Optimizing Edge Computing for Telemedicine: A Deep Reinforcement Learning Approach to Task Offloading and Resource Allocation

This study introduces an optimized Mobile Edge Computing (MEC) framework specifically designed for telemedicine applications. By integrating deep reinforcement learning (DRL), we develop a system that enhances task offloading and resource management in edge computing environments. Our approach focuses on the critical needs of healthcare monitoring, such as urgency in emergency responses and sustainability in power usage. The proposed model employs a parallel processing architecture that minimizes energy consumption and processing time by intelligently managing data flow and computational tasks across multiple edge servers. Key innovations include the use of a Deep Deterministic Policy Gradient (DDPG) algorithm, which dynamically optimizes the balance between processing speed and energy efficiency, ensuring robust and timely medical services. Through extensive simulations, our model demonstrates significant improvements in cost-effectiveness and operational efficiency compared to traditional methods, making it a promising solution for future smart healthcare systems.