Dynamic priority-based task scheduling and adaptive resource allocation algorithms for efficient edge computing in healthcare systems

The Internet of Things (IoT) has revolutionized healthcare by interconnecting a wide range of devices over the Internet. Cloud computing has traditionally fulfilled the computational demands of these IoT devices, enabling the collection and analysis of data from healthcare environments. However, its suitability for latency-sensitive applications has been limited due to the presence of remote cloud data centers. In response, edge computing has emerged, pushing computational capabilities to the edge of networks to mitigate these limitations. This work presents a novel approach for task scheduling and resource allocation in edge computing environments tailored for healthcare monitoring systems. By implementing dynamic priority-based task scheduling and adaptive resource allocation algorithms, our methodology enhances the handling of resource requests between end devices, edge nodes (ENs), and the cloud. These innovations focus on reducing latency, optimizing resource utilization, and improving overall system performance. Extensive simulations demonstrate the efficacy of our approach, showcasing significant improvements in task execution time, resource utilization, and energy consumption. • DPTARA introduces dynamic priority-based scheduling for real-time healthcare task based on urgency and system load. • It implements adaptive resource allocation to efficiently distribute tasks across local, edge, and cloud environments. • The system reduces latency, accelerates execution, and improves resource utilization beyond existing solutions. • Designed for real-world applications, DPTARA ensures scalability and modular integration for healthcare and IoT systems.