基于改进自适应聚类的无线传感器目标跟踪算法

Wireless Sensor Target Tracking Algorithm Based on Improved Adaptive Clustering

针对无线传感器网络目标跟踪中跟踪精度与传感器节能问题进行研究,提出基于预测的簇头自适应聚类算法,该算法采用动态聚类算法,通过考虑传感器节点剩余能量、节点到汇聚的距离和节点到运动目标的距离3个参数,最大限度地减少簇头与基站间的远程通信,节约网络能量,延长网络寿命,降低目标漏失概率,加入预测机制,采用线性预测的方法预测跟踪目标的下一个位置,继而根据得到的预测误差,改变集群的大小和形状,减少参与跟踪过程的传感器节点数量,节约网络能量,保持合理的跟踪精度。仿真实验对网络能源消耗、跟踪误差和网络寿命进行分析,实验结果表明,与现有算法相比,该算法在考虑随机运动速度的情况下仍能准确地跟踪目标,且能耗更低,大大延长了网络的生命周期,从而表明所提算法的有效性和可行性。

In this paper,the problem of tracking accuracy and sensor energy saving in wireless sensor network target tracking is studied,and a cluster head adaptive clustering algorithm based on prediction is proposed. Firstly,the dynamic clustering algorithm is adopted in the algorithm. By considering the residual energy of sensor nodes,the distance from nodes to convergence and the distance from nodes to moving targets,the algorithm can minimize the difference between cluster heads and base stations On this basis,a prediction mechanism is added to predict the next position of the tracking target,and then the size and shape of the cluster are changed according to the prediction error,so as to reduce the number of sensor nodes involved in the tracking process and save network energy,keep reasonable tracking accuracy. The simulation results show that compared with the existing algorithms,the proposed algorithm can accurately track the target with lower energy consumption and greatly prolong the network life cycle,which shows the effectiveness and feasibility of the proposed algorithm.