Optimal Sensor Scheduling Against Deception Attacks Under Random Access Protocol

Abstract

State estimation from multiple remote wireless sensors requires scheduling protocols to orchestrate the limited bandwidth and prevent data collisions. A critical aspect overlooked in state-of-the-art protocols is handling the exposure of the wireless channel to cyber attacks. We investigate how, under a random access protocol, optimal sensor scheduling strategies can mitigate the detrimental effects of deception attacks. To this purpose, we quantify the contribution of each sensor to the overall estimation performance and formulate a corresponding scheduling metric. We then model the strategic interaction between the attacker and the defender as a zero-sum game and analytically derive its Nash equilibrium. We further show that the proposed optimal scheduling strategy is flexible enough to incorporate sensor power constraints. Numerical simulations validate the theoretical results and demonstrate the effectiveness of the proposed scheduling strategies in enhancing state estimation resilience against deception attacks.