. degree from Chiba University in
1995.
He joined the Oki Electric Industry Co., Ltd. in
1987. He received the Paper Award from the Marine
Acoustics Society of Japan in 1995. His current
research interests are digital signal processing,wireless communication, and control systems.

Minako Hara is a Research Engineer in Green
Material Project at NTT Energy and Environment
Systems Laboratories.
She received the B.S. in applied chemistry from
Tokyo University of Science in 1998, and M.E. and
Ph.D. degrees in applied chemistry from The
University of Tokyo in 2000 and 2005, respectively.
During 2004-2006, as a post-doctor researcher at theJapan Science and Technology Agency, shedeveloped a methodology of environmental impact
assessment and an eco-efficiency index. She joined NTT Energy andEnvironment Systems Laboratories in 2006 and studied environmental
assessment, including life cycle assessment and material flow analysis. She iscurrently studying material recovery technology.
Dr. Hara is a member of the Society of Environmental Science, Japan and theSociety for Environmental Economics and Policy Studies.

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144

Analysis of voice quality over infrastructured
WLAN with Distribution System
Edgar da Silva Arrais*, Claudio de Castro Monteiro** Federal Institute of Education, Science and Technology, AE 310 South Avenue 05 LO, South Master Plan,
Palmas, Tocantins - Brazil.
edgararrais@gmail.com, ccm@ifto.edu.br
Abstract— This assignment presents a study about voice
transmission in WLAN networks based on IP protocol (VoIP).
The assignment presents an statistical analysis of the QOS(Quality of Service) metrics of these networks for VoIP
transmission. We considered to make calls on a scenario where
WDS (Wireless Distribution System) exists. The objetive is toanalyse the impact that the WDS may bring on in voice
transmission. The results found are compared between the twoenvironments and presented in tabular and graph form.
Index Terms—WLAN, VoIP, WDS

I. INTRODUCTIONT

he traffic generated by real time multimedia applications in
mobile devices grows exponencially day-by-day on theinternet. It is common to find such devices connected to theinternet through WLAN, 3G, WiMAX networks among otherwireless access technologies. With mobile users increasingly
thristy for content, connection speed and largely for the
network availability anywhere using the available means on
their devices, we observe one of the greatest challenges faced by
the network managers, the service quality in WLAN networks.
According to jiwire [1] indicators the number of Wi-Fi
hot-spots tripled around the world in four years. This raise isjustified due to the popularization of the mobile devices likesmartphones and tablets. The most part of these users, whichcorresponds to 55% of the mobile users surveyed seeks thewireless access as a way of fast connections with a minor cost
per data traffic. The real time multimedia applications
detached in [1] are the on demand video transmissions, andvoice over IP (VoIP).
Attend this raising demand of applications is one of theManuscript received July 30, 2012. This work was supported in part by the
IFTO - Federal Institute of Education, Science and Technology of Tocantins.
E. S. A. is with the IFTO - Federal Institute of Education, Science andTechnology of Tocantins, BR Brazil (+55 (63)-3229-2200; e-mail: edgararrais@
gmail.com).
C. C. M. is with the IFTO - Federal Institute of Education, Science andTechnology of Tocantins, BR Brazil (+55 (63)-3229-2200; e-mail: ccm@
ifto.edu.br).

challenges faced by the managers, preserving the minimum
quality of the services provided by these networks in any pointof the network. The number of access points (AP) controlled
can raise significantly according to the coverage area and thenumber of users to be integrated to these networks. One of thecommonly used technologies to realize the WLAN networks
distribution is the WDS (Wireless Distribution System). WDS
allows the interconnection among Aps without cables, throughthe own AP's wireless interface. Such characteristic is
extremely interesting when it is intended to expand the network
to regions where the cabling is unable to reach. With WDS
function active the AP starts to realize the function of repeaterof the clients signal to the central AP connected to the main
network (DS). Within this the WLAN signal coverage is
expanded according to the installation of other Aps with WDS.
In contrast one disadvantage is that the reduced bandwidth dueto the sharing with the connected users and a little latency
inserted on the network by WDS.
Real-time applications like VoIP have characteristics ofdelay intolerance, packet loss, lack of bandwidth, voicecompression, therefore it is necessary the use of mechanisms toassure the Quality of Service (QoS). Many other aggravating
already known can be detached when the use of VoIP is relatedwith the transmission means, mainly when the used mean is thewireless pattern.
This assignment has as an objective analyse the impact of
VoIP implementation in WLAN networks, with the use of
WDS through call analysis. To reach the research's objective, It
was necessary to create two WLAN environments. In both
environments we executed VoIP calls and each collect wasprocessed with the use of a packet analyser. Using statistic tools
it was possible to obtain the QOS's metrics involved. The
intention is to appreciate the impact caused by the use of VoIP
applications in environments with WDS.
To evaluate such impacts we realized tests with the purposeof obtaining the QOS's metrics: delay, outflow, jitter and loss.
From the obtained results, we expect to conclude that such
metrics have different values in environments using WDS,
what may compromise the quality of service. This assignmentis organized as follows. In section 2 it is described thebibliographic study. In section 3 it is described the study's
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

proposal and in section 4 it is described the used tools, theprocess of the environments's implementation, methodology
and data analysis. In section 5 are exposed the results. In
section 6 are presented the research's future assignments.
II. RELATED WORKS
Local wireless networks (WLAN) are a viable and economic
alternative for the conventional networks based in cabling. The
great number of mobile devices with wireless interface offeredin the market grows each day, as well as its search due mainly
to the drop of the prices of electronic components. Today it ispossible to find many devices with wireless communicationinterface due to its setup facility, low costs of implementationand the mobility that this interface offers to the device.
WLANs provide the same basic functionalities of cabling
networks (LAN), but combining mobility with access speed
that results in the creation of new habits, relations and ways ofworking. A good example is on the major use of services thatdemand real time requirements like voice applications over IP
and video on mobile devices. The use of multimedia services(voice and video) may demand resources reservations andavailability to have a good functioning and provide a good use
experience to the user. Initially WLAN networks, based on the802.11 pattern were not projected for this kind of traffic. For
the multimedia services to function properly, the associated useof the packets priorization techniques may be necessary.
The pattern WLAN 802.11b and 802.11g can support
theoretically 200 and 900 VoIP sessions respectively, but due tothe inclusion of a great number of headers this number isreduced drastically to only a few sessions. The headers added tothe payload consist in the real time protocols (RTP), UDP, IP
and headers of the MAC layer 802.11. According to [2], the802.11g pattern presents the best performance for the VoIP
traffic in relation to the 802.11a and 802.11b patterns, in bothtypes of CBR (Constant Bit Rate) and VBR (Variable Bit Rate)
traffic. A comparative study was realized using the patterns
802.11a and 802.11g and 802.11b where it was illustrated the
maximum number of sessions and broadcasting rates on the
Nominal and Multiplex-Multicast (M-M), for both types oftraffic CBR and VBR.
As an alternative to raise the number of supported sessions in
WLAN networks, it was proposed the use of the M-M regime tosend packets where the data load reduction was provided for theMulticast packet sending regime. In this mode the headers
UDP, IP and RTP are compressed and rewritten in amini-header and after this combined in a single packet
Multiplex-Multicast, where right after it is sent through the
WLAN to the IP Multicast address. Among the three patterns
analysed in [2] the 802.11g had the best performance comparedto the others, with VBR traffic and M-M regime, the difference
among the others showed to be well significant in the numberof VoIP sessions supported and in the bitrate reached. Using
VBR traffic the flux rate is variable and exist the packet send
economy with periods of silence, what ensure a better audio

