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Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer

One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaoti...

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Autores principales: Zeng, Chuijie, Qin, Tao, Tan, Wei, Lin, Chuan, Zhu, Zhaoqiang, Yang, Jing, Yuan, Shangwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944062/
https://www.ncbi.nlm.nih.gov/pubmed/36810401
http://dx.doi.org/10.3390/biomimetics8010070
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author Zeng, Chuijie
Qin, Tao
Tan, Wei
Lin, Chuan
Zhu, Zhaoqiang
Yang, Jing
Yuan, Shangwei
author_facet Zeng, Chuijie
Qin, Tao
Tan, Wei
Lin, Chuan
Zhu, Zhaoqiang
Yang, Jing
Yuan, Shangwei
author_sort Zeng, Chuijie
collection PubMed
description One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO’s accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN’s coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively.
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spelling pubmed-99440622023-02-23 Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer Zeng, Chuijie Qin, Tao Tan, Wei Lin, Chuan Zhu, Zhaoqiang Yang, Jing Yuan, Shangwei Biomimetics (Basel) Article One of the most important challenges for heterogeneous wireless sensor networks (HWSNs) is adequate network coverage and connectivity. Aiming at this problem, this paper proposes an improved wild horse optimizer algorithm (IWHO). Firstly, the population’s variety is increased by using the SPM chaotic mapping at initialization; secondly, the WHO and Golden Sine Algorithm (Golden-SA) are hybridized to improve the WHO’s accuracy and arrive at faster convergence; Thirdly, the IWHO can escape from a local optimum and broaden the search space by using opposition-based learning and the Cauchy variation strategy. The results indicate that the IWHO has the best capacity for optimization by contrasting the simulation tests with seven algorithms on 23 test functions. Finally, three sets of coverage optimization experiments in different simulated environments are designed to test the effectiveness of this algorithm. The validation results demonstrate that the IWHO can achieve better and more effective sensor connectivity and coverage ratio compared to that of several algorithms. After optimization, the HWSN’s coverage and connectivity ratio attained 98.51% and 20.04%, and after adding obstacles, 97.79% and 17.44%, respectively. MDPI 2023-02-06 /pmc/articles/PMC9944062/ /pubmed/36810401 http://dx.doi.org/10.3390/biomimetics8010070 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zeng, Chuijie
Qin, Tao
Tan, Wei
Lin, Chuan
Zhu, Zhaoqiang
Yang, Jing
Yuan, Shangwei
Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title_full Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title_fullStr Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title_full_unstemmed Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title_short Coverage Optimization of Heterogeneous Wireless Sensor Network Based on Improved Wild Horse Optimizer
title_sort coverage optimization of heterogeneous wireless sensor network based on improved wild horse optimizer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944062/
https://www.ncbi.nlm.nih.gov/pubmed/36810401
http://dx.doi.org/10.3390/biomimetics8010070
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