Wireless Sensor Network using Particle Swarm Optimization

Short Paper
Proc. of Int. Conf. on Advances in Control System and Electricals Engineering 2013

Wireless Sensor Network using Particle Swarm
Optimization
Shekh Md Mahmudul Islam1, Mohammad Anisur Rahman Reza and Md Adnan Kiber
Department of Applied Physics, Electronics & Communication Engineering,
University of Dhaka, Dhaka, Banngladesh-1000
Email: mahmud_apececadet@yahoo.com
sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and
communications bandwidth. The topology of the WSNs can
vary from a simple star network to an advanced multi-hoop
wireless mesh network. The propagation technique between
the hops of the network can be routing or flooding. Today
such networks are used in many industrial and consumer applications, such as industrial process monitoring and control,
machine health monitoring, and so on.WSN consists of four
main components: a radio, a processor, sensor and battery. In
most deployments sensor nodes have self-organizing capabilities to form an appropriate structure. A WSN monitors an
environment by sensing its physical properties .It is a network of tiny, inexpensive autonomous nodes that can acquire
process and transmit sensory data over wireless medium. One
or more powerful base station serves as the final destination
of the data. The properties of WSNs that pose technical challenge include dense ad-hoc deployment, dynamic topology.
WSN issues such as node deployment, localization, energyaware clustering and data aggregation are often formulated
as optimization problems. Traditional analytical optimization
technique requires enormous computational effort, which
grows exponentially as the problem size increases. An optimization method that requires moderate memory and computational resources and yet produces good results is desirable,
especially for implementation on an individual sensor node.
Bio-inspired optimization methods are computationally efficient alternatives to analytical methods. Particle swarm optimization is a popular multi-dimensional optimization technique.
Ease of implementation, high quality of solutions, computational efficiency and speed of convergence are strength of
PSO. This paper focuses on sensor deployment using Particle Swarm Optimization (PSO) algorithm. One of the fundamental issues that arise in WSN is coverage area in addition
to location identification, tracking and deployment. In this
coverage the nodes have the effective responsibility to cover
the predefined area. The most effective approach for sensor
deployment is to place sensor in such a manner that the maximal network coverage is achieved.

Abstract— Wireless sensor network (WSN) is becoming
progressively important and challenging research area. A
Wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical and
environmental conditions and to co-operatively pass their data
through the network to a main location. Wireless sensor
consists of small low cost sensor nodes, having a limited
transmission range and their processing, storage capabilities
and energy resources are limited. The main task of such a
network is to gather information from a node and transmit it
to a base station for further processing.WSN has different
issues such as optimal sensor deployment, node localization,
base station placement, location of target nodes, energy aware
clustering and data aggregation. Recently researchers around
the world are applying bio-inspired optimization algorithm
known as particle swarm optimization (PSO) for increasing
efficiency in the WSN issues. This paper describes the use of
PSO algorithm for optimal sensor deployment in WSN.
Index Terms— Wireless sensor network (WSN), Particle Swarm
Optimization (PSO), base station placement

I. INTRODUCTION
Wireless Sensor Network (WSN) are an emerging technology[1] that has potential application in surveillance, environment and habitat monitoring ,structural monitoring and
healthcare, and disaster management[2]. A wireless sensor
network (WSN) consists of spatially distributed autonomous
sensors to monitor physical or environmental conditions,
such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location.
The more modern networks are bi-directional, also enabling
control of sensor activity. The development of wireless sensor networks was motivated by military applications such as
battlefield surveillance; The WSN is built of “nodes” – from
a few to several hundreds or even thousands, where each
node is connected to one (or sometimes several) sensors.
Each such sensor network node has typically several parts: a
radio transceiver with an internal antenna or connection to
an external antenna, a microcontroller, an electronic circuit
for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A
sensor node might vary in size from that of a shoebox down
to the size of a grain of dust, although functioning “motes”
of genuine microscopic dimensions have yet to be created.
The cost of sensor nodes is similarly variable, ranging from a
few to hundreds of dollars, depending on the complexity of
the individual sensor nodes. Size and cost constraints on
© 2013 ACEEE
DOI: 03.LSCS.2013.1.502

II. PARTICLE SWARM OPTIMIZATION
PSO is a population based optimization technique
developed by Eberhart and Kennedy in 1995[3]. In PSO, the
potential solutions, called particles, fly through the problem
space following the current optimum particles. Each particle
keeps track of its coordinates in the problem space, which
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Short Paper
are associated with the best solution (fitness) it has achieved
so far (the fitness value is also stored). This value is called
pbest. Another best value that is tracked by the particle swarm
optimizer is the best value obtained so far by any particle in
the swarm called gbest. Each individual in PSO flies in the
search space with a velocity which is dynamically adjusted
according to its own flying experience (pbest) and its
companion’s experience (gbest), so that the individuals of
the population can be expected to move towards better
solution areas. Each individual is treated as a volume-less
particle in the D-dimensional search space. The particles are
manipulated according to the following equations [4]

