Distributed Data-Selective DLMS Estimation Under Channel Attacks

With the continuous development of big data processing technology, data selection algorithms gradually attract the attention of researchers. In this paper, a distributed data-selection diffusion least mean square (DLMS) algorithm, which can improve the estimation accuracy of traditional data selection algorithms and can also censor data packets that do not bring enough innovation in wireless sensor networks, is proposed to censor the valid data in distributed iterative updates. And the adaption-then-combination strategy of the proposed algorithm is obtained. Meanwhile, in the distributed estimation system, the channel attacks are considered. When the network is under channel attacks, an adaptive credibility weight matrix is designed to improve the robustness of the distributed data-selection DLMS algorithm. We analyze the proposed algorithms in mean and mean-square performance. A series of simulations are carried out to demonstrate the effectiveness of the proposed algorithms. Moreover, it can be examined that through the comparison between the Metropolis weight strategy and the credibility weight strategy, the proposed credibility weight strategy is more robust in the face of channel attacks.