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quality transmitted.
Other important factor related in the use of WLAN networks
can be described as an interference of many other networks
very close, producing the interference among them. When
there are many other WLAN networks in a small geographic
area the number of supported sessions is reduced even more,reaching values between 1.63 and 10.34 sessions using only the
GSM codec 6.10. According to [3] one of the main culprit by
the reduction of the VoIP session numbers in areas withmultiple WLAN networks is the mutual interference of CSMA
(Carrier Sense Multiple Access). Many transmission sources
using the same frequency or very close frequencies end up
suffering interference into one another. This way thepercentage of lost packets can raise what is not interesting to
any intolerant to losses application, including VoIP.
To weaken the number of sessions it was proposed in [3] anadmission call control based on a graphic model of conflicts,
where it was possible to raise in 52% the number of VoIP
sessions using the pattern 802.11b and 802.11g, what
represented the number of 2.48 and 14.14 sessions. It was also
proposed the use of three orthogonal channels to reduce thelateral interference, what raised the supported sessions capacity
in the 802.11b and 802.11g patterns to 7.39 and 40,91. Withthe use of CoTDMA (coarse-grained time-divisionmultiple-access) in a partnership with CSMA was possible toobtain an improvement of 35% in the used patterns, that
resulted in 10 sessions to 802.11b and 58 sessions to 802.11g.
Other relevant factor to the VoIP is the voice qualitytransmission in IP networks evaluation. The evaluations can bedone through subjective methods, where people do the listening
evaluation, or in an objective form these based on mathematic
models. In [4] it is referred the recommendation ITU-T P.800
as one of the methods and procedures used for this kind ofevaluation. It consists basically in computing an average
opinion of the listener in a scale from 1 to 5. This scale is
known as MOS (Mean Opinion Score). The MOS scale is thefollowing: 1 – for a horrible quality, 2 – for bad, 3 – for areasonable, 4 – referring to a good quality, and 5 – for an
excellent quality. In [5] it is presented many objective methods,
with emphasis for the E-model presented as an effective
method on the voice quality. The E-model is an evaluation
model based on a concept where “psychological factors on a
subjective scale are auditory”, that is factors gauged from thecall quality loss may be computed generating a scalar model
named R factor. The R factor scale has a variation from 0 to100. The R factor [5] is calculated from the equation (1).
R  ( Ro  Is )  Id  Ie  A(1)

Ro is the noise signal relation; according to therecommendation the pattern value is 94.77. Is is the factor forthe voice quantization, and its pattern value is 1.41. Id
corresponds to the delay degradation. Ie represents the signal
distortion caused by the codecs, loss and buffers. For the codec
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

G.722 used in this study we assumed the value of therecommendation like being 13. And finally A corresponds tothe quantity of degradation that the user is willing to accept. In
this study we assumed the value 10, that in therecommendation refers to the quality in celular networks withmobility. The articles and recommendations studied indicatedalternatives to fix or improve the performance of the use of
VoIP on WLAN networks. In this assignment, accordingly
described previously it will be analysed the use of WDS as away of WLAN network expansion, thus seeking to prove its
performance in relation to the VoIP and the induced impact.
All collected samples in this study were made in realenvironments, without the use of simulators. No QOS policy isimplemented in both environments. As a method of the quality
call's evaluation in this study will be used the E-model.
III. PROPOSAL
The objective of this study is to analyse from a statistic pointof view the variables (random), latency, jitter, packet loss andthe throughput measured in a non-intrusive way in WLAN
networks making use of a VoIP application. We proposed also
to realize an objective measurement according illustrated in
picture 1 about the speaking quality, obtaining the prediction of
the quality call based on the parameters of delay using the
E-Model. In this study the evaluation will be realized throughexperiments in real environments, the first in a WLAN
network without the use of WDS, and second in a WLAN
network with WDS at the Federal Institute of Education,
Science e Tecnology – IFTO.

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that the first does not exist the background traffic, that is,
without any network use at the time of the VoIP calls, it will be
realized a pre-sample with the objective of knowing the averagevalues and average diversion pattern for each one of theanalysed variables. From the calculation of the sample size (2)we seek to know an ideal sample size for both environmentswith 95% trustyworthy of finding the real average values for
quality metrics of service in voice transmissions under IP
networks.
With the sample size defined, with all the samples collected,
processed and with its respective graphics we have the capacity
to analyse the impact caused by the use of WDS in WLAN
networks in VoIP transmissions. The metrics found will beanalysed, compared and discussed, this way we intend to get toa conclusion of the benefits of the use or not of the WDS as away of expansion of a WLAN network and mainly to point out
the positive and negative points of the use of the technology.
A. Materials
The materials used in this study are the ones related on table
I. The access points used were the 3COM 7760[11] model,
referred now like WDS1 and WDS2. The operating systemused on the stations was Ubuntu 10.10 32 bits [6] installed on
the two laptops Asus Eee PC 1015PEM [7] referred now as
station A and Station B. The multimedia applicative thatallowed the accomplishment of the calls between stations A
and B was the Jitsi [8]. The codec used in both environments
was the G.722. In the analysis phase, it was used the statistic
software R [9] to execute the calculations and the Gnuplot [10]to illustrate the results with graphics.
TABLE I
MATERIALS

VERSION

Fig. 1. Non-Intrusive Measurement Objective

The size of the collected sample in this environment will be
realized per point, the size of the sample will be applied in both
environments for statistic analysis purposes. Addresing thestudy of random variables, in these environments we adopt anaverage of the metrics found as a comparative value, because
these values can be different according to the network demand,
time, background traffic among other variables that may
interfere in the result. A pre-sample will be processed in thesecond environment of this study with the objective to serve as
initial parameter for the size of the sample in both
environments, this way avoiding waste of resources andoperating costs with unnecessary collection. Knowing that thesecond environment tends to be to most degraded considering

Computer
System Operational
Softphone
PBX
Codec
Sniffer
Statistical Software
Graphics
Access Point

Asus Eee PC 1015 PEM
Ubuntu 10.10 Maverick Meerkat
Jitsi 1.1
Askozia Embedded 2.0.4
G.722
Wireshark 1.8.0
R-2.15.1
Gnuplot 4.6.0
3Com 7760 a/b/g PoE

On the PBX two SIP counts were added with identification
101@200.129.176.50 and 102@200.129.176.50 to configure
on stations A and B. The packet analyser was installed on
station B with the objective of realizing the capture in parallelto the call answering, accordingly illustrated in picture 1. The
WLAN networks in both environments do not have any
security key set. The distribution of the IP addresses to thestations was realized through DHCP protocol.
B. Environment 1
The first environment proposed was the scenario where the
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

WDS is not implemented. Picture 2 illustrates the environment
created to collect the samples. The environment was configured
in a typical form, being this a BSS (basic service set) composedby an access point pattern 802.11g connected directly throughthe port uplink to the PBX VoIP. The A and B stations wereassociated to this BSS to make use of the VoIP application.

