The rapid evolution of the Internet of Things (IoT) technology makes smart healthcare monitoring systems, in which patient health data is constantly collected with the help of wearable sensors and medical equipment. Nevertheless, transmitting large volumes of high latency medical information directly to remote cloud-computers can often lead to energy usage and communication latency. These problems are addressed by fog computing through decreasing the network congestion, improving system performance, and moving the processing of the data near the IoT devices. This paper presents an AI-based adaptive offloading of tasks infrastructure with a reinforcementbased scheduling system in the healthcare monitoring field. The proposed approach involves a Q-learning algorithm to dynamically decide whether cloud servers or fog nodes should process healthcare tasks. The decision-making is made considering realtime system components such as network latency, node energy availability, CPU requirements, and job deadlines. Experimental testing based on a healthcare IoMT dataset containing 1000 task instances reveals better performance with approximately $\mathbf{1 5}-\mathbf{2 0}$ percent lower latency and 10-15 percent energy consumption.
Priyanshu Sekhar Bhuyan, Rishav Kumar Shrivastava, Vridhi Patnala et al.· International Conference on...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has intensified demands for low-latency, resource-efficient task scheduling at the network edge. Conventional policies such as Round-Robin and First-Come-First-Serve (FCFS) fail to satisfy the Quality-of-Service (QoS) requirements of Industrial-IoT and autonomous-vehicle workloads. This paper presents Multi-Queue Priority-Based Scheduling (MQPBS), a lightweight algorithm that classifies tasks into three dynamic priority queues (High, Medium, Low) using deadline-aware heuristics, applies Shortest-Job-First (SJF) intra-queue ordering, and employs an aging mechanism to prevent starvation. Extensive simulation over task sets of 200–1000 tasks demonstrates that MQPBS reduces average waiting time by up to 17.6%, improves throughput by up to 10.8%, lowers energy consumption by 20%, and cuts the Deadline Miss Ratio (DMR) compared with the Priority-Aware Task-Scheduling (PaTS) baseline. Ablation experiments confirm the independent contribution of each algorithmic component. Scalability and sensitivity analyses further validate the robustness of MQPBS under heterogeneous arrival patterns and varying load intensities. The results establish MQPBS as a scalable, reliable scheduler for next-generation edge infrastructures.
Shibang Maity, Roshan Panda, M. Tanisha et al.· International Conference on...· 0 citations