相似度优化的无线传感器网络移动节点定位

针对无线传感器网络中移动节点的定位特性,提出了一种利用序列相似度改进的蒙特卡洛定位算法.该算法先利用各信标节点的信号强度值对移动节点初定位,优化原算法的采样区域.同时将信号值存储为目标序列,通过比较信标节点和样本点间序列与目标序列的相似度过滤样本点,并以相似度值作为加权标准计算移动节点坐标.仿真结果表明,与其他算法相比,在不同的信标节点密度下,定位误差减少了1%~10%,在不同的节点最大移动速度的情况下,定位误差减少了30%~40%.

Localization Optimized by Similarity for WSN Mobile Nodes

One of the most crucial tasks in wireless sensor networks (WSN) is to determine the locations of sensory nodes as they may not all be equipped with GPS receivers. In this paper, an improved algorithm called Monte Carlo localization weighted by similarity (MCWS) is proposed. MCWS optimizes the sampling area of Monte Carlo localization (MCL) by adopting the mobile node's location based on the received signal strength indicator (RSSI) as the new sampling center. The signal values are stored as a target sequence, and by comparing the similarity between samples' sequences and the target sequence, samples can be filtered. Also the similarity values are used as the weighted standards to calculate coordinate of the mobile node. Extensive simulation results confirm that the new localization approach outperforms other MCL algorithms. The MCWS algorithm reduces the localization error by 1%~10% under different density of beacon nodes and by 30%~40% under different maximum speed of mobile nodes, respectively.