147

Both access points were named in the image as WDS1 and
WDS2. All the collections in this environment were realized ina space outdoors. On table II it is illustrated the distance inmeters in relation to the access point at the moment ofcollection and the number of necessary jumps until DS(distributed system).

Fig. 2. Environment 1 without a WDS.

In this environment were realized calls between the twostations and captured the exchanged packets during thecollected sample. The packets were saved in an arquive of theown wireshark, where each file corresponds to a realized call.
After collecting the files were exported to the CSV format(Comma-separated values). Right after the files were imported
on the R software to realize the statistical analisys. The
calculations that were realized are based in the QOS's metrics:
latency, jitter, packet loss and outflow.
The stations remained without mobility in the moment of therealization of the calls. The distance between A station and theaccess point was about 4 meters and between B station it was 3
meters.
C. Environment 2
The second environment proposed was based in a WLAN
network with WDS existing at IFTO. This environment iscomposed by two access points (AP) forming an ESS (Extended
Service Set) connected through its wireless interfaces. Picture 3
illustrates this environment, as well as two stations that wereconfigured to this network to make calls.

Fig. 4. Collection Points.
TABLE IIDISTANCE ACCESS POINTS
CheckpointAP

DISTANCE

HOPS

P1
P2
P3
P4
P5

DS
WDS1
WDS1
WDS1
WDS2

46,44
41,96
14,76
31,07
16,30

0
1
1
1
2

IV. METHODOLOGY
The study was realized in three steps: data collection,
processing and analysis of the results.
A. Collection
The collection was realized in both environments through theaccomplishment of the VoIP calls between two stations without
mobility to fixed distances in pre-fixed points at IFTO. The
points were locations where there is concentration of users
from the network analysed, being these usually near to theclassrooms or areas of free access. The collection began
executing a pre-sample calls in the environment. It was
adopted a fixed number of twenty calls, with the objective of
indicating the number of necessary calls to be realized to atrustiful degree of 95% (3). Each call made had the average
duration of one minute between the stations. Below follows theformula for the sample calculation (2) used.

Fig. 3. Environment 2 with WDS.

The location of the stations in relation to the access points isdefined on table II. The collection points were named with P1
for the collection point 1 and so on until the collection point P5.

    

n 2
E







2

(2)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

Where, n corresponds to the number of individuals on thesample, is the critical value that corresponds to the2wished trust degree.  is the swerve from the metric's
population pattern studied and E corresponds to the maximum
estimated error. The pre-sample realized of 20 calls was
realized to define the size of initial sample to be realized in both
environments in this study. With the pattern swerve of the calls
and the maximum error admitted it was possible to find asample size of 60 calls, according table II, for the second
environment.

delay, jitter, packet loss and outflow. At the end of the analysis,
we obtained 60 averages of each metric. The data obtained
through the R tool will be illustrated in section 5. To estimate
the population average of the metrics, it was used a break of
95% of trust (3).

Delay
JitterThroughput

36.53
0.006
85.62

STANDARD
DEVIATION
9.04
0.00080.01(3)   z xnWhere, µ corresponds to the average of the population, z thenumber of pattern swerves in the table of normal distribution,
x corresponds to the pattern swerve of the population and n thenumber of elements in the sample.
V. RESULTSTABLE III
PRE-SAMPLE

MEAN

148

ERROR
2.3
0.06
3

SAMPLE
SIZE
60
42
53

The analysis of the calls presented in this study show the
impact provoked use of VoIP in WLAN networks with the useof WDS. In the first environment the average values found are
the values expressed in table IV.
A. Environment 1

The metrics delay resulted in a sample size of 60 calls, being
this the size adopted, inasmuch it comprehends the sample
generated by the other metrics. The packet loss wan notanalysed inasmuch it generated a null loss, taking its invalid
sample for the calculation of the sample size.
B. Analysis
The data analysis was realized with the use of the statistic
software R and the shell script language. This processing had
as an objective to provide the average central tendency and thepattern dispersion measurement of each service quality metricbroached in this study. In the analysis was also realized theobjective measuring of the call quality with the found results.
With the first environment properly mounted the firstcollection of 60 calls can be realized. The calls were always
made with station A as the origin. In each call, the wireshark
software was executed with the purpose of capturing all thereceived and sent packets through the WLAN interface. In thisenvironment, were generated 60 calls through one-day period
in different time. All the capture files were saved in a directoryfor later analysis.
With the use of filters on wireshark, it was possible to realize
the relevant data export used for the statistical calculations.
They were: packet size, average time, protocol, ip origin
address, ip destination address and RTP packet sequence. With
the exported data of each call, it was realized the import ofthese files to the statistic software R. With the information of
the imported calls we realized an exploratory analysis throughdescriptive average statistics, swerve-pattern in each one of theimported files, as in general we obtained the graphics of all
made calls. With the calls from the first environment saved andconverted to one of the formats accepted by the R software, we
imported the 60 files realizing the calculations of the metrics

TABLE IVAMBIENT 1

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

19.98
0.002
85.55
0

19.99
0.006
85.62
0

20
0.008
85.67
0

STANDARD
DEVIATION
0.002
0.00080.01
0In this environment we note the values of the metrics suitableto voice transmission, inasmuch the values attend the
maximum requirements according to the recommendations of
ITU-T. The delay average value found on the calls is within the
maximum value accepted for the VoIP according to ITU-T –G.114's recommendation [12]. The variation of the delay isquite below of what is recommended, as well as the othermetrics favoring this way the environment for the VoIP
transmission. It is important to remember that thisenvironment was analysed initially as a reference to becompared with the results of the second environment where the
network's distribution is done using WDS. The variation of thedelay in each call in this environment was relatively small,according to the illustration of picture 5. The small delay's
variation can be justified by the circumstance of theenvironment being in use only by the two stations, that is, it did
not occur any background traffic at the moment of thecollection.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

149

Fig. 5. Delay Mean

Fig. 7. Throughput Mean

The other metrics also had little variation on the realized
calls sample. The jitter's average is expressed on picture 6.

