基于最优信标节点的无线传感器网络质心定位算法

针对无线传感器网络(WSN)质心定位(CL)算法精度不高的问题,提出了一种基于最优信标节点的质心定位(OBCL)算法.OBCL算法采用了4个移动信标节点,首先,对移动信标节点的路径进行规划;然后,未知节点根据集合偏移度(SDD)从候选信标节点集合中选出最优信标节点来进行位置估计;其次,为了解决信标节点不足导致无法定位的问题,引入角色转变机制,未知节点在完成定位之后成为准信标节点来进行辅助定位;最后,为了保证网络中所有节点能够完成定位,在完成初次定位之后,需要进行重定位过程.仿真实验结果表明,与CL、基于加权的质心定位(WCL)、利用接收信号强度加权的质心定位(RR-WCL)这3种算法相比,OBCL算法的平均定位误差分别降低了67.7%、39.2%、24.4%.由于OBCL算法只需要4个移动信标节点就能达到较好的定位效果,因此适合应用于对网络成本低、定位精度要求高的场景.

Optimal beacon nodes-based centroid localization algorithm for wireless sensor network

To improve the accuracy of Centroid Localization (CL) algorithm in Wireless Sensor Network (WSN), an Optimal Beacon nodes-based Centroid Localization (OBCL) algorithm was proposed. In this algorithm, four mobile beacon nodes were used. First, the path for each mobile beacon node was planned. Second, the optimal beacon nodes were selected from the candidate beacon nodes by each unknown node to estimate location according to Set Deviation Degree (SDD). Besides, a role-change mechanism that an unknown node can assist other unknown nodes to locate as the expectant beacon node after it got its estimated location was adopted to solve the problem of beacon nodes' shortage. At last, to ensure that each unknown node could get its location, a relocation procedure was executed after the completion of the initial locating. The simulation results show that, the average locating error is respectively reduced by 67.7%, 39.2%, 24.4% comparing with the CL, WCL (Weighted Centroid Localization), RR-WCL (Weighted Centroid Localization based on Received signal strength indication Ration) algorithms. For the reason that OBCL can achieve better locating results using only four mobile beacon nodes, it is suitable for scenes which require low network cost and high locating accuracy.