Deep Reinforcement Learning for Joint Sensor Scheduling and Power Allocation under DoS Attack

Abstract

In this paper, we focus on the problem of remote state estimation in wireless networked cyber-physical systems (CPS). Information from multiple sensors is transmitted to a central gateway over a wireless network with fewer channels than sensors. Channel and power allocation are performed jointly, in the presence of a denial of service (DoS) attack where one or more channels are jammed by the attacker through the transmission of spurious signals. The attack policy is unknown and the central gateway has the objective of minimizing state estimation error with maximum energy efficiency. Therefore, the problem involves a novel combination of discrete and continuous action spaces. In addition, the state and action spaces have high dimensionality and the channel states are not fully known to the defender. We propose a novel model-free and off-policy deep reinforcement learning algorithm to address the problem. The proposed algorithm shows promise in solving online complex CPS problems, outperforming some other existing benchmark algorithms.