基于信念重用的WSNs能量高效跟踪

针对无线传感器网络(WSNs)中目标跟踪性能与传感器能量消耗难以平衡问题,提出一种信念重用的WSNs能量高效跟踪算法。使用部分可观察马尔可夫决策过程(POMDPs)对动态不确定环境下的WSNs进行建模,将跟踪性能与能量消耗平衡优化问题转化为POMDPs最优值函数求解过程;采用最大报酬值启发式查找方法获得跟踪性能的逼近最优值;采用信念重用方法避免重复获取信念,有效降低传感器通信带来的能量消耗。实验结果表明:信念重用算法能够有效优化跟踪性能与能量消耗之间的平衡,达到以较低的能量消耗获得较高跟踪性能的目的。

Energy-efficient tracking in WSNs based on belief reusing

In order to solve the problem of the tradeoff between target tracking performance and sensors energy consumption in wireless sensor networks(WSNs),a novel algorithm using belief reusing for energy-efficient tracking in WSNs is proposed.WSNs is modeled under dynamic uncertainty environment using partially observable Markov decision processes(POMDPs),and the optimization of the tradeoff between tracking performance and energy consumption is transformed into solving the optimal function value of POMDPs.The maximum rewards heuristic search algorithm is used to approximate the optimal value with respect to tracking performance.A novel approach called the belief reusing(BR) is presented to avoid repeatedly acquiring belief states,this approach can effectively reduce sensors energy consumption during communication.The experimental results show that the proposed algorithm has its effectiveness in optimizing the tradeoff between tracking performance and energy consumption,so it can meet the requirement of high tracking performance with low energy consumption.