Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks

Body area network (BAN) is a key technology of the medical Internet of things for personal health monitoring. Integrated with edge computing, it realizes real-time monitoring of physiological data, emergency warning, and intelligent treatment and diagnosis. However, the quality of service (QoS) requirements of the computing tasks in BAN varie with the urgency of the sensing data. The existing resource allocation methods in edge computing network are difficult to efficiently and flexibly support dynamic QoS of multi-source heterogeneous tasks in BAN. A dynamic QoS-aware stochastic optimization problem on computation offloading decisions and edge computing resource allocation was studied. Firstly, considering the Markov nature of multi-source task priorities and channel state changes in BAN, the original stochastic optimization problem was transformed into an infinite horizon Markov decision process problem. Then, a multi-source task priority sequence for each BAN was constructed and an online decision-making method that integrated proximal policy optimization (PPO) was proposed for task offloading and computing resource allocation. The simulation results show that the proposed optimization scheme outperforms existing baseline methods, effectively meeting the dynamic priority requirements of tasks in BAN and reducing the energy consumption as well as the average delay required for task completion.