Age of Information-Aware Scheduling for Dynamic Wireless Body Area Networks

Wireless body area network (WBAN) can be applied to a variety of scenarios, such as physical training, patient care, and health monitoring for the aged. To prolong the lifetime of WBANs and respond to an emergency, energy efficiency and age of information (AOI) become two most important performance indicators. The dynamic characteristic of WBANs makes the link quality of wireless communication vary intensely, which brings challenges to the reliability of data transmission. Conventional scheduling policies do not take into account the AOI and that the life time of the data packets from some sensor nodes may exceed the time limit, which are energy-inefficient and may cause an accident when unforeseen emergency happens. As a remedy, first, in order to fulfill the channel prediction, we adopt a deep learning method based on neural basis expansion analysis for interpretable time series (NBEATS). The transmit power according to channel prediction results is adjusted, to improve the energy efficiency of data transmission. Then, we propose an AOI-aware scheduling (AOI-AS) strategy which takes into account energy efficiency and data freshness. Simulation experiments show that our proposed scheduling strategy can reduce the AOI by 7% and the average energy consumption by 6% compared with the stochastic scheduling (SS) mechanism.