Jamming Games in Underwater Sensor Networks with Reinforcement Learning

Jamming attacks that can further lead to denial of service attacks have thrown serious threats to underwater sensor networks (UWSNs). However, due to the narrow bandwidth of underwater acoustic signals and time variant propagation environments, jamming in UWSNs cannot be fully addressed by spread spectrum techniques, one type of widely-used antijamming methods in wireless networks for decades. In this work, we investigate jamming attacks in underwater sensor networks. More specifically, the interactions between the underwater sensors and jammers in UWSNs are formulated as an underwater jamming game, in which the players choose their transmit power levels to maximize their individual utilities based on the signal to interference plus noise ratio of the legal signals and transmission costs. The Nash equilibrium (NE) of a static jamming game is presented in a closed-form expression for the jamming scenario with known acoustic channel gains. For the dynamic and unknown underwater environments, we propose a reinforcement learning-based anti-jamming method for UWSNs, in which each sensor chooses its transmit power without knowing the channel gain of the jammers. Simulations are performed to evaluate the NE in the static jamming game in underwater sensor networks and to validate the efficacy of the proposed anti-jamming power control scheme against jamming in dynamic environments.