Communication-Efficient Secure Distributed Estimation With Noisy Measurement Against FDI Attack

We study the problem of distributed estimation over wireless sensor networks (WSNs), where measurement noises and false data injection (FDI) attacks are considered. We propose the malicious node filtrating based secure diffusion bias compensated recursive least squares (MFS-dBCRLS) algorithm. To resist input noises, the BCRLS algorithm is introduced in local adaptation. To reduce the effect of network adversaries, a suitable time gating and a two-stage malicious node filtrating method are presented. Based on instantaneous state detection results in the first temporary filtrating stage, we permanently filtrate malicious neighbors in the second stage by proposing a new threshold test to decide final state. Besides, the selection range of the threshold is detailedly analyzed. Simulation results reveal communication-efficiency and robustness of our proposed method compared with some state-of-the-art algorithms under different FDI attacks.