To estimate the metric's average population collected in thisstudy we used a gap of trust of 95%. Applying the formula (2)we achieved.
TABLE VCONFIDENCE INTERVAL

Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
19.9935
0.006385.62130MEAN
19.99420.006585.6246
0

UPPER
ENDPOINT
19.9949
0.006785.62780µ0.00070.00020.00320

The quality call measurement was realized in this study
using the E-model. Initially it is necessary to know the scalar
factor (R-factor) that varies from 0 to 100. The R factor isrelated with MOS as described on section 3 methodology. In
this first environment we obtained the average factors.
Fig. 6. Jitter Mean

The average jitter on this environment was kept around0.006 ms that is the time's variation in the packets delivery wasvery small. The loss found in this environment was null, as
expected and confirmed. The average throughput was 85.62
bits/s was found in the sample and the same way of othermetrics they remained constant and coherent to theenvironment without representing grand variations, according
to picture 7.

TABLE VI
FACTOR R AND MOS

Ambient 1

FACTOR R MEAN

MOS

70.36

3

All the calls made in the first environment had a quality
average prediction on the scale MOS 3 that suggest the calls
had a reasonable quality, where some users would beunsatisfied with that quality. This value was expected due toused codec in the G.722 study, being for this, expected a MOS
on scale 3.
B. Environment 2
In the second environment collection the average values ofthe metrics are the values expressed by collection points. In thetables below are illustrated the resulting average values fromfive collection points.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

150

TABLE VIIAMBIENT 2 - P1

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

19.98
0.001
81.66
0

20.51
0.01
83.46
0.11

20.96
0.05
85.66
2.09

STANDARD
DEVIATION
0.31
0.01
1.27
0.45

The point P1 was located roughly 46,44 meters of distancefrom the access point. A station was associated to WDS1 at the
moment of the calls. The average delay in this environment was
20,51ms. Compared to the average delay in the firstenvironment this corresponds to an increase of 2,60% on the
average delay. The jitter metric had an increase of 172% in thiscollecting point compared to the environment without the
WDS. The throughput had a loss of 2.52% when compared tothe average throughput of the first environment. The metricpacket loss had an average value of 0.1%. In the firstenvironment we did not have packet loss.

Fig. 10. Throughput P1.

Fig. 11. Packet Loss P1.

Fig. 8. Delay P1.

Estimating the real average of the metrics population to a gapof 95% of trust in P1, we obtained:
TABLE IIXCONFIDENCE INTERVAL

Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
20.4374
0.014983.14370.0010MEAN
20.5159
0.017983.46620.1136UPPER
ENDPOINT
20.59430.020983.78880.2284µ0.07840.00290.32250.1147

Measuring the quality of the realized calls in this point, we
obtained:
TABLE IX
FACTOR R AND MOS

Ambient 2 - P1

FACTOR R MEAN

MOS

69.84

2

Fig. 9. Jitter P1.

Comparing the MOS obtained in the first environment with
MOS of the P1 point was found the R factor equal to 69.84,
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

151

resulting in a inferior scale of the realized calls in theenvironment without WDS. Such fact can be justified due to thestation being associated to the WDS1, where there is a jump
between the access points to reach the PBX.
The second collection point, P2 was at a rough distance of
41.96 meters from the access point WDS1. The metric's
average is illustrated on table X.
TABLE XAMBIENT 2 – P2

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

19.98
0.0006
79.68
0

20.29
0.009
84.38
0.0051

21.48
0.038
85.66
0.17

STANDARD
DEVIATION
0.39
0.007
1.61
0.02

Fig. 14. Throughput P2.

The metric's delay had an increase of 1.51% in the averagewhen compared to the environment 1. Jitter had an increase of
44.27%. Throughput had an average of 1.45% inferior to theobtained in environment 1. Packet loss had an increase of
0.005% in this point.

Fig. 15. Packet Loss P2.

Estimating the population average of the metrics in P2, we
obtained:
TABLE XICONFIDENCE INTERVAL
Fig. 12. Delay P2.
Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
20.1957
0.007683.97220.0019MEAN
20.2966
0.009484.3800
0.0051UPPER
ENDPOINT
20.3974
0.011384.7877
0.0123µ0.10080.00180.40770.0071

Measuring the quality of the calls realized in this point, we
obtained:
TABLE XIIFACTOR R AND MOS

Ambient 2 – P2

Fig. 13. Jitter P2.

FACTOR R MEAN

MOS

70.06

3

The MOS obtained in P2 was the same obtianed in P1. The
rising of the average of delays and jitter can be justified too by
the aditional jump now realized in this collection point to reachthe PBX. This jump inserted a significant delay and jitter in the
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152

calls, this way the MOS was obtained due to the scale for theobtained value 3 comprehend values between 70 and 80 for the
R factor.
The collection point P3 was at a distance of 14.73 metersfrom the WDS1. The average of the metrics is expressed ontable XIII.
TABLE XIIIAMBIENT 2 – P3

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

20.32
0.000004
80.33
0

20.64
0.014
82.94
0.16

21.31
0.034
84.21
4.78

STANDARD
DEVIATION
0.18
0.007
0.75
0.76

In this point the average delay had an increase of 3.24%
compared to the first environment. Jitter had an increase of
125.05%. Throughput had an average of 3.13% less whencompared to environment 1. Packet loss had an increase of
0.16%.

Fig. 18. Throughput mean in P3.

Fig. 19. Packet loss mean in P3.

Fig. 16. Delay mean in P3.

Estimating the average population of the metrics in P3,
according to table XIV, and calculating the MOS we obtained:
TABLE XIVCONFIDENCE INTERVAL

Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
20.59450.012882.7518
0.0262MEAN
20.64240.014882.94250.1683UPPER
ENDPOINT
20.6904
0.016783.1332
0,3629µ0.04790.00190.19070.1946TABLE XVFACTOR R AND MOS

Ambient 2 – P3

Fig. 17. Jitter mean in P3

FACTOR MEAN

MOS

69.71

2

In P3 the collection was realized near to the access point. In
this point we obtained a MOS equal 2. This loss in performancecan be justified by the raise of the average delay and jitter, what
degraded the quality of the call. The fourth collecting point P4
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point we obtained the following averages:
TABLE XVIAMBIENT 2 – P4

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

19.99
0.003
77.44
0

20.60
0.016
83.10
0.03

22.10
0.041
85.64
2.05

STANDARD
DEVIATION
0.42
0.009
1.66
0.26

In P4 the average delay had an increase of 3.07% in relation
to the first environment. Jitter had an increase of 153.21%.
Throughput an average of 2.94% less when compared toenvironment 1. The packets loss in this collection point was
0.034%.
Fig. 22. Throughput mean in P4.

Fig. 20. Delay mean in P4.
Fig. 23. Packet loss mean in P4.