V wV c1rand   pid Xid  c2 R    pgd Xid 
and
id
id

X id  X id  Vid

(1)
(2)

Fig 1: Geometric illustration of particle movement in PSO

where c1 and c2 are positive constants (learning factors),

possible[5].Events in an area devoid of an adequate number
of sensor nodes remain unnoticed; and the areas having
dense sensor population suffer from congestion and delays.
Optimally deployed WSN assures adequate quality of service,
long network life and financial economy. Available PSO
solutions to the deployment problem are computed centrally
on a base station for determining position of sensors.

and rand() and Rand() are two random functions in the range





X i  X i1 , X i 2 , ..., X iD represents the ith particle;

[0,1];

Pi   pi1 , pi 2 ,..., piD 

represents the best previous

position (the position giving the best fitness value) of the
particle; the symbol g represents the index of the best particle among all the particles in the population;



A. Sensor Coverage
A sensor placed on a location point(X1, Y1) can cover a
location point (X2, Y2), if the Euclidian distance between the
two points is:
(X1-X2)2-(Y1-Y2)2<=r2
Where, r is the sensing range of the sensor. The mean
value of the location point (Xi,Yi) for i=1,2,….M is represented
by (mx,my).Sensor node is the centered of location points it
has to cover. The distance between the sensor node and the
farthest location point denote the sensing range r.Area, A is
divided into R regions and each region is placed with sensor
nodes by minimizing Euclidian distance between location
points and their closest centroid. Area A is covered with R
sensor nodes. The coverage problem can be formulated as
an optimization problem and defined as: P is the set points
and R is the fixed no of sensors, the optimum location for
deploying all R sensors such that every location point is
covered.



Vi  Vi1 , Vi 2 , ..., ViD represents the rate of the position
change (velocity) for particle i. Equations (1 and 2) are the
equation describing the flying trajectory of a population of
particles. Equation (1) describes how the velocity is dynamically updated and Equation (2) the position update of the
“flying” particles. Equation (1) consists of three parts. The
first part is the momentum part. The velocity can’t be changed
abruptly. It is changed from the current velocity. The second
part is the “cognitive” part which represents private thinking
of itself - learning from its own flying experience. The third
part is the “social” part which represents the collaboration
among particles - learning from group flying experience. The
commonly used PSOs are either global version or local version. In the global version of PSO, each particle flies through
the search space with a velocity that is dynamically adjusted
according to the particle’s personal best performance
achieved so far and the best performance achieved so far by
all the particles. In the local version of PSO, each particle’s
velocity is adjusted according to its personal best and the
best performance achieved so far within its neighborhood.
The neighborhood of each particle is generally defined as
topologically nearest particles to the particle at each side. A
local version of PSO with time varying inertia coefficient is
used in this work is shown in Fig 1.

III. PROBLEM FORMULATION
The main objective of the present work paper is to minimize
the distance between the neighboring nodes, maximizing
coverage in the network, while simultaneously satisfying all
constraints.
1. All sensor nodes are homogenous and have mobility
2. We assume that deployed sensor nodes can fully cover
the sensing fields Sensing coverage and communication
coverage of each node assumed to have circular shape
without any irregularity.
3. The design variables are two dimensional co-ordinates
of the sensor nodes
4. All the sensor nodes cover equal sensing field areas

II. OPTIMAL WSN DEPLOYMENT
WSN problem refers to determining position for sensor
nodes such that the desired coverage, connectivity and
energy efficiency can be achieved with as few nodes as
© 2013 ACEEE
DOI: 03.LSCS.2013.1.502

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Short Paper
The above are common assumption for many sensor
network applications.
A. Flow chart
The Flow chart contains a recursive iteration loop and
can be described by the following pseudocode.Fitness,F; F
depends on the Euclidian distance between the sensor node
and the nearest centroid. Calculate fitness for each particle.
Among the swarm, the particle with least fitness is considered
as the global best particle as it is closest to the optimum
solutions. The swarm is said to have accomplished the task if
all the particles in it have acquired fitness less than or equal
to the range of sensors incorporated in the network. In particle
swarm optimization we perform the following actions [Fig. 2]
1. Network information and algorithm parameters—inertia,
weight, learning factor, velocity boundary value and the
largest iterative numbers are initialized. Array of particles are
initialized with random position and velocity vector.
2. Find the distance of the interest point to its nearest
sensor. Fitness is evaluated for every particle at its current
position using Euclidian distances.
3. Minimize the fitness value; ideally the fitness value
should be equal to zero, where the distance between the
interests points with their nearest sensors are within the
sensor’s sensing range. If the fitness of the particle is lesser
than that of the best particle, then the particle would be the
best particle for the next move, and fitness of that particle is
taken as best fitness.
4. Each particle is made to modify its current position and
current velocity.
5. If the next position of the particle is the best, then the
particle chooses a new position, otherwise the same algorithm
is continued.
6. The process is repeated in iteration, until all the particles
communicate with each other and generate maximum
coverage.
B. PSO parameters
For the proposed method the number of particles are taken
as 40 and the learning factor C1=C2=2.An inertia weight factor
is linearly reduced as the search proceeds from 0.9 to
0.4[Xiahui et al(2004) ].The maximum velocity and maximum
iterations[Bo Li and RenYue Xiao(2007)] are taken as 50 and
300 respectively.
IV. RESULTS AND DISCUSSIONS
The initial population is created randomly and the
objective function is calculated. The new sequence generation
based on the initial sequence illustrated in the following
example. Consider the following initial sequence Pibest and Pgbest
as follows:
Present: 2 6 3 5 4 1
Pibest: 6 1 2 5 3 4
Pgbest: 5 3 6 4 2 1
Assume C1=C2=1 and rand () =0.57.Then Pibest is generated
by swapping the individuals of a present sequence.
Present: 2 6 3 5 4 1 Swap :( 2 6)
© 2013 ACEEE
DOI: 03.LSCS.2013.1.502

