Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area,...

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Main Author:	Razali, Mohd. Norsyarizad
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/79394/1/MohdNorsyarizadRazaliPFS2017.pdf
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id	my-utm-ep.79394
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spelling	my-utm-ep.793942018-10-14T08:45:18Z Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges 2017 Razali, Mohd. Norsyarizad QA Mathematics The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime. 2017 Thesis http://eprints.utm.my/id/eprint/79394/ http://eprints.utm.my/id/eprint/79394/1/MohdNorsyarizadRazaliPFS2017.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Razali, Mohd. Norsyarizad Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
description	The extensive applications of directional sensor networks (DSNs) in a wide range of situations have attracted a great deal of attention. One significant problem linked with DSNs is target coverage, which primarily operate based on simultaneously observing a group of targets occurring in a set area, hence maximizing the network lifetime. As there are limitations to the directional sensors’ sensing angle and energy resource, designing new techniques for effectively managing the energy consumption of the sensors is crucial. In this study, two problems were addressed. First, a new learning automata-based algorithm is proposed to solve the target coverage problem, in cases where sensors have multiple power levels (i.e., sensors have multiple sensing ranges), by selecting a subset of sensor directions that is able to monitor all the targets. In real applications, targets may have different coverage quality requirements, which leads to the second; the priority-based target coverage problem, which has not yet been investigated in the field of study. In this problem, two newly developed algorithms based on learning automata and greedy are proposed to select a subset of sensor directions in a way that different coverage quality requirements of all the targets could be satisfied. All of the proposed algorithms were assessed for their performances via a number of experiments. In addition, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. All algorithms are successful in solving the problems; however, the learning automata-based algorithms are proven to be superior by up to 18% comparing with the greedy-based algorithms, when considering extending the network lifetime.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Razali, Mohd. Norsyarizad
author_facet	Razali, Mohd. Norsyarizad
author_sort	Razali, Mohd. Norsyarizad
title	Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
title_short	Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
title_full	Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
title_fullStr	Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
title_full_unstemmed	Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
title_sort	learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2017
url	http://eprints.utm.my/id/eprint/79394/1/MohdNorsyarizadRazaliPFS2017.pdf
_version_	1747818217884090368

Learning automata-based solution to target coverage problem for directional sensor networks with adjustable sensing ranges

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