Security Event-Trigger Mechanism-based Diffusion Kalman Filter for Wireless Sensor Networks under Cyber-Attack

This paper studies the problem of state estimation in wireless sensor networks affected by false data injection attack. An event-triggered mechanism-based diffusion Kalman filter algorithm is proposed to solve this problem. Firstly, an event-triggered mechanism based on saturation function is designed to reduce the sampling rate of nodes and clipping abnormal measurements. To maintain the stability of node estimation and prevent the diffusion of contaminated data between nodes, a data transmission mechanism based on local outlier factor is designed to control the data exchange between nodes. Simulation results show that the designed algorithm improves the estimation performance in the case of false data injection attack, the communication rate between nodes and the transmission of contaminated data are reduced.