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Volume 114, Issue 3,March 2010

www.sensorsportal.com ISSN 1726-5479

Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, fax: +34 93 4011989, e-mail:[email protected]

Editors for Western EuropeMeijer, Gerard C.M.,Delft University of Technology, The Netherlands

Ferrari, Vittorio, Universit di Brescia, Italy

Editor South America

Costa-Felix, Rodrigo, Inmetro, Brazil

Editor for Eastern EuropeSachenko, Anatoly, Ternopil State Economic University, Ukraine

Editors for North AmericaDatskos, Panos G., Oak Ridge National Laboratory, USA

Fabien, J. Josse, Marquette University, USA

Katz, Evgeny, Clarkson University, USA

Editor for AsiaOhyama, Shinji, Tokyo Institute of Technology, Japan

Editor for Asia-Pacific

Mukhopadhyay, Subhas, Massey University, New Zealand

Editorial Advisory Board

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Annamalai, Karthigeyan, National Institute of Advanced Industrial Science

and Technology, Japan

Arcega, Francisco, University of Zaragoza, SpainArguel, Philippe, CNRS, France

Ahn, Jae-Pyoung, Korea Institute of Science and Technology, Korea

Arndt, Michael, Robert Bosch GmbH, Germany

Ascoli, Giorgio, George Mason University, USA

Atalay, Selcuk, Inonu University, Turkey

Atghiaee, Ahmad, University of Tehran, Iran

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Avachit, Patil Lalchand, North Maharashtra University, India

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Bahreyni, Behraad, University of Manitoba, Canada

Baliga,Shankar, B., General Monitors Transnational, USA

Baoxian, Ye, Zhengzhou University, China

Barford, Lee, Agilent Laboratories, USA

Barlingay, Ravindra, RF Arrays Systems, India

Basu, Sukumar, Jadavpur University, India

Beck, Stephen, University of Sheffield, UKBen Bouzid, Sihem, Institut National de Recherche Scientifique, Tunisia

Benachaiba, Chellali, Universitaire de Bechar, Algeria

Binnie, T. David, Napier University, UK

Bischoff, Gerlinde, Inst. Analytical Chemistry, Germany

Bodas, Dhananjay, IMTEK, Germany

Borges Carval, Nuno, Universidade de Aveiro, Portugal

Bousbia-Salah, Mounir, University of Annaba, Algeria

Bouvet, Marcel, CNRS UPMC, France

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Cai, Qingyun, Hunan University, China

Campanella, Luigi, University La Sapienza, Italy

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Chakrabarty, Chandan Kumar, Universiti Tenaga Nasional, Malaysia

Chakravorty, Dipankar, Association for the Cultivation of Science, India

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Chaudhari, Gajanan, Shri Shivaji Science College, India

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Chen, Rongshun, National Tsing Hua University, Taiwan

Cheng, Kuo-Sheng, National Cheng Kung University, Taiwan

Chiang, Jeffrey (Cheng-Ta), Industrial Technol. Research Institute, Taiwan

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Chung, Wen-Yaw, Chung Yuan Christian University, Taiwan

Corres, Jesus, Universidad Publica de Navarra, Spain

Cortes, Camilo A., Universidad Nacional de Colombia, Colombia

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De Stefano, Luca, Institute for Microelectronics and Microsystem, Italy

Deshmukh, Kiran, Shri Shivaji Mahavidyalaya, Barshi, India

Dickert, Franz L., Vienna University, Austria

Dieguez, Angel, University of Barcelona, Spain

Dimitropoulos, Panos, University of Thessaly, Greece

Ding, Jianning,Jiangsu Polytechnic University, ChinaKim,Min Young, Kyungpook National University, Korea South

Djordjevich, Alexandar, City University of Hong Kong, Hong Kong

Donato, Nicola, University of Messina, Italy

Donato, Patricio, Universidad de Mar del Plata, Argentina

Dong, Feng, Tianjin University, China

Drljaca, Predrag, Instersema Sensoric SA, SwitzerlandDubey, Venketesh, Bournemouth University, UK

Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, Germany

Erdem, Gursan K. Arzum, Ege University, Turkey

Erkmen, Aydan M., Middle East Technical University, Turkey

Estelle, Patrice, Insa Rennes, France

Estrada, Horacio, University of North Carolina, USA

Faiz, Adil, INSA Lyon, France

Fericean, Sorin, Balluff GmbH, Germany

Fernandes, Joana M., University of Porto, Portugal

Francioso, Luca, CNR-IMM Institute for Microelectronics and

Microsystems, Italy

Francis, Laurent, University Catholique de Louvain, Belgium

Fu, Weiling, South-Western Hospital, Chongqing, China

Gaura, Elena, Coventry University, UK

Geng, Yanfeng, China University of Petroleum, China

Gole, James, Georgia Institute of Technology, USAGong, Hao, National University of Singapore, Singapore

