Performance evaluation of variants of particle swarm optimization algorithms for the purpose of clustering in wireless sensor networks

An Adaptive k-means based Method for
Energy Efficiency Routing in WSN

Wireless Sensor Network (WSN):
An Introduction
 WSN: A Collection of tiny, inexpensive autonomous but energy
deficient nodes that can acquire, process and transmit
sensory data over wireless medium.
 limited computation capability, small battery size and small
memory storage.
 potential use in surveillance, monitoring and management.
 Various challenges of WSN include node deployment,
management of scarce resources such as bandwidth, memory
and energy in ad-hoc but dynamic topology of nodes.
 Research areas in WSN include Efficient Node Deployment,
Node Energy Management, Data transmission Security,
Clustering of Nodes etc.


ENERGY MANAGEMENT IN
WSN
•Communication is the most energy expensive activity of a node. Energy
required to transmit varies exponentially with transmission distance.
• A solution lies in Multihop Transmission.
•Cluster based hierarchical routing protocol is an energy efficient routing
protocol. (LEACH PROTOCOL)

Multihop Transmission
 The amount of
energy used in
figure (a) can be
modeled by this
formula:
 ampk(3d1 + d2)2
 Whereas the
amount of energy
used in figure (b)
uses this formula:
 ampk(3d1
2
+ d2
2
)

LEACH : An Introduction…
 Low-Energy Adaptive Clustering Hierarchy
(LEACH) is a dynamic clustering method in
which nodes elect themselves as cluster heads
with some probability P.
 The algorithm is run iteratively into rounds and
every node becomes a cluster head at least
once within 1/P rounds.
 LEACH has two phases: setup phase where
clusters are formed and steady state phase
that consists of data communication process

The Problem
 The reason we need network protocol such as
LEACH is due to the fact that a node in the
network is no longer useful when its battery
dies.
 This protocol allows us to space out the
lifespan of the nodes, allowing it to do only the
minimum work it needs to transmit data.

The Cluster-Head
 The LEACH Network is made up of nodes,
some of which are called cluster-heads
 The job of the cluster-head is to collect data from
their surrounding nodes and pass it on to the
base station
 LEACH is dynamic because the job of cluster-
head rotates

WSN Energy MODEL
 This is the formula for the amount of energy
depletion by data transfer:

LEACH’s Two Phases
 The LEACH network has two phases: the set-
up phase and the steady-state
 The Set-Up Phase
 Where cluster-heads are chosen
 The Steady-State
 The cluster-head is maintained
 When data is transmitted between nodes

Stochastic Threshold Algorithm
 Cluster-heads can be chosen stochastically
(randomly based) on this algorithm:
 If n < T(n), then that node becomes a cluster-
head
 The algorithm is designed so that each node
becomes a cluster-head at least once

Deterministic Threshold
Algorithm
 A modified version of this protocol is known as
LEACH-C (or LEACH Centralized)
 This version has a deterministic threshold
algorithm, which takes into account the amount
of energy in the node…

Deterministic Threshold
Algorithm
 …and/or whether or not the node was recently
a cluster-head

What’s the Difference?
 REMEMBER: The goal of these protocol is to
increase the life of the network
 The changes between the LEACH stochastic
algorithm and the LEACH-C deterministic
algorithm alone is proven to increase the FND
(First Node Dies) lifetime by 30% and the HND
(Half Node Dies) lifetime by 20%

An Example of a LEACH
Network
 While neither of
these diagrams is
the optimum
scenario, the
second is better
because the cluster-
heads are spaced
out and the network
is more properly
sectioned

How to increase LEACH
Efficiency?
 Use some metaheuristic proven algorithm for
LEACH clustering.
 Several algorithms used in past such as GA
[Hussain2007] , PSO [Latiff2008] etc.
 Results shows significant improvements in
network life.
 Various variants of PSO used for LEACH
clustering [Kulkarni2010] .

Problem Identification:
 We will be working on two areas, one on
clustering in WSN and another on a clustering
algorithm such as k-means.
 Till now k-means is supplied with number of
cluster .
 We propose to use an adaptive k-means
clustering algorithm.

Research Objectives
Objectives:
 To propose and implement ADAPTIVE K-
MEANS LEACH clustering in WSN.
 To compare performance of the random
LEACH and adaptive K-MEANS LEACH”.

Bibliography & References
 [Bandyopadhyay2003] S. Bandyopadhyay and E. J. Coyle, “An energy efficient hierarchical clustering algorithm for wireless
sensor networks.” in Proceedings of the IEEE Conference on Computer Communications (INFOCOM), 2003.
 [Cao2008] X. Cao, H. Zhang, J. Shi, and G. Cui, “Cluster heads election analysis for multi-hop wireless sensor networks
based on weighted graph and particle swarm optimization,” in Proceedings of the 4th International Conference on Natural
Computation (ICNC), vol. 7, 2008, pp. 599–603.
 [Guru2006] S. Guru, S. Halgamuge, and S. Fernando, “Particle swarm optimizers for cluster formation in wireless sensor
networks,” in Proceedings of the International Conference on Intelligent Sensors, Sensor Networks and Information
Processing (ISSNIP), S. K. Halgamuge, Ed., 2005, pp. 319–324.
 [Handy2002] M.J. Handy, M. Haas, D. Timmermann “Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-
Head Selection”;; 2002; http://www.vs.inf.ethz.ch/publ/se/IEEE_MWCN2002.pdf
 [Heinzelman2000] W. R Heinzelman, A. P Chandrakasan, and H. Balakrishnan,(2000), “Energy efficient communication
protocol for wireless micro-sensor networks,” in Proceedings of the 33rd Hawaii International Conference on System
Sciences.
 [Hussain2007] Sajid Hussain, Abdul Wasey Matin, Obidul Islam, Genetic Algorithm for Hierarchical Wireless Sensor
Networks, JOURNAL OF NETWORKS, VOL. 2, NO. 5, SEPTEMBER 2007
 [Kulkarni2010] Raghavendra V. Kulkarni, and Ganesh Kumar Venayagamoorthy, Particle Swarm Optimization in Wireless
Sensor Networks: A Brief Survey, IEEE transaction on system, man and cybernetics. Part C: Applications and Reviews,
2010 Digital Object Identifier 10.1109/TSMCC.2010.2054080
 [Latiff2007] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, “Energy-aware clustering for wireless sensor networks using
particle swarm optimization,” in Proceedings of the 18th IEEE International Symposium on Personal, Indoor and Mobile
Radio Communications (PIMRC), 2007, pp. 1–5.
 [Lindsey2002] S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-efficient gathering in sensor information systems,” in
Proceedings of the IEEE Aerospace Conference, March 2002.
 [Song2005] Song, Dezhen “Probabilistic Modeling of Leach Protocol and Computing Sensor Energy Consumption Rate in
Sensor Networks”;; February 22, 2005; http://www.cs.tamu.edu/academics/tr/tamu-cs-tr-2005-2-2

Performance evaluation of variants of particle swarm optimization algorithms for the purpose of clustering in wireless sensor networks

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Performance evaluation of variants of particle swarm optimization algorithms for the purpose of clustering in wireless sensor networks (20)

Recently uploaded (20)

Performance evaluation of variants of particle swarm optimization algorithms for the purpose of clustering in wireless sensor networks