Optimal Transmission Scheduling for Remote State Estimation Under Active Eavesdropping-Based DoS Attacks

In this article, the optimal transmission scheduling problem for remote state estimation over signal-to-interference-plus-noise ratio-based network channel is studied, in the presence of an active attacker who is able to implement DoS attacks to jam the network based on the eavesdropping information. An intelligent sensor is used to send the local state estimates to a remote estimator, and by co-designing the power control and the scheduling decision such that the sensor can decide whether to transmit and what power to use for communication, a coupling transmission strategy is provided. To minimize the energy consumption and the known estimation error covariance (EEC) of the remote estimator for the sensor, while maximizing the unknown eavesdropping EEC, the co-design scheduling issue is modeled as a modified Markov decision process by applying a Monte Carlo method based on a belief state probability distribution. A Clipped HetUpSoft Q-learning algorithm is designed to achieve the approximate optimal strategy online. Finally, simulation results are provided to validate the effectiveness of the developed approaches.