基于蒙特卡洛方法的移动传感网节点定位优化算法

Optimized Localization Scheme for Mobile Wireless Sensor Networks Based on Monte Carlo Method

无线传感器网络正在被应用到各种各样的监测环境中,在这些应用场景中,传感器节点的位置信息大都是至关重要的。目前对传感器节点定位方面的研究大都只针对静态WSN的情况,对于移动WSN节点定位的研究仍然十分有限。该文提出了移动WSN中节点间互相优化定位的新思路,通过判断式筛选出定位精度高的节点,并协助其他节点进行定位条件的优化。所提出的算法TSBMCL通过更精确的裁剪待定位节点的蒙特卡洛盒,并增加节点的粒子滤波条件来实现节点的精确定位。大规模的仿真结果表明,该算法可精确的锁定节点位置区域,高效的采样得到节点的位置样本,相比于传统的移动WSN蒙特卡洛定位方法,大大提高了节点的定位精度。

Wireless sensor networks have been applied to various surveillance circumstances where sensor node＇s location information is critical. While the present research on localization mainly focuses on static WSN, the achievements on mobile WSN＇s localization are still very limited. In this paper, by selecting nodes of better localizing situations, a novel scheme is proposed that nodes could mutually optimize localization conditions to reach a better effect. The algorithm of TSBMCL grants nodes better localization results by cutting more precise Monte Carlo Box and adding more particle filtering requirements. Large scale simulations show that this algorithm can lock nodes＇ location area more precisely and improve the sampling efficiency. Compared with classic Monte Carlo localizing schemes for mobile WSN,TSBMCL improves the localizing precision greatly.