A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network

Wireless Sensor Networks (WSNs) consist of huge number of sensor nodes with limited energy that are scattered in a restricted geographic region. Since WSN's nodes are very small and have limited battery power, decreasing energy consumption due to increasing network lifetime is the biggest issue...

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Main Author:	Alipanah, Farab
Format:	Thesis
Published:	2014
Subjects:	TJ Mechanical engineering and machinery
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id	my-utm-ep.42255
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spelling	my-utm-ep.422552020-08-23T06:30:53Z A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network 2014 Alipanah, Farab TJ Mechanical engineering and machinery Wireless Sensor Networks (WSNs) consist of huge number of sensor nodes with limited energy that are scattered in a restricted geographic region. Since WSN's nodes are very small and have limited battery power, decreasing energy consumption due to increasing network lifetime is the biggest issue in these networks. Previous studies have demonstrated that if network nodes are organized in a number of clusters, energy dissipation reduces which leads to increase in network lifetime. Establishing control over the number and place of CHs (cluster heads) and clusters size has always been a challenge. Researchers usually ignore to consider all effective factors, such as: remaining energy, distance to BS, distance to other nodes and, number and the places of CHs during clustering. In this study, we put attention into minimizing the WSN energy consumption to increasing the network lifetime by finding the best CHs. Investigating of each WSN factors on its lifetime was studied as well. By using Genetic Algorithm (GA), almost all effective factors considered for more efficient clustering. The GA's steps were based on last researcher's findings and its fitness function' formula included most WSN’s lifetime factors. Simulation results though using NS-2 showed that the proposed algorithm decreased the WSN's energy consumption which leaded to increasing the network lifetime. It improved the First Node Dies (FND) and Half of the Nodes Dies (HND) about 40% and 50%, respectively over LEACH, 30% and 37% for LEACH-C that caused to energy balance of cluster heads and leaded to longer lifetime of network. It is evident from results that our approach satisfies our expectations. Our LEACH-CGA algorithm clearly decreases the energy consumption, postpones FND and HND and increases the WSN's lifetime. 2014 Thesis http://eprints.utm.my/id/eprint/42255/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:105675 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Alipanah, Farab A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
description	Wireless Sensor Networks (WSNs) consist of huge number of sensor nodes with limited energy that are scattered in a restricted geographic region. Since WSN's nodes are very small and have limited battery power, decreasing energy consumption due to increasing network lifetime is the biggest issue in these networks. Previous studies have demonstrated that if network nodes are organized in a number of clusters, energy dissipation reduces which leads to increase in network lifetime. Establishing control over the number and place of CHs (cluster heads) and clusters size has always been a challenge. Researchers usually ignore to consider all effective factors, such as: remaining energy, distance to BS, distance to other nodes and, number and the places of CHs during clustering. In this study, we put attention into minimizing the WSN energy consumption to increasing the network lifetime by finding the best CHs. Investigating of each WSN factors on its lifetime was studied as well. By using Genetic Algorithm (GA), almost all effective factors considered for more efficient clustering. The GA's steps were based on last researcher's findings and its fitness function' formula included most WSN’s lifetime factors. Simulation results though using NS-2 showed that the proposed algorithm decreased the WSN's energy consumption which leaded to increasing the network lifetime. It improved the First Node Dies (FND) and Half of the Nodes Dies (HND) about 40% and 50%, respectively over LEACH, 30% and 37% for LEACH-C that caused to energy balance of cluster heads and leaded to longer lifetime of network. It is evident from results that our approach satisfies our expectations. Our LEACH-CGA algorithm clearly decreases the energy consumption, postpones FND and HND and increases the WSN's lifetime.
format	Thesis
qualification_level	Master's degree
author	Alipanah, Farab
author_facet	Alipanah, Farab
author_sort	Alipanah, Farab
title	A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
title_short	A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
title_full	A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
title_fullStr	A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
title_full_unstemmed	A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
title_sort	clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
_version_	1747816727631101952

A clustering method based on genetic algorithm to reduce energy consumption in wireless sensor network

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