Fig 2: Flow chart of PSO algorithm

6 2 3 5 4 1 Swap :( 2, 1)
6 1 3 5 4 2 Swap :( 3, 2)
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Short Paper
6 1 2 5 4 3 Swap :( 4, 3)
6 1 2 5 3 4 ——Pibest
Here (2,6) (2,1) (3,2) (4,3) are used for getting Pibest from
the present sequence .The Pgbest is generated by swapping
the individual of a present sequence.
Present: 2 6 3 5 4 1 Swap :( 2, 5)
5 6 3 2 4 1 Swap: (6, 3)
5 3 6 2 4 1 Swap: (2, 4)
5 3 6 2 4 1—Pgbest
Hence (2, 5), (6, 3) and (2, 4) are used for getting Pgbest
from present sequence.
Velocity=1*0.57{(2,6),(2,1),(3,2),(4,3)}+1*0.57{(2,5),(6,3),(2,4)}
The 57% of the change in both the parts are considered.
Hence the first two changes in both the parts (2,6),(2,1) and
(2,5),(6,3) is considered.
New sequence=present+ velocity=2 6 3 5 4 1+ (2, 6), (2,
1), (2, 5), (6, 3)
Hence the sequence is generated for the next generation
is 3 1 6 2 4 5.Similarly for all other particles the new sequences
are generated and objective function is evaluated and is
shown in Fig.3 and Fig. 4.

(c)

(d)
Fig 3: PSO’s Particle location of the target (a) Randomly distributed
particles (b) Particles position after 5 0 interactions (c) Pa rticles
position after 100 interactions

(a)

Fig 4: Sensor Vs Coverage

It is proved theoretically that the maximum efficient
coverage [Men-Shen Tsai and Wu-chang Wu (2008)] or the
minimum number of nodes required to cover rectangular area
of 50×50 is 40.
(b)

© 2013 ACEEE
DOI: 03.LSCS.2013.1.502

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Short Paper
V. CONCLUSIONS

REFERENCES

Scale and density of deployment, environmental uncertainties and constraints in energy, memory and bandwidth
and computing resources pose serious challenge to the developer of WSNs. Most analytical methods suffer from slow
or lack of convergence to the final solution. This call for first
optimization algorithm that produces quality solution utilizing less resource.PSO has been popular technique used to
solve optimization problem in WSN due to its simplicity, high
quality of solution, first convergence and insignificant computational burden. The proposed work has the ability to
achieve optimal solution of converge problem with minimum
number of sensors in wireless network. The results show
that PSO approach is effective and robust for efficient coverage problem of sensor deployment and is considered to give
almost the optimal solutions in WSN. In future the focus can
be given to achieve 100 % coverage with minimum number of
sensors. The study of the 100% coverage using various optimal search techniques also presents several interesting
challenges.

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Network Survey”, computer networks, Vol 52,no-12,pp. 2292–
2330,2008
[2] Eberhart R. and Kennedy J. (1995). A new optimizer using
Particle Swarm Theory. Proc 6 th Int. symposium on Micro
machine and Human Science, pp.39-43
[3] Poli, R. 2007. Analysis of the publications on the Applications
of Particle Swarm Optimisation, Journal of Artificial Evolution
and Applications, 2008, 1-10.
[4] Lazinica, A. 2009. Particle Swarm Optimization. (Edited),
Published by In-Tech, Croatia, January
[5] K.V. Price, R.M Storn, and J.A Lampinen, Differential
evolution: A Practical approach to Global optimization, Ser.
Natural Computing series; Berlin, Germany: Springer-Verlag,
2005 .

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DOI: 03.LSCS.2013.1.502

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Wireless Sensor Network using Particle Swarm Optimization

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