A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions
At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/11/10/488 |
| _version_ | 1797550373509005312 |
|---|---|
| author | Yinghui Meng Qianying Zhi Qiuwen Zhang Ni Yao |
| author_facet | Yinghui Meng Qianying Zhi Qiuwen Zhang Ni Yao |
| author_sort | Yinghui Meng |
| collection | DOAJ |
| description | At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes. |
| first_indexed | 2024-03-10T15:28:22Z |
| format | Article |
| id | doaj.art-29ff0d5a13c1491da6a04230a047282d |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T15:28:22Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-29ff0d5a13c1491da6a04230a047282d2023-11-20T17:49:24ZengMDPI AGInformation2078-24892020-10-01111048810.3390/info11100488A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave RegionsYinghui Meng0Qianying Zhi1Qiuwen Zhang2Ni Yao3School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaAt present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.https://www.mdpi.com/2078-2489/11/10/488nodes localizationconcave regionintersection ratiosimilar pathparticle swarm optimization algorithm |
| spellingShingle | Yinghui Meng Qianying Zhi Qiuwen Zhang Ni Yao A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions Information nodes localization concave region intersection ratio similar path particle swarm optimization algorithm |
| title | A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions |
| title_full | A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions |
| title_fullStr | A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions |
| title_full_unstemmed | A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions |
| title_short | A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions |
| title_sort | two stage particle swarm optimization algorithm for wireless sensor nodes localization in concave regions |
| topic | nodes localization concave region intersection ratio similar path particle swarm optimization algorithm |
| url | https://www.mdpi.com/2078-2489/11/10/488 |
| work_keys_str_mv | AT yinghuimeng atwostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT qianyingzhi atwostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT qiuwenzhang atwostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT niyao atwostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT yinghuimeng twostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT qianyingzhi twostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT qiuwenzhang twostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions AT niyao twostageparticleswarmoptimizationalgorithmforwirelesssensornodeslocalizationinconcaveregions |