Gonzalez de la Rosa, Juan Jose, University of Cadiz, Spain

Granel, Annette, Goteborg University, Sweden

Graff, Mason, The University of Texas at Arlington, USA

Guan, Shan, Eastman Kodak, USA

Guillet, Bruno, University of Caen, France

Guo, Zhen, New Jersey Institute of Technology, USA

Gupta, Narendra Kumar, Napier University, UK

Hadjiloucas, Sillas, The University of Reading, UK

Haider, Mohammad R., Sonoma State University, USA

Hashsham, Syed, Michigan State University, USA

Hasni, Abdelhafid, Bechar University, Algeria

Hernandez, Alvaro, University of Alcala, Spain

Hernandez, Wilmar, Universidad Politecnica de Madrid, Spain

Homentcovschi, Dorel, SUNY Binghamton, USA

Horstman, Tom, U.S. Automation Group, LLC, USA

Hsiai, Tzung (John), University of Southern California, USA

Huang, Jeng-Sheng, Chung Yuan Christian University, Taiwan

Huang, Star, National Tsing Hua University, Taiwan

Huang, Wei, PSG Design Center, USA

Hui, David, University of New Orleans, USA

Jaffrezic-Renault, Nicole, Ecole Centrale de Lyon, France

Jaime Calvo-Galleg, Jaime, Universidad de Salamanca, Spain

James, Daniel, Griffith University, Australia

Janting, Jakob, DELTA Danish Electronics, Denmark

Jiang, Liudi, University of Southampton, UK

Jiang, Wei, University of Virginia, USA

Jiao, Zheng, Shanghai University, China

John, Joachim, IMEC, Belgium

Kalach, Andrew, Voronezh Institute of Ministry of Interior, Russia

Kang, Moonho, Sunmoon University, Korea South

Kaniusas, Eugenijus, Vienna University of Technology, Austria

Katake, Anup, Texas A&M University, USA

Kausel, Wilfried, University of Music, Vienna, Austria

Kavasoglu, Nese, Mugla University, Turkey

Ke,Cathy, Tyndall National Institute, Ireland

Khan,Asif, Aligarh Muslim University, Aligarh, India

Sapozhnikova, Ksenia, D.I.Mendeleyev Institute for Metrology, Russia

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Ko,Sang Choon, Electronics. and Telecom. Research Inst., Korea South

Kockar, Hakan, Balikesir University, Turkey

Kotulska, Malgorzata, Wroclaw University of Technology, Poland

Kratz, Henrik, Uppsala University, Sweden

Kumar, Arun, University of South Florida, USA

Kumar, Subodh, National Physical Laboratory, India

Kung, Chih-Hsien, Chang-Jung Christian University, Taiwan

Lacnjevac, Caslav, University of Belgrade, Serbia

Lay-Ekuakille, Aime, University of Lecce, Italy

Lee, Jang Myung, Pusan National University, Korea South

Lee, Jun Su, AmkorTechnology, Inc. South Korea

Lei, Hua, National Starch and Chemical Company, USA

Li, Genxi, Nanjing University, ChinaLi, Hui, Shanghai Jiaotong University, China

Li, Xian-Fang, Central South University, China

Liang, Yuanchang, University of Washington, USA

Liawruangrath, Saisunee, Chiang Mai University, Thailand

Liew, Kim Meow, City University of Hong Kong, Hong Kong

Lin, Hermann, National Kaohsiung University, Taiwan

Lin, Paul, Cleveland State University, USA

Linderholm, Pontus, EPFL - Microsystems Laboratory, Switzerland

Liu, Aihua, University of Oklahoma, USA

Liu Changgeng, Louisiana State University, USA

Liu, Cheng-Hsien, National Tsing Hua University, Taiwan

Liu, Songqin, Southeast University, China

Lodeiro, Carlos, University of Vigo, Spain

Lorenzo, Maria Encarnacio, Universidad Autonoma de Madrid, Spain

Lukaszewicz, Jerzy Pawel, Nicholas Copernicus University, Poland

Ma, Zhanfang, Northeast Normal University, ChinaMajstorovic, Vidosav, University of Belgrade, Serbia

Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados,

Mexico

Matay, Ladislav, Slovak Academy of Sciences, Slovakia

Mathur, Prafull, National Physical Laboratory, India

Maurya, D.K., Institute of Materials Research and Engineering, Singapore

Mekid, Samir, University of Manchester, UK

Melnyk, Ivan, Photon Control Inc., Canada

Mendes, Paulo, University of Minho, Portugal

Mennell, Julie, Northumbria University, UK

Mi, Bin, Boston Scientific Corporation, USA

Minas, Graca, University of Minho, Portugal

Moghavvemi, Mahmoud, University of Malaya, Malaysia

Mohammadi, Mohammad-Reza, University of Cambridge, UK

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MexicoMoradi, Majid, University of Kerman, Iran

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Mulla, Imtiaz Sirajuddin, National Chemical Laboratory, Pune, India

Neelamegam, Periasamy, Sastra Deemed University, India

Neshkova, Milka, Bulgarian Academy of Sciences, Bulgaria

Oberhammer, Joachim, Royal Institute of Technology, Sweden

Ould Lahoucine, Cherif, University of Guelma, Algeria

Pamidighanta, Sayanu, Bharat Electronics Limited (BEL), India

Pan, Jisheng, Institute of Materials Research & Engineering, Singapore

Park, Joon-Shik, Korea Electronics Technology Institute, Korea South

Penza, Michele, ENEA C.R., Italy

Pereira, Jose Miguel, Instituto Politecnico de Setebal, Portugal

Petsev, Dimiter, University of New Mexico, USA

Pogacnik, Lea, University of Ljubljana, Slovenia

Post, Michael, National Research Council, CanadaPrance, Robert, University of Sussex, UK

