当前位置>主页 > 期刊在线 > 计算机技术 >

计算机技术22年3期

基于 GWO 和 PSO 协同优化的 DV-Hop 定位算法
朱子行,陈辉
(安徽理工大学,安徽 淮南 232001)

摘  要:无线传感器网络具有感知和处理信息的能力,只有当被测网络内节点的位置已知时,节点传递给用户的信息才有意义。针对 DV-Hop 定位中传统最小二乘法不可避免的精度低的缺点,引入粒子群算法(PSO)和灰狼优化器(GWO)来估计未知节点位置。粒子群算法具有个体记忆的特点,采用粒子位置更新代替灰狼个体位置更新,使灰狼算法在优化上具有可记忆性。仿真数据表明,改进后的算法可以有效降低节点定位误差,实现更高的定位精度。


关键词:无线传感器网络;DV-Hop;灰狼优化器;粒子群算法



DOI:10.19850/j.cnki.2096-4706.2022.03.023


中图分类号:TN934                                         文献标识码:A                                   文章编号:2096-4706(2022)03-0088-04


DV-Hop Positioning Algorithm Based on GWO and PSO Collaborative Optimization

ZHU Zihang, CHEN Hui

(Anhui University of Science & Technology, Huainan 232001, China)

Abstract: Wireless sensor networks have the ability to sense and process information, and the information passed by the nodes to the user is meaningful only when the location of the nodes within the network under test is known. In view of the inevitable shortcoming of low precision of the traditional least squares method in DV-Hop (distance vector-hop) localization, the Particle Swarm optimization (PSO) and the Gray Wolf Optimizer (GWO) are introduced to estimate unknown node positions. The Particle Swarm optimization has the characteristics of individual memory, and the particle position update is used to replace the gray wolf individual position update, so that the gray wolf algorithm has memory in optimization. The simulation data show that the improved algorithm can effectively reduce the node positioning error and achieve higher positioning accuracy.

Keywords: wireless sensor network; DV-Hop; Grey Wolf Optimizer; Particle Swarm optimization


参考文献:

[1] FAN Z,CHU H,WANG F,et al.A New Non-Line-of-Sight Localization Algorithm for Wireless Sensor Network [C]//2020 IEEE 6th International Conference on Computer and Communications (ICCC).Chengdu:IEEE,2020:858-862.

[2] 缪祎晟,赵春江,吴华瑞 . 信道与能耗感知的农田 WSN 机会路由优化方法 [J]. 重庆理工大学学报(自然科学),2021,35(9):1-7.

[3] 杨艳芳,王伟,王召巴 . 基于 WSN 室内定位的路径损耗模型参数算法研究 [J]. 电子测量技术,2021,44(13):54-58.

[4] KANWAR V,KUMAR A.“Distance Vector Hop Based Range Free Localization in WSN using Genetic Algorithm” [C]//2019 6th International Conference on Computing for Sustainable Global Development (INDIACom).New Delhi:IEEE,2019:724-728.

[5] NICULESCU D,NATH B.DV Based Positioning in Ad Hoc Networks [J]. Telecommunication Systems,2003,22(1-4):267-280.

[6] AZAD J,KANWAR V,KUMAR A.Effect of Network Topologies on Localization using DV-Hop based PSO Algorithm [C]//2021 5th International Conference on Trends in Electronics and Informatics (ICOEI).Tirunelveli:IEEE,2021:40-45.

[7] 石琴琴,徐强,张建平 . 基于距离修正及灰狼优化算法对DV-Hop 定位的改进 [J]. 传感技术学报,2019,32(10):1549-1555.

[8] 李真,王帆,王冉珺 . 一种结合灰狼算法的粒子群优化算法 [J]. 计算机测量与控制,2021,29(10):217-222.

[9] 吴之舟,张玲华 . 基于加权和 RSSI 测距的 DV?Hop 定位算法 [J]. 数据采集与处理,2021,36(6):1217-1225.


作者简介:朱子行(1998—),男,汉族,安徽淮北人,在读硕士,研究方向:无线传感器网络定位方向;通讯作者:陈辉(1973—),男,汉族,安徽庐江人,副教授,硕士生导师,博士,研究方向:无线传感器网络,机器学习、物联网技术及应用。