This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft’s loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

本文介绍了一种用于无线传感器网络（WSN）的新事件触发卡尔曼共识滤波器（ET-KCF）算法的设计。该算法基于信息新鲜度，信息新鲜度被计算为采样数据的信息寿命（AoI）。该算法融合了传统的事件触发机制，信息新鲜度计算方法和卡尔曼共识过滤器（KCF）算法，可以更有效地估算飞机中的污染物浓度。所提出的方法还考虑了数据包丢失和飞机通信路径丢失对WSN的影响，并提出了一种基于AoI新鲜度的ET-KCF算法阈值选择方法，该方法将数据包AoI与最小平均AoI进行比较系统的。由于减少了过期信息的传输，因此该方法可以明显降低能耗。最后，利用李雅普诺夫稳定性理论和矩阵理论证明了算法的收敛性。仿真结果表明，与现有的KCF相比，该算法具有更好的容错能力，并且功耗更低。