Computing Offloading Strategy for Internet of Medical Things in Space-Air-Ground Integrated Network

In remote areas or emergency rescue scenarios, the lack of infrastructural network facilities, and the limited energy and computational resources of Internet of Medical Things (IoMT), IoMT devices pose a challenge for medical devices to handle a large number of computationally intensive tasks. To solve this problem, a computation offloading strategy that integrates Space-Air-Ground Integrated Network (SAGIN) and Mobile Edge Computing (MEC) technologies is proposed to achieve low-latency and energy-efficient edge healthcare. Unmanned aerial vehicle (UAV) and Low Earth Orbit (LEO) satellites are used to enhance network coverage, and the problem of maximizing system energy efficiency is constructed through joint UAV location deployment and offloading decisions. To optimize the system energy efficiency, an energy-efficient UAV 2D position deployment algorithm (PD-UAV 2D) based on medical IoMT device grouping is proposed. The algorithm determines the association relationship between UAVs and IoMT devices through the IoMT device grouping mapping strategy and dynamically adjusts the coverage radius of UAVs based on the idea of a minimum coverage circle to achieve energy- efficient UAV deployment. In addition, considering the urgency of the task and the residual energy state of the medical devices, a computational offloading mechanism with task priority is designed to provide offloading services for the medical devices using UAVs and LEOs. Finally, an adaptive particle swarm (APSO) algorithm is used to solve the problem. Simulations show that the proposed scheme can significantly improve the system's eneray efficiency.