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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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 |
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TJ Mechanical Engineering and Machinery TJ Mechanical Engineering and Machinery |
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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 |
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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 |
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1776101413703647232 |