大规模无线传感器网络快速定位算法

A fast localization algorithm for large-scale wireless sensor networks

针对大规模无线传感器网络(WSN)定位算法普遍存在时间复杂度过高的问题,实现了WSN邻近节点间逐对"比较关系"矩阵到位置坐标的快速可视化映射.算法首先引进快速映射(FastMap)计算过程,把参考节点作为定位的轴点,选择距离最长的对角线作为轴线,避免了相对坐标到绝对坐标的转换过程;将FastMap运算的概略坐标作为MDS(multi-dimensional scaling)的输入,提高了定位精度.在MATLAB软件中设置600m×600m的定位区域,利用无线信号衰减模型产生虚拟测试点,分别针对包含3 600,1 600,900,576,400个节点的无线传感器网络进行仿真实验.结果表明:与随机型和经典MDS算法相比,所提出的算法在保持高的定位精度的前提下,大大降低了时间复杂度.算法被应用于智能超市导购系统,21辆购物车的平均定位误差为0.158 5m.

Most of the existing localization algorithms for large scale wireless sensor networks （WSN） have high complexities in time. To solve this problem, a fast visualization mapping from pairwise proximity matrix between nodes to corresponding coordinates was realized in WSN. Specifically, fast mapping algorithm procedure was introduced at first, and the reference nodes Served as the pivot points. And choosing the longest diagonal was to be used for pivot lines. Thus, the transformation from relative coordinates to absolute ones was avoided. In order to improve the localization accuracy, the output of FastMap algorithm was sent to initialize MDS （multi-dimensional scaling）. Area location of 600 mX 600 m was set in MATLAB. Using the wireless signal attenuation model was to produce the virtual measuring points, and carried out the simulation experiment of the wireless sensor network which respectively contained 3 600, 1 600, 900, 576 and 400 nodes. The results show that the proposed algorithm not only keeps high positional accuracy, but also reduces the time complexity. This algorithm was used in smart supermarket guiding system. The average location errors of 21 shopping carts were 0. 158 5 m.