A clustering approach to adaptively improve energy efficiency and load balancing in WSNs

Clustering has been widely used in Wireless Sensor Networks (WSN) to solve problems associated with large nodes and effectively conserve energy, while load balancing has equally been used to effectively optimize network resources such as bandwidth. In this research, we propose a routing protocol, t...

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Main Author:	Haneen, Ahmed Hasan
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Cluster analysis Sensor networks Wireless LANs
Online Access:	http://psasir.upm.edu.my/id/eprint/83805/1/FSKTM%202019%208%20-%20IR.pdf
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id	my-upm-ir.83805
record_format	uketd_dc
spelling	my-upm-ir.838052020-10-20T04:45:22Z A clustering approach to adaptively improve energy efficiency and load balancing in WSNs 2019-01 Haneen, Ahmed Hasan Clustering has been widely used in Wireless Sensor Networks (WSN) to solve problems associated with large nodes and effectively conserve energy, while load balancing has equally been used to effectively optimize network resources such as bandwidth. In this research, we propose a routing protocol, the Hierarchical Energy-Balancing Multipath routing protocol (HEBM) for Wireless Sensor Networks, which combines load balancing and clustering to significantly improve WSN services, e.g. information routing. In our approach, load traffic is shared amongst nodes in the same cluster with the aim of minimizing dropping probability resulting from queue overflow at some nodes. The benefits of our proposed work include: attain an improved balanced in cluster size which could guarantee minimal energy dissipation in the entire network, balancing the energy dissipation among the sensor nodes, which in turn extends the lifetime of the network. The cluster heads are optimally selected and properly distributed over the entire network thus allowing member nodes to reach them without expending much energy, while adequately balancing the load. Additionally, member nodes are turned off periodically based on set sleeping control rules in order to optimize their energy consumption. The methodology to be used for this work is simulation on NS2 discrete event simulator. We intend to use two scenarios in our simulations. In the first scenario, 100 nodes are uniformly and randomly distributed in a 200 square meters area. To study the effect of scale on the performance of HEBM, 200 nodes are uniformly and randomly dispersed in a field of size 200 square meters in the second scenario. In both instances, we assume that the BS is at the center of the field. Performance metrics include: Energy consumption, Network lifetime, latency, and residual energy. Cluster analysis Sensor networks Wireless LANs 2019-01 Thesis http://psasir.upm.edu.my/id/eprint/83805/ http://psasir.upm.edu.my/id/eprint/83805/1/FSKTM%202019%208%20-%20IR.pdf text en public masters Universiti Putra Malaysia Cluster analysis Sensor networks Wireless LANs Ayob, Fahrul Hakim
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Ayob, Fahrul Hakim
topic	Cluster analysis Sensor networks Wireless LANs
spellingShingle	Cluster analysis Sensor networks Wireless LANs Haneen, Ahmed Hasan A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
description	Clustering has been widely used in Wireless Sensor Networks (WSN) to solve problems associated with large nodes and effectively conserve energy, while load balancing has equally been used to effectively optimize network resources such as bandwidth. In this research, we propose a routing protocol, the Hierarchical Energy-Balancing Multipath routing protocol (HEBM) for Wireless Sensor Networks, which combines load balancing and clustering to significantly improve WSN services, e.g. information routing. In our approach, load traffic is shared amongst nodes in the same cluster with the aim of minimizing dropping probability resulting from queue overflow at some nodes. The benefits of our proposed work include: attain an improved balanced in cluster size which could guarantee minimal energy dissipation in the entire network, balancing the energy dissipation among the sensor nodes, which in turn extends the lifetime of the network. The cluster heads are optimally selected and properly distributed over the entire network thus allowing member nodes to reach them without expending much energy, while adequately balancing the load. Additionally, member nodes are turned off periodically based on set sleeping control rules in order to optimize their energy consumption. The methodology to be used for this work is simulation on NS2 discrete event simulator. We intend to use two scenarios in our simulations. In the first scenario, 100 nodes are uniformly and randomly distributed in a 200 square meters area. To study the effect of scale on the performance of HEBM, 200 nodes are uniformly and randomly dispersed in a field of size 200 square meters in the second scenario. In both instances, we assume that the BS is at the center of the field. Performance metrics include: Energy consumption, Network lifetime, latency, and residual energy.
format	Thesis
qualification_level	Master's degree
author	Haneen, Ahmed Hasan
author_facet	Haneen, Ahmed Hasan
author_sort	Haneen, Ahmed Hasan
title	A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
title_short	A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
title_full	A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
title_fullStr	A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
title_full_unstemmed	A clustering approach to adaptively improve energy efficiency and load balancing in WSNs
title_sort	clustering approach to adaptively improve energy efficiency and load balancing in wsns
granting_institution	Universiti Putra Malaysia
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/83805/1/FSKTM%202019%208%20-%20IR.pdf
_version_	1747813419818418176

A clustering approach to adaptively improve energy efficiency and load balancing in WSNs

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