Estimating the population average of the metrics in P4 (seetable XVII), and calculating MOS (see table XVIII) we
obtained:
TABLE XIVCONFIDENCE INTERVAL

Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
20.50250.014282.68130.0328MEAN
20.6091
0.016683.10350.0342UPPER
ENDPOINT
20.7156
0.0191
83.52560.1013µ0.10650.00240.4221
0.0671

Measuring the quality of the calls made in this point, we
obtained:
TABLE XVIIIFACTOR R AND MOS

Fig. 21. Jitter mean in P4.

Ambient 2 – P4

FACTOR R MEAN

MOS

69,75

2

In the collection point P4 we obtained a R factor equal to
69.75. This value led the MOS to be measured on a scale like 2,
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what turns the quality call bad for many users. The collection
point P5 was at a distance of 16.30 meters from WDS2. In thiscollection the packets transmitted through the station makes
use of two jumps to reach DS.
TABLE XIXAMBIENT 2 – P5

Delay
JitterThroughput
Packet Loss

MIN

MEAN

MAX

20,54
0,00001
59,74
0

22,78
0,028
75,57
0,98

28,65
0,063
83,32
24,97

STANDARD
DEVIATION
1,84
0,012
5,62
3,42

In P5 the average delay had an increase of 13.97% in relation
to the first environment. In this collection we computed theworst performance for the metric's delay. Jitter had an increaseof 326% compared to the first environment. Throughput had an
average of 11.78% less than when compared to the firstenvironment. Packet loss was computed in 0.98%.

Fig. 26. Throughput mean in P5.

Fig. 27. Packet loss mean in P5.
TABLE XIVCONFIDENCE INTERVAL

Fig. 24. Delay mean in P5.

Delay
JitterThroughput
Packet Loss

LOWER
ENDPOINT
22.32160.024874.1502
0.1170MEAN
22.7876
0.028075.5734
0.9831UPPER
ENDPOINT
23.2537
0.031376.9966
1.8492µ0.46600.0032
1.42320.8661TABLE XXI
FACTOR R AND MOS

Ambient 2 – P5

Fig. 25. Jitter mean in P5.

FACTOR MEAN

MOS

67,57

2

In P5 was observed the worst performance among the fivecollection points. This fact can be justified due to the transited
packets among the stations make use of the 2 jumps using theaccess points WDS2 and WDS1 to reach the PBX. Thisadditional jump influenced directly in the outflow's
performance, increase of the delay and jitter, as well as the loss
began to have a considerable value at this point.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

[4]

VI. FINAL CONSIDERATIONSIn this study we analysed the use of VoIP applications in
WLAN environments with and without using WDS. Throughthe execution of calls in two environments we observed andcomputed significant differences on the analysed metrics (seetable XXII). In both environments analysed we found themetrics favorable for voice transmissions on IP networks.
TABLE XXII
ALL AMBIENTS

Ambient 1
Ambient 2 P1
Ambient 2 P2
Ambient 2 P3
Ambient 2 P4
Ambient 2 P5

155

DELAY

JITTER

THROUGHPUT

PACKET LOSS

19,9942
20,5159
20,2966
20,6424
20,6091
22,7876

0,00650,01790,00940,01480,0166
0,0280

85,62
83,46
84,38
82,94
83,10
75,57

0
0,113
0,005
0,168
0,034
0,983

We observed in the environment where WDS is practiced thedifference in the computed metrics. These metrics havedifferent values when the collection point is using other points
to take the packets to the DS. In our study the collection point
P5 was the only one analysed in what the packets transmitted
need to pass through two access points to reach the DS. It is
possible that exists a relation between the number of jumps andthe use of WDS. We observed the different metrics inenvironments with a number of jumps superior to 1. These
metrics were observed on point P5 where the delay's average
suffered a considerable growth. The other metrics also had its
average values altered. Jitter had an increase and throughput
suffered a loss. Packet loss came close to 1%.

L. Sun. “Speech Quality Prediction for Voice over Internet Protocol
Networks”. 2004 198 f. Thesis (Doctor of Philosophy in Computing,Communications and Eletronics) Faculty of Thecnology – University of
Plymouth, Plymouth – United Kingdom.
[5] ITU-T The E-Model, a computational model for use in transmission
planning. Recommendation G.107. Geneva – Switzerland. 2005.
[6] Canonical. Ubuntu 10.10. [Online]. Available: http://www.ubuntu.com[7] AsusTeK
ComputerInc.
[Online].
Available:
http://br.asus.com/Eee/Eee_PC/Eee_PC_1015PEM[8] Jitsi Communicator. [Online]. Available: https://jitsi.org[9] R. The R Project for Statistical Computing. [Online]. Available:
http://www.r-project.org/[10] Gnuplot. [Online]. Available: http://www.gnuplot.info/[11] 3Com.[Online. Available: http://h20000.www2.hp.com[12] ITU-T G.114. One-way transmission time. Recommendation. 05/2003.

Edgar S. Arrais. Graduated in of Technology in
Computer Networks at University Lutheran Center ofPalmas (2009). Pos graduate in telematics at the Federal
Institute of Education, Science and Technology ofTocantins (2012). He is currently Manager of Networks Department of Public Safety Tocantins. He has experiencein Computer Science with emphasis in Computer Networks,
acting on the following topics: embedded system, software,
open source, hardware and management.

Claudio C. Monteiro. Graduated in Data Processing
Technology from the University of Amazonia (1990),
Masters in Computer Science from Universidade Federal
da Paraíba (1997). PhD in Electrical Engineering at the
University of Brasilia - UNB (2012). In addition, is teacher
at the Federal Institute of Education, Science andTechnology of Tocantins (IFTO - palmas.ifto.edu.br),leader of the Research Group on Networks Computers(Gredes - gredes.ifto.edu.br). He has experience in computer science, withemphasis on wireless networks, network protocols, QoS / QoE and operating
systems.

VII. FUTURE ASSIGNMENTS
As future assignments we aim yet to realize the measurementin other 5 points scattered through IFTO. In these additional
points we expect to find metrics that degrade even more the
VoIP applications. These 5 points use a higher quantity ofjumps between the access points to reach the DS. As a futureassignment it remains to use a multiple regression model toestimate the MOS using the variables of QOS looking for a
relation between the analysed variables.
Other assignment to be realized in this same environment is
to make calls with the stations in movement. We tried toobserve the variations of the metrics at the moment of thechange of BSS, looking to compute the delay and the influenceof it in the VoIP applications.
REFERENCES
[1]
[2]

[3]

Jireware, ”JiWire Mobile Audience Insights Report 2012 ”. Available:
http://www.jiwire.com/
M. I. Anis and M. Z. Khan, J. Inan, R. Azmatullah, “Comparison between
the ieee 802.11 x standards of voip using the cbr and vbr voice schemes”. In
Radio and Wireless Symposium, 2008 IEEE. IEEE, 2008.
A. Chan and S. C. Liew. “Performance of voip over multiple co-located ieee802.11 wireless lans”. IEEE Transactions on Mobile Computing. 2008.

