Bingquan Chen, Xingfeng Guo, Yuanfeng Huang, M. Yang
{"title":"Improved DV-Hop Node location Optimization Algorithm Based on Adaptive Particle Swarm","authors":"Bingquan Chen, Xingfeng Guo, Yuanfeng Huang, M. Yang","doi":"10.1109/icaice54393.2021.00010","DOIUrl":null,"url":null,"abstract":"Aiming at the problem that the traditional DV-Hop node positioning algorithm uses the least square method to calculate the node coordinates, there is an error, while the traditional particle swarm optimization (PSO) algorithm is easy to fall into the local optimal solution. This paper proposes an improved DV-Hop adaptive particle swarm optimization. Hop positioning algorithm (APSO-DV-Hop). First, modify the average hop distance in the traditional DV-Hop positioning algorithm; Secondly, the improved adaptive particle swarm (PSO) algorithm is used to improve the local search capability of the particle swarm algorithm; Finally, the two improved algorithms are combined to improve the node positioning accuracy of the algorithm. Experimental simulation results show that the proposed algorithm has higher positioning accuracy under the same communication overhead and hardware conditions.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icaice54393.2021.00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Aiming at the problem that the traditional DV-Hop node positioning algorithm uses the least square method to calculate the node coordinates, there is an error, while the traditional particle swarm optimization (PSO) algorithm is easy to fall into the local optimal solution. This paper proposes an improved DV-Hop adaptive particle swarm optimization. Hop positioning algorithm (APSO-DV-Hop). First, modify the average hop distance in the traditional DV-Hop positioning algorithm; Secondly, the improved adaptive particle swarm (PSO) algorithm is used to improve the local search capability of the particle swarm algorithm; Finally, the two improved algorithms are combined to improve the node positioning accuracy of the algorithm. Experimental simulation results show that the proposed algorithm has higher positioning accuracy under the same communication overhead and hardware conditions.