Reinforcement Learning-Based Sensor Access Control for WBANs

Abstract

Wireless body area networks that support fast-growing healthcare applications have to control the access of the sensors in the dynamic network and channel states. In this paper, we propose a sensor access control scheme based on reinforcement learning that enables the coordinator to choose the access time and transmit power of the sensors based on the state that consists of the signal-to-interference plus noise ratio, the transmission priority, the battery level, and the transmission delay of the sensors. This scheme is proved to improve the quality-of-service, save the energy consumption of the sensors, and enhance the transmission reliability. The simulation results show that this scheme reduces the bit error rate, saves the energy consumption, decreases the transmission delay, and increases the overall utility of the sensors compared with the benchmark schemes.