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Prateepasen, Asa, Kingmoungut's University of Technology, Thailand

Pullini, Daniele, Centro Ricerche FIAT, Italy

Pumera, Martin, National Institute for Materials Science, Japan

Radhakrishnan, S. National Chemical Laboratory, Pune, India

Rajanna, K., Indian Institute of Science, India

Ramadan, Qasem, Institute of Microelectronics, Singapore

Rao, Basuthkar, Tata Inst. of Fundamental Research, India

Raoof, Kosai, Joseph Fourier University of Grenoble, France

Reig, Candid, University of Valencia, Spain

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Rothberg, Steve, Loughborough University, UK

Sadana, Ajit, University of Mississippi, USA

Sadeghian Marnani, Hamed, TU Delft, The Netherlands

Sandacci, Serghei, Sensor Technology Ltd., UK

Schneider, John K., Ultra-Scan Corporation, USA

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Seifter, Achim, Los Alamos National Laboratory, USA

Sengupta, Deepak, Advance Bio-Photonics, India

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Shin, Kyuho, Samsung Advanced Institute of Technology, Korea

Shmaliy, Yuriy, Kharkiv National Univ. of Radio Electronics, Ukraine

Silva Girao, Pedro, Technical University of Lisbon, Portugal

Singh, V. R., National Physical Laboratory, India

Slomovitz, Daniel, UTE, Uruguay

Smith, Martin, Open University, UK

Soleymanpour, Ahmad, Damghan Basic Science University, Iran

Somani, Prakash R., Centre for Materials for Electronics Technol., IndiaSrinivas, Talabattula, Indian Institute of Science, Bangalore, India

Srivastava, Arvind K., Northwestern University, USA

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Suri, C. Raman, Institute of Microbial Technology, India

Sysoev, Victor, Saratov State Technical University, Russia

Szewczyk, Roman, Industrial Research Inst. for Automation and

Measurement, Poland

Tan, Ooi Kiang, Nanyang Technological University, Singapore,

Tang, Dianping, Southwest University, China

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Thumbavanam Pad, Kartik, Carnegie Mellon University, USA

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Tsigara, Anna, National Hellenic Research Foundation, Greece

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Vaseashta, Ashok, Marshall University, USA

Vazquez, Carmen, Carlos III University in Madrid, Spain

Vieira, Manuela, Instituto Superior de Engenharia de Lisboa, Portugal

Vigna, Benedetto, STMicroelectronics, Italy

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Wandelt, Barbara, Technical University of Lodz, Poland

Wang, Jiangping, Xi'an Shiyou University, China

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Wang, Liang, Pacific Northwest National Laboratory, USAWang, Mi, University of Leeds, UK

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Yang, Wuqiang, The University of Manchester, UKYang, Xiaoling, University of Georgia, Athens, GA, USA

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Zhang, Xueji, World Precision Instruments, Inc., USA

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Zourob, Mohammed, University of Cambridge, UKSensors & Transducers Journal (ISSN 1726-5479) is a peer review international journal published monthly online by International Frequency Sensor Association (IFSA).

Available in electronic and on CD. Copyright 2009 by International Frequency Sensor Association. All rights reserved.


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SSeennssoorrss && TTrraannssdduucceerrss JJoouurrnnaall

CCoonntteennttss

Volume 114Issue 3March 2010

www.sensorsportal.com ISSN 1726-5479

Editorial

Sensors: Smart vs. IntelligentSergey Y. Yurish I

Research Articles

Novel Sensors for Food InspectionsMohd. Syaifudin Bin Abdul Rahman, Subhas C. Mukhopadhyay and Pak Lam Yu .......................... 1

A Neural Network Approach to Fluid Level Measurement in Dynamic Environments Usinga Single Capacitive SensorEdin Terzic, Romesh Nagarajah, Muhammad Alamgir...................................................................... 41Novel Orthogonal Signal Based Decomposition of Digital Signals: Applicationto Sensor FusionAbdul Faheem Mohed, Garimella Rama Murthy and Ram Bilas Pachori .......................................... 56

A Multiobjective Fuzzy Inference System based Deployment Strategyfor a Distributed Mobile Sensor NetworkAmol P. Bhondekar, Gagan Jindal, T. Ramakrishna Reddy, C. Ghanshyam, Ashavani Kumar,Pawan Kapur and M. L. Singla ........................................................................................................... 66

A Low Cost and High Speed Electrical Capacitance Tomography System DesignRuzairi Abdul Rahim, Zhen Cong Tee, Mohd Hafiz Fazalul Rahiman, Jayasuman Pusppanathan. .. 83

Fiber Optic Long Period Grating Based Sensor for Coconut Oil Adulteration DetectionT. M. Libish, J. Linesh, P. Biswas, S. Bandyopadhyay, K. Dasgupta and P. Radhakrishnan ........... 102

Type Identification of Unknown Thermocouple Using Principle Component AnalysisPalash Kundu and Gautam Sarkar..................................................................................................... 112

A Dynamic Micro Force Sensing Probe Based on PVDFQiangxian Huang, Kang Ni, Nan Shi, Maosheng Hou, Xiaolong Wang............................................. 122

LED-Based Colour Sensing SystemIbrahim Al-Bahadly and Rashid Berndt .............................................................................................. 132

Design and Development of Black Box for Analyzing Accidents in Indian RailwaysAlka Dubey and Ashish Verma........................................................................................................... 151

Use of the Maximum Torque Sensor to Reduce the Starting Current in the Induction MotorMuchlas and Hariyadi Soetedjo.......................................................................................................... 161

Implementation of FPGA based PID Controller for DC Motor Speed Control SystemSavita Sonoli, Nagabhushan Raju Konduru....................................................................................... 170


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ZigBee Radio with External Low-Noise AmplifierAllan Huynh, Jingcheng Zhang, Qin-Zhong Ye and Shaofang Gong ................................................ 184

Authors are encouraged to submit article in MS Word (doc) and Acrobat (pdf) formats by e-mail: [email protected]

Please visit journals webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm

International Frequency Sensor Association (IFSA).


