无线传感器网络异构数据融合模型优化研究

Study on Optimization of Heterogeneous Data Fusion Model in Wireless Sensor Network

针对无线传感网中存在的能耗和网络的安全性等问题,从数据融合的角度出发,提出一种无线传感器网络数据融合模型。模型引入信息熵来实现一种新的信任度的计算方式,配合对异常数据的监测及过滤方式建立信任机制,通过信任机制来提高无线传感器网络的安全性和可靠性;采用混合簇结构来减少网络时延,降低系统能耗;根据节点的剩余能量、节点到基站的距离以及信任度等因素来完成对簇头的阶段性重选,通过对节点的阶段性重选达到负载平衡、延长网络生命周期的目的;为解决无迹卡尔曼滤波在强非线性系统中估计效果差和滤波发散的问题,该算法将无迹卡尔曼滤波算法叠加使用,同时在第一次使用无迹卡尔曼滤波时在观测噪声协方差矩阵中引入衰减因子。算法的仿真结果表明,相比于传统算法,所提算法提高了滤波结果的精度。

Aiming at the problems of energy consumption and network security in wireless sensor network,this paper proposes a data fusion model of wireless sensor network from the point of view of data fusion.The model introduces information entropy to realize a new way of calculating trust degree,completes the establishment of trust mechanism with monitoring and filtering of abnormal data,and improves the security and reliability of wireless sensor network through trust mechanism.In order to solve the problem of poor estimation effect and filtering divergence in strong nonlinear systems,the unscented Kalman filter algorithm is super imposed and the attenuation factor is introduced in the observation noise covariance matrix when the unscented Kalman filter is used for the first time.The simulation results show that the proposed algorithm improves the accuracy of filtering results compared with the traditional algorithm.