基于改进粒子群优化PID的双补偿时钟同步算法

Dual compensation clock synchronization algorithm based on improved particle swarm optimization PID

在无线传感网络系统中,主节点和从节点存在大量实时传输的数据,为了保证数据传输的准确性,需要传感网络上全部节点的时钟保持同步。其中,时钟偏差和时钟漂移的精确测量是实现主从时钟同步的关键。针对实际工作环境中出现的时钟漂移和时钟偏差,提出了一种基于改进粒子群算法优化PID的双补偿时钟同步算法。利用卡尔曼滤波器对时钟偏差和时钟漂移进行同时估计,利用估计值对从节点时钟进行补偿和修正。在粒子群算法更新方程的基础上引入环境因子,使粒子适应时钟不断变化的动态环境。实验结果表明,在无线传感网络系统引入改进粒子群优化PID的双补偿时钟同步算法,减少了由于动态环境中传统粒子群陷入局部最优值的影响,增强了粒子群算法的全局寻优能力,提高了时钟同步的精度,消除了从节点时钟不稳定性对时钟同步的影响。

In the wireless sensor network system,there is a large amount of real-time data transmission between the master node and the slave node.In order to ensure the accuracy of data transmission,the clocks of all nodes on the sensor network should be synchronized.Among them,accurate measurement of clock deviation and clock drift is the key to realize masterslave clock synchronization.Aiming at the clock drift and clock deviation in the actual working environment,a dual-compensation clock synchronization algorithm based on the improved particle swarm algorithm to optimize PID is proposed.This algorithm uses the Kalman filter to simultaneously estimate the clock deviation and clock drift,and uses the estimated value to compensate and correct the slave node clock.On the basis of the update equation of the particle swarm algorithm,environmental factors are introduced to make the particles adapt to the dynamic environment of constantly changing clocks.Experiments show that the introduction of an improved dual-compensation clock synchronization algorithm for particle swarm optimization PID in wireless sensor network systems reduces the impact of traditional particle swarms falling into local optimal values in dynamic environments,and enhances the global optimization capability of particle swarm optimization,improve the accuracy of clock synchronization and eliminate the influence of clock instability from the node on clock synchronization.