基于密度分簇的无线传感器网络定位算法

针对MDS-MAP(P)算法存在节点间最短路径距离计算误差、合并误差及算法复杂度过高等问题，提出了一种基于密度分簇的算法MDS-MAP(DB)。该算法选择邻居节点数最多的节点作为分簇机制的开始节点，一跳邻居节点组成的簇域内利用三角不等式法则测距，两跳内节点组成的簇域内利用最短路径法测距，且每个簇域内只有簇头节点执行测距算法，降低了测距误差及算法计算复杂度，提高了算法的性能。仿真实验结果表明，该算法具有更小的定位误差。

Density-Based Clustering Localization Algorithm for Wireless Sensor Networks

The MDS-MAP(P) localization algorithm has shortage of calculation errors of the shortest path distance, combined error, and the higher complexity of the algorithm. In order to solve these problems, A density-based clustering localization algorithm called MDS-MAP(DB) is proposed. The algorithm selects a node which has most neighbor nodes as the start node in the clustering mechanism. The clustering which is built by 1 hop neighbor nodes uses the triangle inequality estimating missing distances. The clustering which is built by 2 hop nodes uses the shortest path distance estimating missing distances. In all clustering, only the cluster head executes the algorithm of estimating distance. In this way, the error of estimating distances and the computational complexity of this algorithm have been reduced. The results of simulation show that the proposed algorithm can reduces the average estimation errors.