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Sensors & Transducers Journal, Vol. 114, Issue 3, March 2010,pp. 66-82

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SSSeeennnsssooorrrsss &&& TTTrrraaannnsssddduuuccceeerrrsssISSN 1726-5479

2010 by IFSA

http://www.sensorsportal.com

A Multiobjective Fuzzy Inference System based Deployment

Strategy for a Distributed Mobile Sensor Network

1Amol P. Bhondekar,

1Gagan Jindal,

1T. Ramakrishna Reddy,

1C. Ghanshyam,

2Ashavani Kumar,

1Pawan Kapur and

1M. L. Singla

1Central Scientific Instruments Organisation (CSIR),

Sector 30C, Chandigarh 160030, India2

National Institute of Technology Kurukshetra,

Kurukshetra, Haryana-136119, India

E-mail: [email protected] [email protected]

Received: 5 November 2009 /Accepted: 22 March 2010 /Published: 29 March 2010

Abstract: Sensor deployment scheme highly governs the effectiveness of distributed wireless sensor

network. Issues such as energy conservation and clustering make the deployment problem much more

complex. A multiobjective Fuzzy Inference System based strategy for mobile sensor deployment is

presented in this paper. This strategy gives a synergistic combination of energy capacity, clustering

and peer-to-peer deployment. Performance of our strategy is evaluated in terms of coverage,

uniformity, speed and clustering. Our algorithm is compared against a modified distributed self-

spreading algorithm to exhibit better performance. Copyright 2010 IFSA.

Keywords: Wireless sensor networks, Deployment, Clustering, Fuzzy inference system

1. Introduction

Advancements in technologies such as sensing, Electronics and computing have attracted tremendous

research interest in the field of Wireless Sensor Networks (WSN), apart from their enormous potential

for both commercial and military applications. A WSN generally consists of a large number of low-

cost, low-power, multifunctional, energy constrained sensor nodes with limited computational and

communication capabilities [1]. In WSNs sensors may be deployed either randomly or

deterministically depending upon the application [2]. Deployment in a battlefield or hazardous areas isgenerally random, where as a deterministic deployment is preferred in amicable environments.


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In general there are certain issues which are needed to be taken care of while senor network

deployment. Network lifetime is one of the important issues to optimize as energy resources in a WSN

are limited due to operation on battery. Replacing or recharging of battery in the network may not be

feasible. Though the overall function of the network may not be hampered due to failure of one or

fewer sensor nodes of the network as neighbouring sensor nodes may take over, but for optimum

performance the network density must be high enough. Network Connectivity, which depends upon

the communication protocol, is another WSN design issue. Generally cluster based architecture isfollowed as the most common protocol. In cluster-based architecture, the sensor nodes are grouped in

clusters which communicate with a sink node (master node); the sink node gathers information from

the nodes in its cluster (slave nodes) and transmits the information to the base station. Network

connectivity issues include the number of sensor nodes in a cluster depending upon the load handling

capability of the sink nodes, as well as the ability of sensor nodes to reach these sinks. Apart from the

design issues discussed above parameters depend upon the application for which the network is to be

deployed. The problem becomes much more complex when the sensor nodes are mobile and are

randomly deployed.

Several strategies have been reported for mobile sensor network deployment by various researchers.

Strategies based on Virtual forces concept was presented in [3]. Wherein two kinds of virtual forcesact upon the sensor nodes, a repellent force which repels the nodes to improve coverage and an

attractive force for maintaining the connectivity between sensor nodes. A distributed self spreading

Algorithm (DSSA) was proposed by Heo and Varshney [4]. Strategies based on voronoi diagram,

constrained multivariable nonlinear programming has also been reported by P. Cheng, C. Chuah and

X. Liu [5] respectively. Shu et al [6] applied Fuzzy logic systems to handle uncertainties in the random

movement and unpredictable oscillations in sensor node deployment. However issues related to

clustering and power management were not handled by Shu et al.

In this paper, we apply fuzzy inference system (FIS) to handle issues related to clustering and power

management along with the uncertainties in distributed sensor node deployment. We have applied FIS

to redeploy the sensor nodes after initial random deployment.

Each individual mobile sensor node uses FIS not only to self adjust its location but also its operational

mode. The sensor nodes are capable of assuming two modes i.e. master mode and slave mode.

Neighbouring sensor nodes location is the only information required by an individual sensor node to

make the movement decision. Whereas, operational mode decision depends upon the current battery

capacity and the mode information of neighbours. The operational mode decision eventually leads to

clustering of the sensor nodes.

