A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks

A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs), which benefit ocean surveillance applica­tions, marine monitoring and underwater target detection, have advanced substantially in recent years. However, existing deployment solutions do not satisfy the deployment of mobile underwater sensor nodes as a sto...

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Main Author: Salmah Fattah
Format: Thesis
Language:English
English
Published: 2022
Subjects:
TK7800-8360 Electronics
Online Access:https://eprints.ums.edu.my/id/eprint/35662/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/35662/2/FULLTEXT.pdf
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spelling my-ums-ep.356622023-06-21T01:56:51Z A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks 2022 Salmah Fattah TK7800-8360 Electronics Underwater wireless sensor networks (UWSNs), which benefit ocean surveillance applica­tions, marine monitoring and underwater target detection, have advanced substantially in recent years. However, existing deployment solutions do not satisfy the deployment of mobile underwater sensor nodes as a stochastic system. Internal and external environmental problems concern maximum coverage in the deployment region while minimising energy consumption. To fill this gap, this research proposes and implements a multi-objective optimisation solution to balance conflicts concerning node deployment objectives. First, this research analyses the existing mobile underwater node deployment algorithms to identify the significant problems in existing solutions. Next, it establishes the research problems by implementing various existing algorithms using comparative analysis. Based on that analysis, this research suggests a hybrid algorithm: the Multi-Objective Optimisa­tion Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy). The method adapts the original Non-Dominated Sorting Genetic Algorithm II (NSGA-II) by introducing a hybridisation of adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition techniques. The algorithm introduces the fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method when one solution cannot dominate the other in terms of the fuzzy dominance level. The solution also proposes adaptive multi-parent crossover (AMP) to balance exploration and exploitation with new offspring, changing the number of parents involved in the crossover based on the execution of the new generation. The solution is further improved by introducing prospect theory to guarantee convergence through risk evaluation. The results obtained are then analysed to assess the proposed solution's performance in obtaining each deployment objective's optimal value. Finally, the proposed algorithm's effectiveness regarding node coverage, energy consumption, Pareto-optimal value, and algorithm execution time is validated using three Pareto-optimal metrics: including inverted generation distance (IGD), hypervolume, and diversity. Furthermore, this research utilises five commonly used two-objective ZDT test instances as benchmark tests, namely ZDT-1, ZDT-2, ZDT-3, ZDT-4, and ZDT-6. These tests use specific problem characteristics to impose the underlying proposed solution as well as three other systems. Pareto-optimal values obtained indicate that the proposed solution has almost complete coverage involving the actual Pareto front. Furthermore, all analysis and evaluation attributes indicate that the MOGA-AMPazy deployment algorithm can handle the multi-objective underwater sensor deployment problem better than other solutions. Thus, MOGA-AMPazy provides an efficient and comprehensive deployment solution for mobile sensor nodes in UWSNs. This study makes several noteworthy contributions to the body of knowledge concerning UWSNs, and it provides an excellent multi-objective representation to decision-makers or mission planners to monitor the region of interest (Rol). 2022 Thesis https://eprints.ums.edu.my/id/eprint/35662/ https://eprints.ums.edu.my/id/eprint/35662/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/35662/2/FULLTEXT.pdf text en validuser dphil doctoral Universiti Malaya Faculty Of Computer Science And Information Technology
institution Universiti Malaysia Sabah
collection UMS Institutional Repository
language English
English
topic TK7800-8360 Electronics
spellingShingle TK7800-8360 Electronics
Salmah Fattah
A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
description Underwater wireless sensor networks (UWSNs), which benefit ocean surveillance applica­tions, marine monitoring and underwater target detection, have advanced substantially in recent years. However, existing deployment solutions do not satisfy the deployment of mobile underwater sensor nodes as a stochastic system. Internal and external environmental problems concern maximum coverage in the deployment region while minimising energy consumption. To fill this gap, this research proposes and implements a multi-objective optimisation solution to balance conflicts concerning node deployment objectives. First, this research analyses the existing mobile underwater node deployment algorithms to identify the significant problems in existing solutions. Next, it establishes the research problems by implementing various existing algorithms using comparative analysis. Based on that analysis, this research suggests a hybrid algorithm: the Multi-Objective Optimisa­tion Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy). The method adapts the original Non-Dominated Sorting Genetic Algorithm II (NSGA-II) by introducing a hybridisation of adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition techniques. The algorithm introduces the fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method when one solution cannot dominate the other in terms of the fuzzy dominance level. The solution also proposes adaptive multi-parent crossover (AMP) to balance exploration and exploitation with new offspring, changing the number of parents involved in the crossover based on the execution of the new generation. The solution is further improved by introducing prospect theory to guarantee convergence through risk evaluation. The results obtained are then analysed to assess the proposed solution's performance in obtaining each deployment objective's optimal value. Finally, the proposed algorithm's effectiveness regarding node coverage, energy consumption, Pareto-optimal value, and algorithm execution time is validated using three Pareto-optimal metrics: including inverted generation distance (IGD), hypervolume, and diversity. Furthermore, this research utilises five commonly used two-objective ZDT test instances as benchmark tests, namely ZDT-1, ZDT-2, ZDT-3, ZDT-4, and ZDT-6. These tests use specific problem characteristics to impose the underlying proposed solution as well as three other systems. Pareto-optimal values obtained indicate that the proposed solution has almost complete coverage involving the actual Pareto front. Furthermore, all analysis and evaluation attributes indicate that the MOGA-AMPazy deployment algorithm can handle the multi-objective underwater sensor deployment problem better than other solutions. Thus, MOGA-AMPazy provides an efficient and comprehensive deployment solution for mobile sensor nodes in UWSNs. This study makes several noteworthy contributions to the body of knowledge concerning UWSNs, and it provides an excellent multi-objective representation to decision-makers or mission planners to monitor the region of interest (Rol).
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Salmah Fattah
author_facet Salmah Fattah
author_sort Salmah Fattah
title A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
title_short A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
title_full A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
title_fullStr A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
title_full_unstemmed A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
title_sort hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
granting_institution Universiti Malaya
granting_department Faculty Of Computer Science And Information Technology
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/35662/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/35662/2/FULLTEXT.pdf
_version_ 1776102893555810304

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