基于自然选择粒子群的时钟同步算法

摘要

In order to improve the clock synchronization accuracy of wireless sensor network (WSN) and avoid the synchronization error caused by the unreliable data and the change of the network topology structure in the synchronization process,a simulation model of particle swarm optimization (PSO) algorithm based on natural selection was established.Firstly,using Kalman filtering algorithm to locally filter the measurement data of multiple sensors,the redundant information in the bottom of the measurement data could be removed so that the accuracy of measurement data is improved.Then,particle swarm optimization algorithm based on natural selection was used to set up a data fusion model and calculate the optimal fused estimation.Finally,the simulation results of the fusion model of PSO algorithm based on natural selection show that the fusion algorithm can effectively realize the fusion of five node clock in the networks,and improve the fusion accuracy of time offset and frequency offset of node crystal oscillating,producing a virtual clock of high stability.The concentration ranges of fusion accuracy of time offset and frequency offset are 10-5 and 10-7,respectively.Compared with traditionaladaptive fusion algorithms,the concentration ranges of fusion accuracy of time offset and frequency offset are improved by one order of magnitude.%为了提高无线传感器网络的时钟同步精度,避免因不可靠数据和网络拓扑结构变化在同步过程中产生的同步误差,建立了基于自然选择粒子群融合算法仿真模型.首先,采用卡尔曼滤波算法,将多个传感器的测量数据进行局部滤波处理,去除测量数据中最底层的冗余信息,提高测量数据的准确性;其次,采用自然选择粒子群算法建立数据融合模型,计算网络的最优融合估计;最后,对自然选择粒子群融合算法模型进行仿真,结果表明:该算法的融合模型不仅能够对网络中的5个节点时钟进行有效融合,而且可以提高节点晶振的时偏和频偏的融合精度,形成一个具有较高稳定度的虚拟时钟,其时偏和频偏的融合精度主要集中范围分別为10-5和10-7,与传统自适应融合算法相比较,它的时偏和频偏融合精度提高了1个数量级.