自适应灰狼算法优化的RSSI加权质心定位算法  

RSSI weighted centroid location algorithm optimized by self-adaptive grey wolf optimizer algorithm

作　　者：王磊杨 王改云[2] 李林 董建 秦娟娟 WANG Leiyang;WANG Gaiyun;LI Lin;DONG Jian;QIN Juanjuan(Department of Electronic and Information Engineering,Bozhou University,Bozhou 236800,China;School of Electronic Engineering and Automation,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]亳州学院电子与信息工程系,安徽亳州236800 [2]桂林电子科技大学电子工程与自动化学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2024年第2期196-202,共7页Journal of Guilin University of Electronic Technology

基　　金：安徽省高校自然科学基金(2022AH052412,2023AH052274);安徽省高校一般项目(BYZ2019C07)。

摘　　要：无线传感器网络节点的定位过程中未知节点的邻居锚节点数量会严重影响定位精度。为解决该问题,提出一种自适应灰狼算法优化的RSSI加权质心定位(AD-GWO-RSSI)算法。该算法首先根据传感器网络的连通关系,计算出所有未知节点的平均锚节点数量和每个未知节点的邻居锚节点数量,然后取两者的平均值作为GWO算法优化每个未知节点RSSI加权质心定位过程的阈值,最后将优化后的高精度定位节点作为伪锚节点对其他未知节点进行定位。实验结果表明,通过邻居锚节点数量关系获得自适应阈值的GWO算法去优化RSSI加权质心定位算法,不仅能达到较高的定位精度,还具有较强的泛化性和稳定性。In the localization process of wireless sensor network nodes,the number of neighboring anchor nodes can affect the localization accuracy of unknown nodes.Therefore,a self-adaptive grey wolf algorithm optimized RSSI weighted centroid location(ADGWO-RSSI)algorithm was proposed.Firstly,the algorithm calculated the average number of anchor nodes for all unknown nodes and the number of neighboring anchor nodes for each unknown node based on the connectivity relationship of the sensor network.Secondly the average value of the two was used as the threshold of the GWO algorithm to optimize the RSSI weighted centroid positioning process.Finally,the optimized high-precision localization node was used as a pseudo-anchor node to locate other unknown nodes.The experimental results show that the GWO Algorithm-based RSSI weighted centroid localization algorithm,which obtains an adaptive threshold through the number of neighboring anchor nodes,achieves high localization accuracy,and has strong generalization and stability.

关 键 词：锚节点 GWO RSSI 定位精度 加权质心定位 

分 类 号：TN929.5[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]