Event-based distributed filtering against deception attacks for sensor networks with quantization effect

• Published in: ISA transactions Vol. 126; pp. 338 - 351
• Main Authors: Yuan, Huanhuan, Guo, Yongzhen, Xia, Yuanqing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2022
• Subjects: Deception attack; Distributed filtering; Event-triggered mechanism; Quantization; Sensor networks; Sensor networks; Quantization; Event-triggered mechanism; Deception attack; Distributed filtering
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2021.08.009

This paper considers a distributed secure filtering problem for a category of time-varying system subject to uncertainty and model-reality mismatch, two-stage deception attacks and bandwidth limitation. Both deception attacks between sensor and corresponding estimator and among estimators appear randomly. To alleviate communication burden, a quantization strategy is introduced before transmitting measurement and estimation signals. An event-triggered mechanism is employed for each estimator node thus only necessary data are transmitted to its neighbour sensors when a setting event occurs. The desired target of the problem to be handled is to devise a series of time-varying filters such that the H∞ secure performance is guaranteed against random deception attacks over a finite time horizon. Sufficient conditions ensuring the existence of time-varying filters under effect of complex factors are derived, where filter gains are obtained by finding the solution of a sequence of recursive matrix inequalities online. Simulation results in both numerical example and industrial continuous-stirred tank reactor system are given to show the validity of the presented methodology. •A novel distributed filtering system in which two-stage deception attacks exist in the communication channels is established.•The quantization strategy and event-triggered protocol are exploited for bandwidth and energy saving filtering system.•For the established secure performance, filter gains are derived by obtaining the solution of matrix inequalities for online application.