动态传感器网络中距离相关的蒙特卡洛定位算法

动态无线传感器网络的许多应用中定位技术具有至关重要的作用,在这种网络中部署区域内的全部节点不断运动.针对动态传感器网络中的定位问题,在蒙特卡洛方法的基础上提出了一种新的定位算法-距离相关蒙特卡洛(DRMCL).该算法通过利用节点的RSSI测距性能减小预测时未知节点可能存在的交叠区域,提高定位精度.仿真结果表明,对比蒙特卡洛和凸规划等现有的移动传感器网络定位算法,提出的DRMCL可以明显提高定位精度,在运动的过程中,定位精度能够达到20%以下.

Localization Algorithm Based on Sector Scan for Mobile Wireless Sensor Networks

Localization is crucial to many applications in wireless sensor networks (WSN). In some applications, all nodes are constantly moving in the network. The current algorithms do not be good for address the problem of enabling nodes of mobile wireless sensor networks (MWSN) to determine their localization. To solve this problem, a new localization algorithm based on the idea of Monte Carlo for MWSN is proposed-distance relationship MCL(DRMCL). By using RSSI, this algorithm minimizes the overlapping areas determining the localization of the unknown nodes. To do so, the localization accuracy can be improved. The simulation shows that DRMCL algorithm can obviously improve the localization accuracy compared to Convex and MCL, and the accuracy of the DRMCL is lower than 20%.