Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN

Both real-time physiological data transmission and reduced energy consumption are critical to wireless body area network (WBAN). A joint optimization strategy for offloading and scheduling was proposed to minimize the weighted sum of age of information (AoI) and energy consumption by determining whether data should be processed at sensor nodes or the Sink. To handle the strong coupling between offloading and scheduling decisions, a two-layer Markov decision process (MDP) was used to approximate the optimal solution. A deep reinforcement learning (DRL) approach was introduced to address the dimensionality issue. Simulations show that the DRL strategy performs comparably to the MDP under various weight factors and frame lengths. Furthermore, as the number of sensor nodes increases, the DRL strategy reduces the weighted sum by 3.58% and 24.9% compared to RRG and EG strategies, respectively, and converges twice as fast as the MDP.