Adaptive fuzzy-reinforcement framework for real-time task scheduling and resource optimization in medical edge computing

Abstract

Abstract This paper proposes a hybrid fuzzy-reinforcement learning framework for real-time task scheduling and resource optimization in medical edge computing. The proposed framework introduces a direct mathematical coupling between fuzzy inference and reinforcement learning rather than a simple hybrid combination. By integrating fuzzy logic to evaluate task urgency, bandwidth congestion, and battery constraints with a reinforcement learning agent, the framework dynamically refines offloading strategies. Simulation results in Internet of Medical Things (IoMT) environments demonstrate the framework’s superiority over existing benchmarks, achieving up to 42% lower average latency, 31% greater energy efficiency, and up to 20% improvement over Dynamic Priority-Based Task Scheduling and Adaptive Resource Allocation (DPTARA) under the evaluated benchmark settings in the completion rate of critical tasks. Operating with a linear time complexity of $$O\left(N\cdot M\right),$$ the proposed system guarantees scalable and robust performance for delay-sensitive healthcare applications. Experimental results across four benchmark IIoT healthcare datasets demonstrate the effectiveness of the proposed framework. Specifically, the model achieves average accuracy improvements of 2.8–4.6% over state-of-the-art baselines, while reducing false negative rates by up to 35.4%. In addition, the proposed fuzzy–reinforcement learning scheduler decreases average task latency by 23.7%, improves resource utilization by 18.9%, and enhances system adaptability under dynamic workload conditions. These results confirm the framework’s robustness, scalability, and suitability for real-time medical edge computing environments.