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 + d22)

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