Reinforcement Learning Based Anti-Jamming Schedule in Cyber-Physical Systems. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Gan, Ruimeng; Xiao, Yue; Shao, Jinliang; Zhang, Heng; Xing Zheng, Wei
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 2501
• Page End: 2506
• Publication Date: 2020
• Subjects: Cyber-physical systems (CPSs) -- reinforcement learning -- cognitive radio -- softmax method; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.221
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper, the security issue of cyber-physical systems is investigated, where the observation data is transmitted from a sensor to an estimator through wireless channels disturbed by an attacker. The failure of this data transmission occurs, when the sensor accesses the channel that happens to be attacked by the jammer. Since the system performance measured by the estimation error depends on whether the data transmission is a success, the problem of selecting the channel to alleviate the attack effect is studied. Moreover, the state of each channel is time-variant due to various factors, such as path loss and shadowing. Motivated by energy conservation, the problem of selecting the channel with the best state is also considered. With the help of cognitive radio technique, the sensor has the ability of selecting a sequence of channels dynamically. Based on this, the problem of selecting the channel is resolved by means of reinforcement learning to jointly avoid the attack and enjoy the channel with the best state. A corresponding algorithm is presented to obtain the sequence of channels for the sensor, and its effectiveness is proved analytically. Numerical simulations further verify the derived results.