Tuatara: Location-Driven Power-Adaptive Communication for Wireless Body Area Networks

Radio links in Wireless Body Area Networks (WBANs) suffer from both short-term and long-term variations due to the dynamic network topology and frequent blockage caused by body movements, making it challenging to achieve reliable, energy-efficient and real-time data communication. Through experiments with TelosB motes, we observe a strong positive relationship between the channel quality and the position of the sending node relative to the gateway. Motivated by this observation, we design Tuatara, a novel power-aware communication protocol that allows each sensor node to dynamically adjust its transmission power based on the channel status inferred from its instant position, aiming to save energy, reduce interference, and improve communication reliability. Based on a probabilistic model, power level selection is converted to calculate the optimal probability of selecting each power level at a given position, with the objective of minimizing the transmission cost. A reinforcement learning scheme is designed to adaptively update the power level selection probabilities, making Tuatara self-adaptable to changes in the signal propagation environment. Experimental results demonstrate that Tuatara outperforms the state-of-the-art protocols in various scenarios, with performance close to that of the optimal power selection solution even in scenarios where the packet rate is very low.