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A Hybrid Chaos- Fuzzy -Threshold Steganography
Algorithm for Hiding Secure Data
Mazhar Tayel, Hamed Shawky, Alaa El-Din Sayed Hafez
Electrical Engineering Department, Faculty of Engineering, Alexandria University
Hamed_shawky@yahoo.com, Alaahafez@ieee.org

Abstract— Security problem is an essential requirement fordata transmission. Although the will known methods of dataencryption attain certain security levels, they make the encrypted
data unreadable, and unnatural, this attracts some unintended
observer attention. So it is required to search for a higher security
approach. The present paper introduces a new hybrid
steganography algorithm to achieve a higher level of security. The
proposed algorithm consists of a compound processing using
Chaos – Fuzzy- Thresholding steganography method (CFT).
Implementation of the proposed CFT hybrid algorithm shows ahigher degree of security level. The hidden data reconstruction isbased on an inverse CFT steganography algorithm processing.
Index Terms— Chaos, Steganography, Fuzzy, thresholding

I. INTRODUCTIONS

ome confidential data might be stolen, copied, modified

or destroyed by an unintended observer. Therefore, security
problems become an essential issue. Encryption is a wellknown procedure for secure data transmission. The frequently
used encryption methods include RSA and DES algorithms.
Although these two methods achieve certain security effects,
they make the secret messages unreadable and unnatural. These
unnatural messages usually attract some unintended observer's
attention. This is the reason a new security approach called“Steganography” arises [1,2]. Steganography literally meanscovered writing and is the art of hiding secret messages within
another seemingly innocuous message, or carrier [3]. With theadvent of digital technology, the list of carriers has been madeto include, images, text, e-mails, audio and video messages.
Some Steganography utilities use secret keys. It can distinguish
two kinds of keys: steganographic keys and cryptographic keys
[4]. A steganographic key controls the embedding andextracting process. For example, it can scatter the message to beembedded over a subset of all suitable places in the carrier
Manuscript received April 3, 2012. This work was supported by ElectricalEngineering Department, Faculty of Engineering, Alexandria University,
Alexandria, Egypt.
Mazhar Basyouni Tayel, Electronic Engineering Professor, ElectricalEngineering Department, Faculty of Engineering, Alexandria University,
Alexandria, Egypt (e-mail: Mazhar_Tayel@ yahoo.com).
Alaa El-Din Sayed Hafez, Affilate Instructor, Electrical Engineering
Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt(e-mail: Alaaahafez@ yahoo.com).
Hamed Shawky, Post Graduate Student (Ph.D), Electrical Engineering
Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt(e-mail: Hamed_shawky@ yahoo.com).

medium. Without the key, this subset is unknown, and eachsample used to detect embedding by a statistical attack is a
mixture of used and unused places which spoils the result. A
cryptographic key is used to encrypt/decrypt the message [5,6].
This paper describes a new method of steganography
technique for hiding large volumes of data using digital images,
text, and audio as a cover medium. Before embedding themessage in the cover medium, a logistic map chaotic
encryption technique are used to obtain an undefined(encrypted) message then compress the image color (or data)
range using Fuzzy logic compressor and embed the message inthe cover media by using the proposed steganography method.
This will be discussing in the next section in this work.
II. THEORITICAL DESCRIPTION
To provide a Theoretical description of steganography,
terms and concepts should first be explained an overview ofthe different kinds of steganography is given at a later stage.
Some definitions common to the steganography field: cover
medium, this is the medium in which the data hide for it.
Embedded message, this is the hidden message that want to
put it in the cover medium. Stego-key, this is represented bysome secret information, which is needed in order to extractthe embedded message from the stego-medium. Stegomedium, this is the final piece of information that the casual
observer can see. We can define this simple formula,cover medium + embedded message = stego_message.
There are two general approaches to classify
steganographic systems. The first approach is based on thetype of cover file while the second approach is based on thehiding method or the layout of modification used in theembedding process [7]. These two general classification
approaches of steganography are explained as follows, CoverType Based Classification, Since many kinds of digital media
can be used as cover files of steganography, the first approachof classification breaks down steganography according to thetype of the cover file used. However, the properties of thesecover files vary from one type to another and these properties
control how the secret data can be hidden in these cover files.
To this end, knowing the type of cover file can give us an
indication or idea where the secret data might be hidden.
Mostly, steganographic systems are classified according to thecover file used. Accordingly, different steganography types
can be distinguished such as: image, audio, video, text, andCopyright ⓒ GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013
ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013HTML
steganography[7].
Hiding
Method-BasedClassification, Regardless of the cover type used for datahiding, steganography can be classified according to themethod used to hide secret data. Furthermore, this approach ofsteganography classification is the most preferred approach inthe steganography research community. Accordingly, there arethree ways to hide secret data in cover files: insertion based,
substitution-based, and generation-based method [8].
Many steganography techniques have been proposed
during the last few years. These techniques differ in themechanism or principle being used to hide a secret message.
Therefore, there are six categories of steganography
techniques: spread spectrum techniques, statistical methods,
distortion techniques, cover generation techniques,
substitution systems, and transform domain techniques [9].
Spread spectrum communications define as" the process of
spreading the bandwidth of a narrowband signal across a wideband of frequencies”. In spread spectrum steganography, thefrequency domain of the cover file is considered to be acommunication channel and the secret message as a signal thatis transmitted through it. Since the secret message is spreadthrough a wide frequency band, this technique is relatively
robust against stego file modification or message removal [9].
Statistical Techniques embed only one bit of secret data in acover file. Therefore, it is known as “1-bit” steganography
scheme. If “1” is hidden in a cover file, some statisticalcharacteristics (e.g. entropy and probability distribution) of
this cover file must be changed significantly to clearly indicate
the existence of a message. However, if the hidden bit is “0”,the cover file is left unmodified. Therefore, this techniqueentirely depends on the ability of the receiver to differentiate
between changed and intact cover files [7, 9]. Distortion
Techniques, which means that a receiver does not need theoriginal cover file to extract the hidden message from thecorresponding stego file. However, if a distortion technique isused, the receiver requires the original cover file in order torecover the secret message. For a receiver, the embedded
message is the difference between the modified cover filereceived (the stego file) and the original cover file [6]. Cover
Generation Techniques is preventing such kind of detection
since only stego files are available and there is no cover files
used. The major limitation of this method is the limited stego
files which can be generated. Moreover, the generated stego
files might be unrealistic files for end users (e.g. an image
contains different shapes and colors without any sense or atext without any meaning). Therefore, the main media for suchtechniques are random-looking images and English text files[6]. Substitution Systems, it is important to find out someareas or data that can be modified without having any
significant effects on this cover file. Therefore, a secretmessage can be embedded by replacing the redundant or
insignificant parts of a cover file with secret message bits,
without adding any significant noise to this cover file.
Generally, digital covers have a large number of redundant
bits (e.g. least significant bits (LSB)) [9]. In the substitution
technique of steganography, the bits of the secret message
substitute the LSB of the bytes of the cover file withoutcausing a drastic change to this cover file. Moreover, the LSB
technique is a spatial domain technique since it embeds thesecret bits directly in the cover file. Since LSB substitution

