A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile senso...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Wei-Min Zheng, Ning Liu, Qing-Wei Chai, Shu-Chuan Chu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/335c1e8245c84e3d8dc2a0cd1eb680da
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:335c1e8245c84e3d8dc2a0cd1eb680da
record_format	dspace
spelling	oai:doaj.org-article:335c1e8245c84e3d8dc2a0cd1eb680da2021-11-22T01:09:47ZA Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization1530-867710.1155/2021/1676879https://doaj.org/article/335c1e8245c84e3d8dc2a0cd1eb680da2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1676879https://doaj.org/toc/1530-8677The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.Wei-Min ZhengNing LiuQing-Wei ChaiShu-Chuan ChuHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Wei-Min Zheng Ning Liu Qing-Wei Chai Shu-Chuan Chu A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
description	The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.
format	article
author	Wei-Min Zheng Ning Liu Qing-Wei Chai Shu-Chuan Chu
author_facet	Wei-Min Zheng Ning Liu Qing-Wei Chai Shu-Chuan Chu
author_sort	Wei-Min Zheng
title	A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
title_short	A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
title_full	A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
title_fullStr	A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
title_full_unstemmed	A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization
title_sort	compact adaptive particle swarm optimization algorithm in the application of the mobile sensor localization
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/335c1e8245c84e3d8dc2a0cd1eb680da
work_keys_str_mv	AT weiminzheng acompactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT ningliu acompactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT qingweichai acompactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT shuchuanchu acompactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT weiminzheng compactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT ningliu compactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT qingweichai compactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization AT shuchuanchu compactadaptiveparticleswarmoptimizationalgorithmintheapplicationofthemobilesensorlocalization
_version_	1718418390638395392

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

Ejemplares similares