基于布谷鸟搜索算法的无线传感器网络节点定位  

Node localization of wireless sensor networks based on cuckoo search algorithm

作　　者：肖晓丽[1] 李旦江[1] 谭柳斌 XIAO Xiaoli;LI Danjiang;TAN Liubin(Institute of Computer and Communication Engineering, Changsha University of Sciences and Technology, Changsha 410114,China;Hunan Mass Media Vocational Technical College, Changsha 410007, China)

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114 [2]湖南大众传媒技术学院,长沙410007

出　　处：《计算机工程与应用》2017年第2期141-145,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61303043);湖南省自然科学基金(No.13JJ4052)

摘　　要：无线传感器网络的节点定位实际上是解决测量距离和测距误差的多维约束优化问题。针对最小二乘方法对测距误差敏感的不足,提出一种基于布谷鸟搜索算法的无线传感器网络节点定位算法。该算法利用全局和局部寻优能力强的布谷鸟算法求解定位过程中的多维约束优化问题;通过设定相应的约束适应度函数来减小定位过程的搜索范围,加快了收敛速度,能够快速地确定未知节点的位置。仿真结果表明:相较于粒子群算法和最小二乘算法,该算法能有效地抑制测距误差对定位的影响,提高节点的定位精度,具备很好的实用性。Node localization in wireless sensor networks is a multidimensional constraint optimization problem to solve measurement distance and range. Focusing on the sensitive features of least square method used to measure ranging error,a novel localization algorithm of wireless sensor network is proposed based on cuckoo search algorithm. The constraint optimization problem is solved by using the cuckoo search due to it strongly global and local search ability in this algorithm.By setting the corresponding constraint fitness function, the hunting zone is reduced during the positioning search, the convergence rate is speeded up, it also can quickly find the position of unknown node. The simulation results show that the algorithm effectively suppresses the influence of ranging error and improves the accuracy of node positioning when compared with Particle Swarm Optimization(PSO)algorithm and Least Squares(LS)algorithm. Hence it has better practicability.

关 键 词：无线传感器网络 布谷鸟搜索算法 最小二乘算法 约束优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]