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technique is relatively quick and easy to use, it is the mostcommon technique used for digital steganography andespecially with digital images. However, the embedded
information using the LSB technique is highly vulnerable forimages as a covering media, the LSB of a pixel is replacedwith an M’s bit. If we choose a 24-bit image as cover, we canstore 3 bits in each pixel by modifying the LSBs of R, G and B
array. To the human eye, the resulting stego image will lookidentical to the cover image [11, 12, 13]. Transform Domain
Techniques unlike spatial domain techniques (e.g. LSB
technique), transform (frequency) domain techniques hide
secret data in significant parts of the cover file. Therefore,
frequency domain techniques are considered more robust to
attacks than spatial domain techniques. Hence, most of robust
steganographic systems known today rely on frequency
domain techniques. There are many transforms used to map asignal into the frequency domain. Discrete cosine transforms(DCT), discrete wavelet transforms (DWT), and discrete
Fourier transform (DFT) are methods used as mediums toembed secret data in digital images. However, when we add aslight noise or secret data to some frequency domaincomponents, it changes the whole image rather than changing
only this part of the image. Thus, secret and embedded data
will be spread across the entire image and will not be
concentrated on one certain area or region [13, 14, 15].
A. Chaotic Mapping
Chaos is a dynamical system that is extremely sensitive to its
initial conditions. It is a deterministic nonlinear system thathas random-like behaviours. Chaos theory has become a new
branch of scientific studies today. Discrete chaotic dynamic
systems are used in this system. The implemented map islogistic map, which is one of the simplest form of one
dimensional chaotic maps and mathematically its equation canbe written as [13]:wi +1 = μwi (1 − wi )(1)

where wi is a real value in (0,1), and μ is bifurcation parametersatisfying 0 ≤ μ ≤ 4. The logistic map is chaotic on thecondition 0.35699≤ μ ≤ 4.
B. Fuzzy Logic Compressor
Fig. 1. shows the basic structure of a Fuzzy logic system. The
main building units are a fuzzification unit, a Fuzzy logic
reasoning unit, a knowledge base, and a defuzzification unit.
Defuzzification is the process of converting inferred Fuzzy
control actions into a crisp control action. The fuzzyknowledge-base has a rule-base that maps a Fuzzy inputs
variable, E, into a Fuzzy output, U [16].

Fig. 1. Basic Structure of a Fuzzy Logic Controller (FLC)

This can be expressed by a linguistic statement such as:
E→U (conditions E implies condition U)
which may be written as:
IF E THEN U.
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The Fuzzy knowledge-base also has a database defining thevariables. A Fuzzy variable is defined by a Fuzzy set, which inturn is defined by a membership function. Fuzzy reasoning isused to infer the output contributed from each rule. The Fuzzy
outputs reached from each rule are aggregated and defuzzified
to generate a crisp output. In the design of an FL system it isassumed that, a solution exists, the input and output variables
can be observed and measured, an adequate solution is
acceptable, and a linguistic model can be created based on theknowledge of a human expert. In order to model a systemlinguistically, one needs to identify the input and output
variables of the process to be controlled and define subsets thatcover the universe of discourse of each variable and assign alinguistic label to each one. For example, the linguistic variable
speed may be defined as three Fuzzy subsets: slow, medium,and fast as shown in Fig. 2. Form a rule-base by assigning
relationships between inputs and outputs. Determine adefuzzification method to be used to generate a crisp output
from the Fuzzy outputs generated from the rule-base.

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form of uncertainty happens to arise because of imprecision,
ambiguity, or vagueness, then the variable is probably Fuzzy
and can be represented by a membership function [16].
E. Defuzzification
For a given input, several IF/THEN rules could be launchedat the same time. Each rule would have a different strength,
because a given input may belong to more than one Fuzzy set,
but with different membership values. In general, the output of
the Fuzzy reasoning would involve more than two Fuzzy sets;
therefore, one can write:kF = ∪ Fi

(2)

i =1

Assuming the support of F is X = {x1, x2, x3,…} then for xi ∈ X, F(xi) = wi indicates the degree to which each is suggested by
the rule-base as a good output for the given input. The
defuzzification operation is applied on F to determine the best
crisp output. Numerous defuzzification methods have beensuggested in the literature; however, sometimes different
authors name the same method differently. No method has
proved to be always more advantageous than the others. The
selection of which method to use depends primarily on theexperience of the designer [17].
F. Centroid method
This method is also known as the centre of mass, or centre ofgravity method. It is probably the most commonly useddefuzzification method. The defuzzified output, x* is definedby,Fig. 2. Possible graphical representation of Fuzzy sets [18]C. Membership Functions
Discrete and continuous membership functions of a Fuzzy
set are intended to capture a person’s thinking. Fuzzy
membership functions can still be determined subjectively inpractical problems based on an expert’s opinion. In such asituation one can think of membership functions as a techniqueto formalize empirical problem solving that is based on
experience rather than the knowledge of theory. The expert’s
way of thinking can be captured either directly or through aspecial algorithm. Such determination could become more
focused by physical measurements if the need arises. Available
frequency histograms and other probability data can also help
in constructing the membership function. It is important,however, to note that membership function values, or grades ofmembership, are not probabilities and they do not have to add
to 1. Membership construction can be further simplified by
selecting their form from the smaller family of the commonlyused ones.
D. Fuzzification
Fuzzification is the process of making a crisp quantity Fuzzy.
We do this by simply recognizing that many of the quantities
that we consider to be crisp and deterministic are actually notdeterministic at all; they carry considerable uncertainty. If thex* = ∫μ F ( x ) xdx
μ F (x ) dx

(3)

where the symbol ∫ denotes algebraic integration. Fig. 3.
Represent this method graphically [18].

Fig. 3. Centroid Method

G. Centre of largest area method
This method is applicable when the output consists of at least
two convex Fuzzy subsets which do not overlap. The result isbiased towards a side of one membership function.
III. THE PROPOSED METHOD
The proposed steganography algorithm started with increasing
the original image pixels from byte to word color capacity,and then distributes the hidden-image pixel randomly within
the lower byte of the cover image pixels using chaos
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013
ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013

distribution. Then this stego image processed using Fuzzy
logic compressor to invert and compress its color range over a
specific threshold color decided by the value of the uppersignificant bits of the image i.e. 192 to 255. The stego-image
then is ready for transmission. The original image is separated
from the received stego-image at the first stage of the receiverand invert and expand the color range using Fuzzy logic
decompressor. The initial condition of the chaotic random
sequence is used to collect the stego-image from the lower
byte of the pixels. Then the hidden-image is reconstructed.
Fig. 4. demonstrates the proposed method graphically. The
same idea is performed with any kind of data such as text orsound signals.

