Optimizing Task Offloading in Internet of Medical Things Systems: A Hybrid Fog-Cloud Approach with Actor-Critic Decision Making

In response to the escalating demands for advanced processing capabilities in healthcare applications, we introduce a ground-breaking solution, the Hybrid Fog-Cloud Offloading (HFCO) approach, for Internet of Medical Things (IoMT) devices. This method optimally offloads complex tasks to cloud servers or nearby fog nodes, mitigating the inherent limitations of IoMT devices. The offloading decision is intelligently made by IoMT devices, considering task requirements and collaborating with nearby fog nodes. The challenge of selecting the most suitable fog node is addressed through an autonomous decision-making mechanism employing a Markov Decision Process (MDP) and the Actor-Critic algorithm. Our proposed strategy surpasses current state-of-the-art approaches, evident in numerical simulations showcasing reduced latency, heightened efficiency, load balancing, and improved scalability, resilience, and security. By seamlessly integrating cloud resources, the HFCO approach empowers IoMT applications, ensuring minimal latency, efficient task execution, and a robust, secure healthcare infrastructure. This research pioneers a dynamic solution to enhance the operational efficacy of IoMT devices in healthcare settings.