动态WSN中基于改进的蒙特卡罗盒子定位算法

Improved Monte Carlo Boxed Localization for Mobile Wireless Sensor Networks

针对蒙特卡罗盒子定位算法在动态WSN定位方面所存在的计算量大,精确度不高以及耗时等缺点。提出一种量子粒子群算法优化蒙特卡罗盒子的定位算法(QPSO-MCB)。该算法不仅减小了解空间的搜索时间,同时提高了采样效率,有效改善了蒙特卡罗盒子定位算法的高误差和低效率。仿真结果表明,与蒙特卡罗盒子算法相比,平均采样效率有了明显提高,特别当移动速度增加的情况下,定位误差反而下降。

A new node localization algorithm QPSO -MCB is proposed for mobile WSNs to reduce the time in the search space and improve the sampling efficiency of the Monte Carlo localization algorithm. Simulation results demonstrate that compared with the Monte Carlo algorithm boxes, the proposed QPSO - MCB algorithm improve the sampling speed. Especially with the moving speed increasing positioning error is decreased.