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Reinforcement Learning Based Anti-Jamming Schedule in Cyber-Physical Systems
- 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.
- Subjects :
- 0209 industrial biotechnology
Schedule
Computer science
business.industry
020208 electrical & electronic engineering
Real-time computing
Cyber-physical system
02 engineering and technology
020901 industrial engineering & automation
Cognitive radio
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
Path loss
Wireless
Reinforcement learning
business
Computer Science::Information Theory
Communication channel
Data transmission
Subjects
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