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Path selection with Nash Q‐learning for remote state estimation over multihop relay network
Author(s) -
Zhang Yunlin,
Gan Ruimeng,
Shao Jinliang,
Zhang Heng,
Cheng Yuhua
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4945
Subject(s) - relay , denial of service attack , computer science , path (computing) , nash equilibrium , mathematical optimization , estimator , dropout (neural networks) , channel (broadcasting) , wireless sensor network , topology (electrical circuits) , computer network , mathematics , power (physics) , statistics , physics , the internet , quantum mechanics , combinatorics , machine learning , world wide web
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.