基于测距修正与改进蜣螂优化的DV-Hop定位算法  

DV-Hop Localization Algorithm Based on Ranging Correction and Improved Dung Beetle Optimizer

作　　者：雒明世[1] 陈利军 金浩 LUO Mingshi;Chen Lijun;Jin Hao(School of Computer Science,Xi'an Shiyou University,Xi'an Shaanxi 710065,China)

机构地区：[1]西安石油大学计算机学院,陕西西安710065

出　　处：《无线通信技术》2024年第4期32-38,共7页Wireless Communication Technology

基　　金：陕西省自然科学基础研究计划(编号:2023-JC-YB-591)。

摘　　要：为了提升传统DV-Hop在无线传感网络中节点定位的精度,提出了一种基于测距修正与改进蜣螂优化(Dung Beetle Optimizer,DBO)的DV-Hop定位算法(IDBODV-Hop)。首先,使用多通信半径对节点间的最小跳数进行细化,并使用加权因子对节点间的平均跳距进行修正,以减小算法定位误差;其次将蜣螂优化算法用于求解节点定位目标函数最优值;最后,引入拉丁超立方抽样、变螺旋搜索以及准反向学习和柯西变异,减少了算法陷入局部最优的可能性,进一步提高了算法的收敛速度以及定位精度。通过仿真模拟,IDBODV-Hop算法在不同的条件下平均归一化定位误差与DV-Hop算法、IGWODV-Hop以及ISSADV-Hop相比,分别下降了24.29%、11.11%、6.47%,证明了IDBODV-Hop算法能有效减少定位误差。In order to improve the accuracy of traditional DV-Hop for node localization in wireless sensing networks,an DV-Hop localization algorithm based on ranging correction and improved dung beetle optimizer(IDBODV-Hop)is proposed.Firstly,the minimum number of hops between nodes is refined using multiple communication radius and the average hop distance between nodes is corrected using a weighting factor to reduce the algorithm localization error;secondly,Dung Beetle Optimizer is used to solve the optimal value of the node localization objective function;finally,the introduction of Latin hypercube sampling,variable spiral search,backward learning and the Cauchy mutation reduces the likelihood of the algorithm falling into a local optimum,and further enhances the algorithm's convergence speed as well as localization accuracy.Through simulation,the average normalized localization error of IDBODV-Hop algorithm under different conditions is reduced by 24.29%,11.11%and 6.47%compared with the traditional DV-Hop algorithm,IGWODV-Hop algorithms and ISSADV-Hop algorithms,which proves that the IDBODV-Hop algorithm can effectively reduce the localization error.

关 键 词：无线传感器网络 DV-HOP 跳距修正 蜣螂优化算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]