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.
To combat the jamming attacks, Chen et al. REF developed a reinforcement learning-based power control scheme for in-body sensors.
Reinforcement Learning Based Power Control for In-Body Sensors in WBANs Against Jamming
IEEE Access
IEEE Access
Computer Science