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Reinforcement Learning Based Anti-Jamming Schedule in Cyber-Physical Systems

Authors :
Wei Xing Zheng
Ruimeng Gan
Jinliang Shao
Yue Xiao
Heng Zhang
Source :
IFAC-PapersOnLine. 53:2501-2506
Publication Year :
2020
Publisher :
Elsevier BV, 2020.

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.

Details

ISSN :
24058963
Volume :
53
Database :
OpenAIRE
Journal :
IFAC-PapersOnLine
Accession number :
edsair.doi...........03da9cae73f02b66d38d14a88d83a9de
Full Text :
https://doi.org/10.1016/j.ifacol.2020.12.221