Collaborative Cloud-Edge Computing With WPT for Air-Ground Integrated IoMT Network: A URLLC Aware CAFL-Based Approach

The global aging process accelerates, making geriatric disease prevention and management urgent. Combining IoMT with wearable devices for health monitoring is effective, but IoMT task offloading has challenges like weak terminal processing, poor battery life, low - latency needs, and privacy protection difficulties. To address these challenges, this paper proposes a Cloud-edge Collaborative Air-ground Integrated IoMT Network (C2AI2N) that combines ground Base Stations, air-based Autonomous Aerial Vehicles (AAVs) and cloud servers to adapt to future IoMT scenarios and enhance the system capabilities. Edge servers are able to use Wireless Power Transfer (WPT) to provide energy to wearable devices. We then consider the Ultra-Reliable Low Latency Communication (URLLC) task constraints as well as power limitations, and formulate the problem of minimizing the overall system energy consumption. Next, we employ the Lyapunov optimization method to break down the problem into three sub-problems: 1) device-side task splitting; 2) task offloading as well as charging strategy; and 3) server-side resource allocation, which are addressed via Lagrange multiplier method, Clustered Asynchronous Federated Learning (CAFL) based rainbow Deep Q-Network (DQN) approach, and the coati optimization algorithm, respectively. Extensive simulations demonstrate that the proposed method outperforms other baselines. Specifically, the energy consumption of the proposed method can be reduced by over 30.37% compared to the baselines. In addition, the backlog of the URLLC virtual queue can be decreased by more than 59.27%, achieving superior URLLC satisfaction rates.