ABSTRACT Wireless body area networks (WBANs) have to address jamming attacks to support healthcare applications. In this paper, we present a reinforcement learning-based power control scheme for the communication between the in-body sensors and the WBAN coordinator to resist jamming attacks. This scheme applies Q-learning to guide the coordinator to achieve an optimal power control strategy without being aware of the in-body sensor's transmission parameters and the WBAN model of the other sensors in the dynamic anti-jamming transmission. In addition, a transfer learning method is adopted to accelerate the learning speed. Stackelberg equilibria and their existence conditions are deduced in a single time slot to upper bound the performance of the learning-based sensor power control scheme. Simulation results show that the proposed scheme can efficiently increase the utilities and decrease the transmission energy consumptions for the in-body sensors and the WBAN coordinator, and simultaneously reduce the attack possibility of the jammer compared with a standard Q-learning-based sensor power control scheme. INDEX TERMS Wireless body area networks, power control, in-body sensors, jamming attacks, game theory.
Our previous work in REF adopts RL to help the sensors and the coordinator save energy and resist jamming attacks.
Reinforcement Learning Based Power Control for In-Body Sensors in WBANs Against Jamming
IEEE Access
IEEE Access
Computer Science