Abstract
Radio links in wireless body area networks (WBANs) commonly experience highly time-varying attenuation due to the dynamic network topology and frequent occlusions caused by body movements, making it challenging to design a reliable, energy-efficient, and real-time communication protocol for WBANs. In this article, we present Chimp, a learning-based power-aware communication protocol in which each sending node can self-learn the channel quality and choose the best transmission power level to reduce energy consumption and interference range while still guaranteeing high communication reliability. Chimp is designed based on learning automata that uses only the acknowledgment packets and motion data from a local gyroscope sensor to infer the real-time channel status. We design a new cost function that takes into account the energy consumption, communication reliability and interference and develop a new learning function that can guarantee to select the optimal transmission power level to minimize the cost function for any given channel quality. For highly dynamic postures such as walking and running, we exploit the correlation between channel quality and motion data generated by a gyroscope sensor to fastly estimate channel quality, eliminating the need to use expensive channel sampling procedures. We evaluate the performance of Chimp through experiments using TelosB motes equipped with the MPU-9250 motion sensor chip and compare it with the state-of-the-art protocols in different body postures. Experimental results demonstrate that Chimp outperforms existing schemes and works efficiently in most common body postures. In high-date-rate scenarios, it achieves almost the same performance as the optimal power assignment scheme in which the optimal power level for each transmission is calculated based on the collected channel measurements in an off-line manner.