Reinforcement-Learning-Based Relay Mobility and Power Allocation for Underwater Sensor Networks Against Jamming

Underwater sensor networks (UWSNs) are vulnerable to jamming attacks due to the narrow frequency bandwidth and the fast fading channels. In this paper, we propose a reinforcement learning (RL)-based antijamming relay scheme for UWSNs that enables an underwater relay to decide whether to leave the heavily jammed location and choose the relay power based on the state that consists of the bit error rate of the previous transmission, the previous relay power, the current transmit power of the sensor, and the jamming power measured by the relay node. We also propose a deep-RL-based relay scheme to further improve the relay performance for the node that supports deep learning computation. We discuss the computational complexity of the deep-RL-based relay scheme and provide the relay performance bound regarding the bit error rate, energy consumption, and utility of the relay node. Experiments taken in a nonanechoic pool with underwater transducers against smart jamming attacks verify the analysis results. According to the experimental results, the proposed relay scheme can improve the relay performance compared with the benchmark underwater relay schemes.