Abstract
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.
Highlights
Cyber-Physical-Systems (CPS) can provide a broad range of control for complex industrial systems in the Internet of things (IoT) environment through heterogeneous architectures of integrated sensors and devices [1]
For in-network processing techniques, such as estimation, detection, and tracking in CPS, a compressed sensing based consensus method is introduced for distributed detection, estimation, and tracking, which can guarantee the performance in hash environmental conditions such as random packet losses, asymmetry of the links, etc. [2], [3]
We propose a jointly connected network model, in which the continuous topology of CPS at different time t can be modeled with jointly connected graphs collection; the collaborative events detection can be formulated as a consensus optimization problem over the jointly connected networks, which can be solved as a 1-norm optimization problem [7], [8], [9], [16]
Summary
Cyber-Physical-Systems (CPS) can provide a broad range of control for complex industrial systems in the Internet of things (IoT) environment through heterogeneous architectures of integrated sensors and devices [1]. Compare with WSNs, the CPS can bring several advantages: self-organization, real-time information exchange, collaborative controlling, and reliable data consensus to events status [11]. Aiming at improving the reliability and reducing the communication burden in events detection through CPS [7], [16], [17], this paper focuses on robust events detection via a distributed consensus algorithm in CPS, in which the active CPS nodes can collaboratively detect events and seek to iteratively reach a global optimum. The rest of the paper is organized as follows: in Section II, a jointly connected network model is presented, and a distributed sparse events detection problem is formulated as a consensus optimization; in Section III, a collaborative consensus algorithm is proposed; experiment simulations are provided in Section IV to evaluate the effectiveness of the proposed algorithm; Section V concludes the paper
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