基于距离优化和改进粒子群的节点定位算法

Node location algorithm based on distance optimization and improved particle swarm optimization

为提高无线传感器网络中DV-Hop算法的定位精度,提出基于距离优化和改进粒子群的DV-Hop定位算法(DPDV-Hop)。距离估计阶段,利用单跳平均误差修正平均跳距,接收多锚节点的平均跳距估算节点间距离,使估算距离得以优化。位置估计阶段,采用改进的粒子群优化算法(I-PSO)替代最大似然估计法,I-PSO利用粒子成功率PS自适应调节惯性权重因子,产生变异算子提升种群多样性。仿真结果表明,DPDV-Hop算法较其它算法的定位精度有所提升。

To improve the accuracy of DV-Hop algorithm in wireless sensor networks,a DV-Hop positioning algorithm based on distance optimization and improvement particle swarm optimization（DPDV-Hop）was proposed.In the distance estimating stage,the average hop-size was corrected based on the single hop average error,and the distance between nodes was estimated through the average hop-size between the multi-anchor nodes.The estimated distance was the optimized.In the location estimating stage,the maximum likelihood estimation method was replaced using the improved particle swarm optimization algorithm（I-PSO）.The particle success rate PS was used to adaptively adjust the inertia weighting factor to generate mutation operator to enhance the population diversity.The simulation results show that the positioning accuracy of DPDV-Hop algorithm is prior to other algorithms.