We have modified DSSA proposed by Hue and Varshney [4], by introducing rules for operational

mode and battery lifetime. We compare our approach with the modified DSSA in terms of uniformity,coverage, speed and clustering. This comparison proves that our approach outperforms mDSSA.

The rest of the paper is organized in the following way. Section 2 defines our approach to the problem

and the basic functioning of FIS. Minutiae of FIS approach and mDSSA are given in Section 3.

Section 4 and 5 discuss the simulation results and performance respectively followed by conclusion in

Section 6.

2. Methodology

We consider the deployment problem for a rectangular region of interest (RoI), without the loss ofgenerality. Our aim is to find the positions and movements of sensor nodes to achieve maximum

coverage and to distribute the sensor nodes uniformally in minimum time while consuming minimum


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energy. In the initial condition, a given number of sensor nodes are randomly deployed such as by air-

dropping. Because of the randomness in initial deployment, it is very likely that the RoI will not be

fully covered. Part of the RoI might be over crowded with the sensor nodes. Such unbalanced deploy-

ment brings difficulty in uniform sensing, and increases the interference during communications. It can

be seen in the Fig. 1, that there are lots of uncovered and overcrowded areas. Uncovered area cannot

be perceived, while in the overcrowded area, communication between sensor nodes is corrupted by the

interference from neighbouring sensor nodes.

Fig. 1. Random Deployment of sensor nodes.

Our algorithm then intends to re-deploy these sensor nodes such that maximum field coverage and

high quality communication could be achieved. Each individual sensor node in the network needs to

fine-tune its location such that densely deployed sensor nodes can be evenly spreaded in the field. Four

critical measures are considered in our algorithm:

Determine the next-step move distance for each sensor node Determine the next-step move direction for each sensor node Determine the next-step mode adopted by sensor node Determine the battery value left for the sensor nodeFollowing assumptions are being made in this research:

RoI is denoted by a two-dimensional grid. Sensing and communication is modelled as a circle onthis grid.

Coverage discussed in this paper is grid Coverage. A grid point is covered when at least one sensorcovers this point.

A sensor can detect or sense any event within its sensing radius. Coverage is determined based onsensing radius.

Two sensors within their communication range can communicate with each other. Neighbours of asensor are defined as sensor nodes within its communication range.


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Sensor nodes are mobile and are capable of computing, detection and communication. Sensor node can obtain the knowledge of its location. Sensor nodes are capable of reconfiguring themselves as masters or slaves. Sensor in Master mode can communicate with slaves around it, slaves pass on the information to

master which then pass it on to the base station.

Master battery consumption rate and communication range is higher than that of the slaves. All sensor nodes are peer to peer.

2.1. Fuzzy Inference System Design

Fuzzy Logic is a mathematical tool for dealing with uncertainty. Basically, Fuzzy Logic is a

multivalued logic that allows intermediate values to be defined between conventional evaluations like

true/false, yes/no, high/low, etc. Fig. 2 shows the typical structure of a rule-based type-1 FIS [7-8]. FIS

contains four components namely Fuzzification interface, Rule base, Decision Making unit,

Defuzzification interface. When an input is applied to a FIS, the inference engine computes the output

set corresponding to each rule. The defuzzifier then computes a crisp output from these rules output

set. The rules base may be formed based on experience and or from specific experimental/numerical

observations. These rules can be implemented by means of IF-THEN statements [9] for e.g. IF number

of neighbours of a sensor node are low and average Euclidian distance between sensor nodes and its

neighbours is moderate, THEN move the sensor node nearly. The IF-part of the rule is known as

Antecedentand the THEN-part is known as Consequent.

Fig. 2. Rule-based type1 Fuzzy Logic System.

Fuzzy inference process involves five steps namely: fuzzification, fuzzy operation (AND or OR) over

antecedents, inference from the antecedent to the consequent, aggregation of the consequents across

the rules, and defuzzification. The first step is to take the inputs and determine the degree to which

they belong to each of the appropriate fuzzy sets characterized by membership functions. A

membership function (MF) is a curve that defines how each point in the input space is mapped to a

membership interval between 0 and 1. Though the shapes used to describe the membership functions

have hardly any restrictions, some standard mathematical functions developed over the years are

generally used. The input to the fuzzification process is always a crisp numerical value limited to the

universe of discourse of the input variable and the output is a fuzzy degree of membership in the

qualifying linguistic set. Either a table lookup or a function evaluation is used to find out the fuzzified

values. Next, we know the degree to which each part of the antecedent is satisfied for each rule.


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Fig. 3. Membership Function for Shiftdistance.

The input to the fuzzy operator is two or more membership values from fuzzified input variables. Theoutput is a single truth value. The rules weight must be known beforehand to send it for implication.

Every rule has a weight, which is applied to the number given by the antecedent. The input for the

implication process is a single number given by the antecedent, and the output is a fuzzy set.

Implication is implemented for each rule. Aggregation is the process by which the fuzzy sets that

represent the outputs of each rule are combined into a single fuzzy set. Aggregation only occurs once

for each output variable, just prior to defuzzification. The input for the defuzzification process is a

fuzzy set and the output is a single number. The most popular defuzzification method is the centroid

calculation which returns the center of area under the curve. Applying center-of-sets defuzzification

[10], for every input (x1, x2), the output is computed using:

1 2

1 2

1

1

( 1) ( 2 )

( 1, 2 )

( 1) ( 2 )

ml l l

G F F

l

ml l

F F

l

c x x

y x x

x x

=

=

=

,

(1)

where m is the no. of rules.

