A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions
Clicks: 125
ID: 269596
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
125 views
19 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #25 of 39 articles by views in information
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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.
| Reference Key |
meng2020informationa
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Yinghui Meng;Qianying Zhi;Qiuwen Zhang;Ni Yao;Meng, Yinghui;Zhi, Qianying;Zhang, Qiuwen;Yao, Ni; |
| Journal | information |
| Year | 2020 |
| DOI |
10.3390/info11100488
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.