Energy-saving Service Offloading for the Internet of Medical Things Using Deep Reinforcement Learning

Abstract

As a critical branch of the Internet of Things (IoT) in the medicine industry, the Internet of Medical Things (IoMT) significantly improves the quality of healthcare due to its real-time monitoring and low medical cost. Benefiting from edge and cloud computing, IoMT is provided with more computing and storage resources near the terminal to meet the low-delay requirements of computation-intensive services. However, the service offloading from health monitoring units (HMUs) to edge servers generates additional energy consumption. Fortunately, artificial intelligence (AI), which has developed rapidly in recent years, has proved effective in some resource allocation applications. Taking both energy consumption and delay into account, we propose an energy-aware service offloading algorithm under an end-edge-cloud collaborative IoMT system with Asynchronous Advantage Actor-critic (A3C), named ECAC. Technically, ECAC uses the structural similarity between the natural distributed IoMT system and A3C, whose parameters are asynchronously updated. Besides, due to the typical delay-sensitivity mechanism and time-energy correction, ECAC can adjust dynamically to the diverse service types and system requirements. Finally, the effectiveness of ECAC for IoMT is proved on real data.