Path selection with Nash Q‐learning for remote state estimation over multihop relay network. (4th March 2020)

• Record Type: Journal Article
• Authors: Zhang, Yunlin; Gan, Ruimeng; Shao, Jinliang; Zhang, Heng; Cheng, Yuhua
• Other Names: Chen Jiming guestEditor.; Gupta Vijay guestEditor.; Quevedo Daniel E. guestEditor.; Tesi Pietro guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 30:Number 11(2020)
• Page Start: 4331
• Page End: 4344
• Publication Date: 2020-03-04
• Subjects: denial‐of‐service attack -- game theory -- multihop relay network -- remote state estimation; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.4945
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: In this article, the security issue of remote state estimation is investigated for multihop relay networks interrupted by an attacker launching denial‐of‐service attacks. Since the presence of the relay enriches the communication topology, there might exist several paths connecting the sensor and the estimator, consisting of the corresponding channels. Thus, it is reasonable for the sensor to select the path with a lower dropout rate to enhance the system performance measured by the estimation error, due to the dropout rate changing with the channel. However, as an adversary, the objective of the jammer is to deteriorate the corresponding performance through launching attack on the communication path selectively. For addressing the problem on the behalf of both of the sensor and the jammer, we first formulate this problem as a two‐player zero‐sum stochastic game model, and then present a Nash Q‐learning algorithm to explore the equilibrium point for both players, under the assumption that both of them are rational players. Furthermore, the existence of equilibrium point for this problem is proved analytically. Moreover, a more general case of the channel attack, under which the jammer can attack any channels among this network, is considered. Finally, numerical results are presented to verify the effectiveness of the algorithm proposed and the theorem results.