Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems

The Internet of Medical Things (IoMT) produces substantial amounts of real-time data from devices like ECG and EEG monitors, presenting significant issues in latency, energy efficiency, and resource allocation. Traditional offloading methods often fail to satisfy the low-latency and high-reliability requirements of modern healthcare systems. To address these limitations, this study presents a hybrid computing framework that integrates edge and cloud resources to facilitate efficient and scalable data processing. The proposed system integrates Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Graph Neural Networks (GNNs) to enhance task offloading, reduce latency, and optimize resource utilization. Experimental findings indicate that the approach significantly enhances system performance, minimizes energy consumption, and ensures consistent connectivity among diverse IoMT devices. The framework enables adaptable and efficient real-time processing, thereby enhancing advanced healthcare systems and optimizing both clinical decision-making and patient outcomes.