Location-Aware Task Scheduling in Three-Layer IoT Systems Using Reinforcement Learning

Subject Areas : Computer Engineering and IT

mohammad zare ¹ , seyedebrahim dashti ^{2 *}

1 - Islamic Azad university
2 - Department of Computer Engineering, Jahrom Branch, Islamic Azad University, Jahrom, Iran

Received: 2025-01-05 Accepted : 2025-08-29 Published : 2025-10-13

Keywords: Internet of Things (IoT), Cloud Computing, Fog Computing, Location-Aware Scheduling, Reinforcement Learning,

Abstract :

The rapid expansion of Internet of Things (IoT) devices has introduced critical challenges in task scheduling and resource allocation across cloud–fog ecosystems. Traditional approaches, such as location-aware scheduling and heuristic algorithms, often fail to adapt to dynamic IoT environments, where workload intensity, network traffic, and energy constraints fluctuate unpredictably. To address these issues, this study proposes a Reinforcement Learning (RL)-based scheduling framework employing a Deep Q-Network (DQN) for real-time decision-making. The model incorporates system state parameters—including task characteristics, available resources, and network conditions—into its reward function, designed to minimize task completion time, reduce congestion, and optimize energy consumption. Comparative simulation results demonstrate that the proposed RL framework significantly outperforms Genetic Algorithm (GA) and Location-Aware methods, achieving higher throughput and fewer SLA violations. Statistical testing (t-tests, p-values < 0.001) further confirms the robustness and significance of these improvements. The proposed approach thus establishes a scalable and intelligent foundation for future fog–cloud–IoT task orchestration .

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