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<p>Effective task scheduling is critical to minimizing energy consumption and optimizing resource utilization in Internet of Things (IoT) applications deployed on fog computing platforms. This paper proposes a novel algorithm, Dimension Learning and Hunting-optimized Grey Wolf Optimization (DLH-GWO), to optimize green IoT scheduling in fog computing. DLH-GWO combines the basic GWO with a dimension-learning scheme and a local-hunting scheme to improve the exploration–exploitation balance and accelerate convergence. It optimizes solution diversity and avoids early convergence, dramatically cutting energy consumption, task makespan, and rejection rates in fog-based IoT systems. From the experimental results, DLH-GWO is observed to significantly outperform other metaheuristics in terms of energy consumption and makespan. The DLH-GWO algorithm has been shown to perform efficiently regardless of the number of tasks and the capacities of fog nodes. This paper develops a highly efficient optimality method for the emerging field of fog computing and a scalable solution to IoT scheduling problems.</p>

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DLH-GWO: a dimension learning and hunting-enhanced grey wolf optimizer for multi-objective internet of things scheduling in fog environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Heyu Wen

摘要

Effective task scheduling is critical to minimizing energy consumption and optimizing resource utilization in Internet of Things (IoT) applications deployed on fog computing platforms. This paper proposes a novel algorithm, Dimension Learning and Hunting-optimized Grey Wolf Optimization (DLH-GWO), to optimize green IoT scheduling in fog computing. DLH-GWO combines the basic GWO with a dimension-learning scheme and a local-hunting scheme to improve the exploration–exploitation balance and accelerate convergence. It optimizes solution diversity and avoids early convergence, dramatically cutting energy consumption, task makespan, and rejection rates in fog-based IoT systems. From the experimental results, DLH-GWO is observed to significantly outperform other metaheuristics in terms of energy consumption and makespan. The DLH-GWO algorithm has been shown to perform efficiently regardless of the number of tasks and the capacities of fog nodes. This paper develops a highly efficient optimality method for the emerging field of fog computing and a scalable solution to IoT scheduling problems.