无线传感器网络节点自适应惯性权重定位算法

在无线传感器网络定位算法中,为了降低定位误差,提高定位精度,提出一种结合DV-Hop算法和改进粒子群算法的,基于自适应惯性权重的优化定位算法。首先根据DV-Hop算法估算未知节点与信标节点的距离。然后采用改进的粒子群算法做后期优化。根据每次迭代后粒子位置与全局最优位置的距离,对粒子的惯性权重进行动态调整,使其具有动态自适应性。并且利用进化度作为搜索中止条件,加快算法的收敛速度。通过仿真说明,相较于DV-Hop算法和基于已有改进粒子群优化的DV-Hop算法,自适应惯性权重定位算法可以降低平均定位误差,有效地提高了无线传感器网络中节点的定位精度。

An adaptive weight positioning method in wireless sensor network

In order to reduce the positioning error and improve the localization accuracy in Wireless Sensor Network(WSN),an adaptive weight positioning method wcis introduced,which was based on DV-Hop algorithm and improved Particle Swarm Optimization.Improved Particle Swarm Optimization for late optimization after knowing the distance between the unknown nodes and anchor nodes estimated by DV-Hop is used.In this improved optimization,each particle changed its own inertia weight dynamically in each iteration process based on the distance between the particle and the current optimal position and a parameter is added to control the ceasing of iteration,improving the speed.Simulation results in the same environment show the new algorithm have lower average localization error than DV-Hop algorithm and the algorithm optimized by changed Particle Swarm Optimization,it can improve the localization accuracy effectively in WSN.