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(a)

(b)

(c)

Fig. 4. The proposed Steganography algorithm

IV. RESULTS
The input and output membership functions of the proposed
Fuzzy logic compressor (FLC) and Fuzzy logic Decompressor(FLD) is shown in Fig. 5. Fig. 6 demonstrate the transfer
characteristics of the compressor and decompressor. To verify
the effectiveness of the proposed method, it has been
implemented on two different test images with two
hidden-images. Fig. (7,8), (a) shows the original cover image
before processing, (b) illustrate the hidden image before
processing, (c) the chaos based distributed hidden image, (d)the compressed image (e) demonstrate the stego-image after
embed the hidden image chaotically and compressed within its
pixels, and (f) shows the reconstructed image afterreconstruction from the stego-image. The mean square error(MSE) for the first image is 5.9143e+004 which achieve 48.16
dB peak signal to noise ratio (PSNR), MSE for the second
image is 5.2445e+004 which achieve 49.13 dB peak signal tonoise ratio (PSNR). These values of PSNR are acceptable forhiding the image data and can’t notice by inspection.

(d)Fig. 5. Membership functions (a) input of the FLC, (b) output of the FLC, (c)input of the FLD, and (c) output of the FLD

V. CONCLUSION
Figures The paper introduced a new hybrid chaos-Fuzzythresholding (CFT) steganography algorithm for hiding themultimedia data, image, text, or sound. The algorithm
coordinates the data in the image dimensions using chaos
distribution arrangement and performs a negative color
compression using Fuzzy logic Scheme. The data is embeddedwith the original image in the pixels least significant bits, so
can't appears within the image. The received image is separated
and color expanded then rearranged using the initial conditionof the chaos coordination. The algorithm is implemented using
Matlab program for two test images. The results show a goodhiding for the tested data in the original images with highdegree of security if steganalysis is performed on the composed
original image.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013(a) Cover Image(b) Hidden Image(c) Chaos distributed Image(b) fuzzy compressed image(a) Stego-Image(b) Reconstructed Image(a)

Fig. 8. Stages of test image (2)(b)
Fig. 6. Fuzzy logic transfer characteristics (a) T.C. of the FLC, and (b) T.C. of
the FLD(a) Cover Image(b) Hidden Image(c) Chaos distributed Image(b) fuzzy compressed image(a) Stego-Image(b) Reconstructed Image

Fig. 7. stages of test image (1)REFERENCES[1] Uma Devi.G MS by Research – CSE" Steganography-Survey on File
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 2, Issue 1, January 2013
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Mazhar Basyouni Tayel is professor in Faulty ofEngineering, Alexandria University, Alexandria, Egypt.
He holds B.Sc. in Electronics and Communications fromFaculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering fromFaculty of Engineering, Alexandria University. He
tought many technical courses in Electrical andElectronic (Analog and Digital) system design and
Implementation, works as System Engineer for more than
20 years, teach up to 30 undergraduate, postgraduate
subjects, supervising more than 75 thesis, publish morethan 150 papers in different international conferencse, Forums and Journals.

161

1616

Alaa El-Din Sayed Hafez is an affiliate instructor in
Faculty of Engineering, Alexandria University,
Alexandria, Egypt. He was born in Cairo, Egypt in
1967and became a member of IEEE in 2000, a Senior
member of IEEE in 2011. He holds B.Sc. in Electronics
and Communications from Faculty of Engineering,Alexandria University, M.Sc. in Electronics andCommunication from Arab Academy for Science andTechnology and Maritime Transport, Alexandria. He
also holds M.Sc. and Ph.D. in Electrical Engineeringfrom Faculty of Engineering, Alexandria University. He
received many technical courses in Surveillance Radar design and
Implementation, work as Radar System Engineer for more than 5 years, teachup to 20 undergraduate subjects, supervising more than 12 thesis, publish morethan 50 papers in different international conferences and journals.

Hamed Shawky Zied is a Post Graduate Student (Ph.D.),
Alexandria University, Alexandria, Egypt and became aMember of IEEE in 2012. He was born in Minoufia, Egypt
in 1973. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria
University, M.Sc. in Electrical Engineering from Facultyof Engineering, Alexandria University. He received manytechnical courses in electronic engineering design and
Implementation.

Mazhar Basyouni Tayel is professor in Faulty of Engineering, Alexandria
University, Alexandria, Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering from Faculty of Engineering,Alexandria University. He tought many technical courses in Electrical andElectronic (Analog and Digital) system design and Implementation, works as
System Engineer for more than 20 years, teach up to 30 undergraduate,
postgraduate subjects, supervising more than 75 thesis, publish more than 150
papers in different international conferencse, Forums and Journals.
Mazhar Basyouni Tayel is professor in Faulty of Engineering, Alexandria
University, Alexandria, Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering from Faculty of Engineering,Alexandria University. He tought many technical courses in Electrical andElectronic (Analog and Digital) system design and Implementation, works as
System Engineer for more than 20 years, teach up to 30 undergraduate,
postgraduate subjects, supervising more than 75 thesis, publish more than 150
papers in different international conferencse, Forums and Journals.
Mazhar Basyouni Tayel is professor in Faulty of Engineering, Alexandria
University, Alexandria, Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering from Faculty of Engineering,Alexandria University. He tought many technical courses in Electrical andElectronic (Analog and Digital) system design and Implementation, works as
System Engineer for more than 20 years, teach up to 30 undergraduate,
postgraduate subjects, supervising more than 75 thesis, publish more than 150
papers in different international conferencse, Forums and Journals.
Mazhar Basyouni Tayel is professor in Faulty of Engineering, Alexandria
University, Alexandria, Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering from Faculty of Engineering,Alexandria University. He tought many technical courses in Electrical andElectronic (Analog and Digital) system design and Implementation, works as
System Engineer for more than 20 years, teach up to 30 undergraduate,
postgraduate subjects, supervising more than 75 thesis, publish more than 150
papers in different international conferencse, Forums and Journals.
Mazhar Basyouni Tayel is professor in Faulty of Engineering, Alexandria
University, Alexandria, Egypt. He holds B.Sc. in Electronics andCommunications from Faculty of Engineering, Alexandria University, He also
holds M.Sc. and Ph.D. in Electrical Engineering from Faculty of Engineering,Alexandria University. He tought many technical courses in Electrical andElectronic (Analog and Digital) system design and Implementation, works as

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