In this work Fuzzy Logic toolbox of MATLAB

is used for deriving the fuzzy decision surfaces. Two

decision surfaces were derived for Shift Distance and Next Mode decisions. A MATLAB

script was

coded to simulate the sensor deployment using these decision surfaces. The shift distance is calculatedusing the FIS made by using two antecedents and a set of rules as listed in Table 1.

Antecedent 1- Number of neighbours of each sensor.

Antecedent 2- Average Euclidean distance between sensor node and its neighbours.

Here moderate, nearandfarare the values taken from the membership function of respective variable.

Coloumbs law becomes a handy tool for determination of next step move direction [6]. Wherein the

sensor nodes act as similarly charged particles which repel each other. Thus, the direction of movement

for an individual sensor node can be determined by the vector addition of repulsive forces acting on it

from its neighbouring nodes.


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Table 1. Rules for Shift Distance Decision.

Antecedent 1 Antecedent 2 Antecedent 3

Low Near Moderate

Low Moderate Near

Low Far Near

Medium Near Far

Medium Moderate Moderate

Medium Far Near

High Near Far

High Moderate Moderate

High Far Moderate

The next step is to decide the next operational mode that the sensor node would be working in. Initially

all sensor nodes are randomly assigned their modes.

Fig. 4. Membership Function for Selecting mode.

For this FIS we have used three antecedents as inputs and one output, as listed in Table 2.

Antecedent 1:- Battery- Current battery value.

Antecedent 2:- Slaves- Number of slaves the sensor node has in its neighbourhood.

Antecedent 3:- Master- Number of masters or sensors working in master mode in its neighbourhood.

After getting next mode, the next step is to calculate the battery value. Battery value is calculated as

the result of a mathematical function Equation (2) consisting of shift distance, mode and the time

sensor node has been operational.

10 (log(1 ) log(1 ) log(1 ))b t d m= + + + + +,

(2)

where b is the remaining battery value;

t is the time elapsed;

d is the total distance transversed;m is the current operational mode.


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Thus after calculating the next step move information, operational mode and battery value, the sensor

node coordinates and operational mode are updated.

Table 2. Rules for Next mode Decision.

Antecedent 1 Antecedent 2 Antecedent 3 Next mode

Very low None None Hibernate

Low Low Low Slave

Low Low Medium Master

Low Low High Slave

Low Medium Low Master

Low Medium Medium Slave

Low Medium High Slave

Low High Low Master

Low High Medium Master

Low High High Slave

Medium Low Low Master

Medium Low Medium Slave

Medium Low High Slave

Medium Medium Low Master

Medium Medium Medium Slave

Medium Medium High Slave

Medium High Low Master

Medium High Medium Master

Medium High High Slave

High Low Low Master

High Low Medium Slave

High Low High SlaveHigh Medium Low Master

High Medium Medium Master

High Medium High Slave

High High Low Master

High High Medium Master

High High High Slave

3. Algorithms

This Section Explains the Algorithms we have used for both mDSSA and Fuzzy Logic codes.

3.1. Modified Self Spreading Algorithm

DSSA reported by Heo and Varshney [4] was modified, henceforth to be referred as mDSSA, to

accommodate remaining battery strength and time to determine next step operational mode. We have

implemented the algorithm in MATLAB

. Performance of this mDSSA is compared against the

proposed FIS base deployment. We investigate various number of sensors deployed in a field of

80X60sq unit area. The main idea of DSSA is to define a partial force for the movement of sensor

nodes during the deployment process. The force a sensor node receives from a closer neighbour node

[4] is greater than that from a farther neighbour. For N sensor nodes deployed in a square field with

area A, DSSA formulates the partial force sensor node i receives from neighbour node j as


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,

2( | |

| |

i j ii j i jn n n

n n n j i

n n

D p pf cR p p

p p

=

(3)

where cR stands for communication range,2

. .( )

N cRcR

A

= is called the expected density

D is the local density, andi

np stands for the location of node i at time step n.

Each node makes decision to move by adding up all partial forces from its neighbouring nodes. DSSA

sets up two criteria: stable status limit (Slim) and oscillation limit (Olim) to stop a sensors movement.

Various Steps in the algorithm used can be explained as:

3.1.1. Initialization

Initially nodes are deployed randomly on the field. Then we calculate expected density that gives us anidea of the desired density. Expected density is average number of nodes to cover the entire area if

uniformally deployed. Based upon the current battery strength, neighbourhood information and

operational mode the next step operational mode is decided and battery strength is also updated based

on Equation (2).

3.1.2. Partial Force Calculation

Here the nodes are treated as particles in physics following Coloumbs law. Force here depends on the

distance between nodes and also on the current local density. Force corresponding to higher density is

higher.

3.1.3. Oscillation Check

mDSSA deploys two stopping criterias. A node is considered to be in oscillation state if it is moving

back and forth between two points. This state is determined and the oscillation count is maintained, if

it exceeds oscillation limit then the node is stopped at the centre of the two oscillation points.

3.1.4. Stability Check

A node is considered to have achieved stability if it moves less than some threshold value in a fix time

called Stability_limit. This is the second stopping criterian employed in mDSSA to stop nodes

movement.

