Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks

Sensors

Jiwei Wen¹  Linbo Xie¹  Fengzeng Zhu¹  Xu Liu¹  Li Peng¹ 

[1] Engineering Research Center of Internet of Things Applied Technology, Jiangnan University, Ministry of Education, Wuxi 214122, Jiangsu, China;

关键词: wireless sensor network;    switching topology;    deception attack;    distributed filtering;    linear matrix inequality;   

DOI  :  10.3390/s20071948

来源: DOAJ

【 摘 要 】

This paper is concerned with the distributed full- and reduced-order $l_2$ - $l_{\infty }$ state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed which uses homogeneous Markov chain to reflect the change of filtering networks communication modes. Then, the sector-bound deception attacks among the communication channels are taken into consideration, which could better characterize the filtering network communication security. Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks. Furthermore, through an adjustable parameter E, we can obtain full- and reduced-order $l_2$ - $l_{\infty }$ state estimator over sensor networks, respectively. Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem. The purpose of solving the problem is to design a distributed full- and reduced-order filter such that, in the presence of deception attacks, stochastic external interference and switching topologies, the resulting filtering dynamic system is exponentially mean-square stable with prescribed $l_2$ - $l_{\infty }$ performance index. Finally, a simulation example is provided to show the effectiveness and flexibility of the designed approach.

【 授权许可】

Unknown