基于分布式加权多维定标的节点自身定位算法

提出了一种基于对称K最邻近（SKNN）传感器网络节点分布式精确定位算法．该算法首先采用SKNN方法选择每个节点的邻居节点，通过接收信号强度（RSS）方法测得每对节点之间的距离，构建节点距离矩阵，并以距离矩阵为输入，应用分布式多维加权算法对矩阵进行处理，从而获得传感器网络节点之间的局部映射关系．最后，根据参考节点的坐标对节点局部映射关系进行匹配，以获取节点坐标的全局映射．仿真实验表明，采用所提算法可以加强定位精度，提高计算效率，在有25个节点的传感器网络中，定位误差要比dwMDS方法低大约5%。

Node Self-Location Algorithm Based on Distributed and Weighted Multidimensional Scale

A new accurate,distributed location algorithm of a node based on the symmetric K-nearest-neighbor(SKNN) sensor networks is presented,in which SKNN method is used to select neighbor nodes firstly,the distance between each pair of nodes is measured by using the received signal strength(RSS) to construct the distance matrix of nodes,and the matrix is regarded as the inputs to be processed through the distributed and weighted multidimensional scale algorithm,thereby the local mapping relation between nodes of sensor networks is obtained.Finally,the local mapping relation of nodes is matched according to the coordinates of the reference node so as to acquire the global mapping of node's coordinates.Simulation results demonstrate that the proposed algorithm can increase the locating precision and computing efficiency.The locating error is about 5% less than that of dwMDS in sensors network with 25 nodes.