基于邻居协助的无线传感器网络定位算法  

Neighbor-assisted localization algorithm for wireless sensor networks

作　　者：刘巍[1] 杨美 阮芸星[1] 蔡霞[1] Liu Wei;Yang Mei;Ruan Yunxing;Cai Xia(School of Computer Science,Central China Normal University,Wuhan,Hubei 430079,China)

机构地区：[1]华中师范大学计算机学院,湖北武汉430079

出　　处：《计算机时代》2020年第4期18-20,共3页Computer Era

基　　金：中央高校基本科研业务费专项资金(CCNU18QN018)。

摘　　要：在无线传感器网络定位中,所有的节点都可以测量彼此之间的距离。由于这些测量值存在误差,在计算节点位置时需要选择合适的测量距离。首先将节点测量信息构成有向图,然后获取从锚节点到盲节点的拓扑顺序,最后利用最小二乘法计算盲节点的位置。在挑选有向边时采用粒子群算法,以盲节点的估计位置的联合概率为适应度函数来评价盲节点的估计位置的准确性,从而挑选最优测量值作为节点位置。相比采用距离无关的智能定位算法,本算法的定位准确度更高。In wireless sensor network localization, all nodes can measure the distance between each other. Due to the errors of these measurements, it is necessary to select the appropriate measurement distances when calculating the nodes’ position. The proposed method is to construct a digraph by measuring distances firstly;then to obtain the topological order from anchor node to blind node;finally, the locations of blind nodes are calculated by least square method. When selecting the directed edge, particle swarm optimization algorithm is used to evaluate the accuracy of the estimated position of the blind nodes with the joint probability of the estimated positions of the blind nodes as the fitness function. Thus, the optimal measurement value is selected as the node location. Compared with the range-free localization intelligent algorithm, this algorithm has higher accuracy.

关 键 词：邻居协助 无线传感器网络 节点定位 联合概率 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]