Algorithm 1 pseudo code for modified distributed self spreading algorithm

1. Initialization

Initial_node_locations; sensing_range sR; communication_range cR; clock; battery value; mode;

calculate_expected_density

For (no_of_iterations)


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For (no_of_sensors)

Calculate shift_distance moved by each sensor

Time to complete one iteration

Calculate the battery (depend on shift_distance,time and mode)

While (Not (Oscillation occurred OR In a region of stable))

2. Partial Force Calculation

Calculate partial_force,

( , , , )i j

f D cR pn n

Update temporary_position

3. Oscillation

If( | |1 1

i ip p

n n

+ < threshold1)

Increase oscillation_count by 1;

If(oscillation_count < oscillation_limit)

Update next location to the temporary_position;

Update local_density D;

Else

Move to the centroid of oscillating points;

Update local_density;

Stop node is movement;

Else

Update next location to the temporary_position;

Update local_density D;

4. Stable

If( | |1

i ip p

n n

+ < threshold2)

Increase stability_count by 1;

If(stability_count < stability_limit)

Go to while loop;

Else

Stop node is movement;

Else

Go to while loop

End

Increase sensor_count by 1;

End

Increase no_of_iteration by 1;

End


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3.2 Fuzzy Logic Algorithm

After initial random deployment the record of each nodes neighbourhood information and average

distance is maintained, which serves as input to the fuzzy calculation of Shift distance. Number of

masters and Salves are also calculated, which are further used for calculating the next operational

mode using FIS.

Algorithm 2 pseudo algorithm for the Fuzzy Logic algorithm

1. Initialization

Initial_node_locations; range_of_sensors; clock; battery value; mode;

Readfis of shift_distance and next_mode; distributed=1;

While (no_of_iterations)

While (distributed=1)

Increment no_of_iterations by 1;

Distributed=0;

Move_direction=0;

For (no_of_sensors)

Average_euclidean_distance=0;

No_of_neighbours=0;

For (no_of_sensors)

If(|Xj-Xi| < range_of_sensors)

Increment no_of_neighbours by 1;

Average_euclidean_distance=average_distance+mag (Xj-Xi);

If(mode =slave)

Increment slave count by 1;

Else If (mode =master)

Increment master count by 1;

End

Do_not_move=1;

Distributed=1;

End

End

If(mode =master)

For (no_of_sensors)

If(|Xk-Xi| < 3*range_of_sensors)

If(mode > master)

Move master according to coulombs lawElse


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Increase slave_count by 1

End

End

End

If(do_not_move==1)

Evaluate fuzzy for shift_distance & next_mode

Calculate the time for each iteration;

Calculate the battery value (depend on shift_distance, time, mode);

End

If(battery < slave)

Go to hibernate;

End

End

End

4. Simulation and Results

Sensors are initially randomly deployed in the given ROI. Then the algorithm is run and final

deployment is shown in above Fig. 5. It can be seen that the network distribution is uniform and the

masters (Red dots) are surrounded by the slaves (Blue dots). All the slaves are in the communication

range of relevant masters and the masters are in communication range (Red circles) with each other

enabling multihop communications. Surface plots are three-dimensional curve that represents themapping between two inputs and an output.

Fig. 5. Final Deployment After Running Fuzzy Algorithm.


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If there are more than two inputs the surface plot is generated keeping one input as constant. Fig. 6

shows the decision surface generated by the FIS for Shift Distance calculation.

Fig. 6. Decision Surface for Shift distance.

We have three input antecedents for calculating the next operational mode so the following curves are

drawn keeping one of them constant Fig. 7 shows the decision surface between Next Mode, Master

and Battery; here number of slaves is kept as a constant.

Fig. 7. Decision Surface for Next mode, Master and Battery.


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Fig. 8 shows the decision surface generated by the FIS between the Next Mode, Battery and Slaves;

here number of masters is kept as constant.

Fig. 8. Decision Surface for Next mode, Slaves and Battery.

5. Performance and Discussion

The section compares the performance of our fuzzy logic with the modified distributed self spreading

algorithm. Various parameters are deployed to compare the two algorithms.

5.1. Coverage

Coverage [11] accounts for the quality of service of a wireless sensor network. The concept of

coverage as an archetype for the system level functionality of multi-robot systems was introduced by

Gage [12]. In this paper, coverage is defined by the ratio of the union of covered areas of each node

and the complete area of interest. Here the covered area of each node is defined as the area within

sensing radius Rs. It is assumed that it will detect every event happened in this range perfectally. We

have used around 20000 Monte Carlo simulations to calculate Coverage. Monte Carlo method [11]requires many sample points to be evaluated and based on the evaluation the results are calculated.

As we can see Fuzzy algorithm has much higher Coverage ratio for the same number of iterations as

compared to mDSSA and it is almost touching 99 % for thirty five iterations.

In Fig. 10 as we increase the number of nodes the coverage ratio increases for both mDSSA and Fuzzy

but coverage is initially better in case of Fuzzy deployment and increases at greater rate with

increasing number of nodes than mDSSA, which saturates as number of sensors is increased to 300.

Whereas in Fuzzy deployment the coverage ratio is approaching 100% mark with increasing number

of nodes.


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Fig. 9. Coverage vs. No of Iterations curve for Fuzzy and mDSSA.

