Distributed consensus algorithm for events detection in cyber-physical systems

In the harsh environmental conditions of cyber-physical systems (CPSs), the consensus problem seems to be one of the central topics that affect the performance of consensus-based applications, such as events detection, estimation, tracking, blockchain, etc. In this paper, we investigate the events detection based on consensus problem of CPS by means of compressed sensing (CS) for applications such as attack detection, industrial process monitoring, automatic alert system, and prediction for potentially dangerous events in CPS. The edge devices in a CPS are able to calculate a log-likelihood ratio (LLR) from local observation for one or more events via a consensus approach to iteratively optimize the consensus LLRs for the whole CPS system. The information-exchange topologies are considered as a collection of jointly connected networks and an iterative distributed consensus algorithm is proposed to optimize the LLRs to form a global optimal decision. Each active device in the CPS first detects the local region and obtains a local LLR, which then exchanges with its active neighbors. Compressed data collection is enforced by a reliable cluster partitioning scheme, which conserves sensing energy and prolongs network lifetime. Then the LLR estimations are improved iteratively until a global optimum is reached. The proposed distributed consensus algorithm can converge fast and hence improve the reliability with lower transmission burden and computation costs in CPS. Simulation results demonstrated the effectiveness of the proposed approach.