The rise of Internet of Things (IoT) devices has led to the development of numerous time-sensitive applications that require quick responses and low latency. Fog computing has emerged as a solution for processing these IoT applications, but it faces challenges such as resource allocation and job scheduling. Therefore, it is crucial to determine how to assign and schedule tasks on Fog nodes. This work aims to schedule tasks in IoT while minimizing the total energy consumption of nodes and enhancing the Quality of Service (QoS) requirements of IoT tasks, taking into account task deadlines. This paper classifies Fog nodes into two categories based on their traffic level: low and high. It schedules short-deadline tasks on low-traffic nodes using an Improved Golden Eagle Optimization (IGEO) algorithm, an enhancement that utilizes genetic operators for discretization. Long-deadline tasks are processed on high-traffic nodes using reinforcement learning (RL). This combined approach is called the Reinforcement Improved Golden Eagle Optimization (RIGEO) algorithm. Experimental results demonstrate that RIGEO achieves up to a 29% reduction in energy consumption, up to an 86% improvement in response time, and up to a 19% reduction in deadline violations compared to state-of-the-art algorithms.

翻译：物联网设备的兴起催生了大量对快速响应和低延迟有严格要求的时效性应用。雾计算作为处理此类物联网应用的解决方案应运而生，但其面临着资源分配与作业调度等挑战。因此，如何在雾节点上分配和调度任务至关重要。本研究旨在调度物联网任务时，在考虑任务截止时间的前提下，最小化节点的总能耗并提升物联网任务的服务质量要求。本文根据流量水平将雾节点分为两类：低流量节点与高流量节点。对于短截止时间任务，采用一种改进的金鹰优化算法在低流量节点上进行调度，该算法通过引入遗传算子实现了离散化增强。长截止时间任务则在高流量节点上使用强化学习进行处理。这种结合方法被称为强化改进金鹰优化算法。实验结果表明，与现有先进算法相比，RIGEO算法最高可降低29%的能耗，提升86%的响应时间，并减少19%的截止时间违规情况。