Fig. 10. Coverage Ratio vs. No. of Nodes curve for 60 iterations each for Fuzzy and mDSSA.

5.2. Uniformity

Uniformity (U) [13] can be defined as the average local standard deviation of the distances between

nodes.

1

1 N

i

i

U UN =

= (4)

12 2

,

1

1( ( ) )

iK

i i j j

ji

U D MK =

= (5)

where N is the total number of nodes;

Ki is the number of neighbours of the ith

node;Di,j is the distance between i

thand j

th nodes;

Mi is the mean of internodal distances between ith sensornode and its neighbours.


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While calculating local uniformity Ui, at the ith node, only the neighboring sensor nodes that are

present in its communication range Rc are considered. A smaller value ofUsymbolizes that nodes are

more uniformly distributed in the ROI. Network density doesnt have much role to play here while

calculating Uniformity. In Fig. 11 as we can see with number of iterations approaching 60, uniformity

is almost zero in case of Fuzzy while it still hovering around 50 in mDSSA.

Fig. 11.Uniformity vs. No. of iterations.

5.3. Speed

Speed in the deployment of sensor nodes plays an important role in various critical applications. The

required time depends on the complexity of the reasoning and optimization algorithm and physical

time for the movement of sensor nodes. The total time elapsed is defined here as the time elapsed until

all the nodes reach their final locations.

Here we have noted time after every iteration and we can see that the total time in case of mDSSA

amounts to 300 sec compared to 8-9 sec in case of FUZZY deployment. This shows that fuzzy

algorithm works much faster as compared to mDSSA. Lower time value means increased battery life

and increase in lifetime of the complete sensor network.

5.4. Mode

The graph shows as the time progresses the number of masters sharply decreases and number of slaves

increases in case of mDSSA and the final value approaches 70 in case of masters and 225 in case of

slaves thus the slave master ratio is poor and unbalanced while in Fuzzy it maintains a healthy ratio

throughout.

It also shows that power management is better in case of Fuzzy deployment as in the end there are

more number of masters present with higher battery values. Similarly lesser number of slaves for fuzzy

system strengthens our point that fuzzy deployment involves much less energy consumption ascompared to mDSSA.


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Fig. 12.Total time vs. No. of iterations.

Fig. 13.No. of Masters vs. No. of iterations.

Fig. 14.No. of Slaves vs. No. of iterations.


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6. Conclusions

A sensor deployment strategy based on FIS has been proposed in this paper. The technique handles the

complexity and uncertainties involved in the wireless sensor deployment in much better way than any

existing algorithm. In energy constrained WSN the battery and network life are reciprocative and a fast

and efficient strategy is indispensable. FIS algorithm proposed here has proved itself on theseparameters prolonging network lifetime and achieving efficient deployment in much lesser time with

least amount of power consumption. The proposed strategy not only demonstrates the usefulness for

deployment of energy constrained WSNs but also efficient clustering of sensor nodes.

References

[1]. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless Sensor Networks: a Survey, Computer

Networks, 38, 2002, pp. 393-422.

[2]. M. Ishizuka, M. Aida, Performance Study of Node Placement in Sensor Networks, in Proc. of 24th

International Conference on Distributed Computing Systems Workshops, 2004, pp. 598-603.[3]. Y. Zhou and K. Chakrabarty, Sensor Deployment and Target Localization Based on Virtual Forces,IEEE

22nd Annual Joint Conference of the Computer and Communications Societies, Vol. 2, 2003, pp. 1293-

1303.

[4]. N. Heo and P. K. Varshney, Distributed Self Spreading Algorithm for Mobile Wireless Sensor Networks,

IEEE International Conference on Wireless Communications and Networking, Vol. 3, New Orleans, LA,

2003, pp. 1597-1602.

[5]. P. Cheng and C. Chuah and X. Liu, Energy Aware Node Replacements in Wireless Sensor Networks,IEEE

Global Telecommunication Conference, Vol. 5, 2004, pp. 3210-3214.

[6]. Haining Shu, Quilian Liang and Jeon Gao, Distributed Sensor Networks Deployment Using Fuzzyu Logic

Systems,International Journal of Wireless Information Networks, Vol. 14, No. 3, September 2007.

[7]. J. M. Mendel, Fuzzy Logic Systems for Engineering, Proceedings of IEEE, Vol. 83, No. 3, 1995,

pp. 345-377.[8]. http://www.scielo.br/img/revistas/ca/v14n4/a05fig01.gif

[9]. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems, Prentice-Hall, Upper Saddle River, NJ, 2001.

[10]. S. Poduri, and G. S. Sukhatme, Constrained Coverage for Mobile Sensor Networks, IEEE International

Conference on Robotics and Automation, Vol. 1, 2004, pp. 165171.

[11]. Seok Myun Kwon and Jin Suk Kim, Coverage Ratio in the Wireless Sensor Networks Using Monte Carlo

Simullation,IEEE, 2008.

[12]. Gage, D. W., Command Control for Many-Robot Systems, Unmanned Systems Magazine, Vol. 10, No. 4,

1992, pp. 28-34.

[13]. Nojeong Heo and Pramod K. Varshney, An Intelligent Deployment and Clustering Algorithm for a

Distributed Mobile Sensor Network,IEEE, 2003.

___________________

2010 Copyright , International Frequency Sensor Association (IFSA). All rights reserved.

(http://www.sensorsportal.com)


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