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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9944062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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