Energy efficient clustering techniques for data collection in wireless sensor networks

The thesis addresses the problem of energy constraint in Wireless Sensor Networks which requires efficient management of the energy which is mainly from battery source. The focus is to create energy efficient techniques for data collection and to ensure network connectivity in cluster-based routing...

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Main Author:	Dahnil Sikumbang, Dahlila Putri
Format:	Thesis
Published:	2013
Subjects:	TJ Mechanical Engineering and Machinery
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id	my-mmu-ep.5782
record_format	uketd_dc
spelling	my-mmu-ep.57822023-04-13T01:11:26Z Energy efficient clustering techniques for data collection in wireless sensor networks 2013-03 Dahnil Sikumbang, Dahlila Putri TJ Mechanical Engineering and Machinery TK Electrical engineering. Electronics Nuclear engineering The thesis addresses the problem of energy constraint in Wireless Sensor Networks which requires efficient management of the energy which is mainly from battery source. The focus is to create energy efficient techniques for data collection and to ensure network connectivity in cluster-based routing in Wireless Sensor Networks. Thus, the thesis first develops the energy dissipation model in term of number of neighbours connected to a node (node degree) to obtain minimum energy dissipation in the network. The node degree becomes the basis to constraint the cluster size so that throughout the network operation the network remains connected. Based on the idea of constraint cluster size, a second study proposes a new cluster-based routing algorithm called Topology Controlled Adaptive Clustering (TCAC). The proposed clustering technique integrates dynamic transmission power control schemes to constraint clusters which enable cluster heads to fulfill minimum node degree to improve network lifetime and to maintain connectivity throughout the network lifetime. 2013-03 Thesis http://shdl.mmu.edu.my/5782/ http://erep.mmu.edu.my/ phd doctoral Multimedia University Faculty of Computing and Informatics EREP: 7690
institution	Multimedia University
collection	MMU Institutional Repository
topic	TJ Mechanical Engineering and Machinery TJ Mechanical Engineering and Machinery
spellingShingle	TJ Mechanical Engineering and Machinery TJ Mechanical Engineering and Machinery Dahnil Sikumbang, Dahlila Putri Energy efficient clustering techniques for data collection in wireless sensor networks
description	The thesis addresses the problem of energy constraint in Wireless Sensor Networks which requires efficient management of the energy which is mainly from battery source. The focus is to create energy efficient techniques for data collection and to ensure network connectivity in cluster-based routing in Wireless Sensor Networks. Thus, the thesis first develops the energy dissipation model in term of number of neighbours connected to a node (node degree) to obtain minimum energy dissipation in the network. The node degree becomes the basis to constraint the cluster size so that throughout the network operation the network remains connected. Based on the idea of constraint cluster size, a second study proposes a new cluster-based routing algorithm called Topology Controlled Adaptive Clustering (TCAC). The proposed clustering technique integrates dynamic transmission power control schemes to constraint clusters which enable cluster heads to fulfill minimum node degree to improve network lifetime and to maintain connectivity throughout the network lifetime.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Dahnil Sikumbang, Dahlila Putri
author_facet	Dahnil Sikumbang, Dahlila Putri
author_sort	Dahnil Sikumbang, Dahlila Putri
title	Energy efficient clustering techniques for data collection in wireless sensor networks
title_short	Energy efficient clustering techniques for data collection in wireless sensor networks
title_full	Energy efficient clustering techniques for data collection in wireless sensor networks
title_fullStr	Energy efficient clustering techniques for data collection in wireless sensor networks
title_full_unstemmed	Energy efficient clustering techniques for data collection in wireless sensor networks
title_sort	energy efficient clustering techniques for data collection in wireless sensor networks
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics
publishDate	2013
_version_	1776101413703647232

Energy efficient clustering techniques for data collection in wireless sensor networks

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