基于时序蒙特卡洛的WSN节点定位算法

针对无线传感器网络（WSN）中的移动节点定位问题，提出了一种将反馈时间序列与蒙特卡洛相结合的定位算法TSMCL（Feedback Time Series-Based Monte Carlo）。该算法基于目标节点1跳范围内的邻居锚节点（至少3个）反馈信号的先后顺序，构建了节点可能的初始采样区域R1，并以区域R1与蒙特卡洛采样区域R2的重叠区作为新的采样区域R，以进一步缩小采样范围、提高采样效率。仿真结果表明：与蒙特卡洛定位算法相比，提出的TSMCL算法能够减少约38%的定位误差，尤其当节点移动速度较高时，算法的收敛速度也得到了显著提升。

Node Localization Algorithm for WSN Based on Time Sequence Monte Carlo

A new node localization algorithm is proposed for WSN(Wireless Sensor Network),which combines the feedback time series with the Monte Carlo localization,named as TSMCL. The possible initial sampling region of the target node,R1 is determined by the feedback time order from no less than three anchor nodes,which are the one-hop neighbors of the target node. In order to shrink the sampling region and improve the sampling efficiency, the final sampling region R is limited to the overlapped area of R1 and Monte Carlo sampling region(R2). Simulation results demonstrate that compared with Monte Carlo localization,the proposed TSMCL algorithm reduce the posi?tioning error up to 38%or so,and meanwhile,the convergence speed is improved significantly,especially for high mobility.