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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:editor@sensorsportal.com
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
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Editor for AsiaOhyama, Shinji, Tokyo Institute of Technology, Japan
Editor for Asia-Pacific
Mukhopadhyay, Subhas, Massey University, New Zealand
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Annamalai, Karthigeyan, National Institute of Advanced Industrial Science
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Chiang, Jeffrey (Cheng-Ta), Industrial Technol. Research Institute, Taiwan
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Dickert, Franz L., Vienna University, Austria
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Ding, Jianning,Jiangsu Polytechnic University, ChinaKim,Min Young, Kyungpook National University, Korea South
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Drljaca, Predrag, Instersema Sensoric SA, SwitzerlandDubey, Venketesh, Bournemouth University, UK
Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, Germany
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Erkmen, Aydan M., Middle East Technical University, Turkey
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Fu, Weiling, South-Western Hospital, Chongqing, China
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Gole, James, Georgia Institute of Technology, USAGong, Hao, National University of Singapore, Singapore
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Ko,Sang Choon, Electronics. and Telecom. Research Inst., Korea South
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Kumar, Subodh, National Physical Laboratory, India
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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
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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
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Lorenzo, Maria Encarnacio, Universidad Autonoma de Madrid, Spain
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Marquez, Alfredo, Centro de Investigacion en Materiales Avanzados,
Mexico
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Maurya, D.K., Institute of Materials Research and Engineering, Singapore
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Radhakrishnan, S. National Chemical Laboratory, Pune, India
Rajanna, K., Indian Institute of Science, India
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Somani, Prakash R., Centre for Materials for Electronics Technol., IndiaSrinivas, Talabattula, Indian Institute of Science, Bangalore, India
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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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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
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Please visit journals webpage with preparation instructions: http://www.sensorsportal.com/HTML/DIGEST/Submition.htm
International Frequency Sensor Association (IFSA).
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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: amolbhondekar@csio.res.in ashavanikumar@yahoo.co.in
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
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[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-
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[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,
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[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,
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[13]. Nojeong Heo and Pramod K. Varshney, An Intelligent Deployment and Clustering Algorithm for a
Distributed Mobile Sensor Network,IEEE, 2003.
___________________
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