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<p>The growing number of Internet of Things (IoT) devices and interconnected systems has significantly increased computational demands, necessitating efficient task scheduling in Fog-Cloud-IoT (FCIoT) networks. While fog computing offers reduced latency, optimal performance is challenged by energy constraints and dynamic network conditions. This work formulates the FCIoT task scheduling problem and proposes an energy-efficient, deadline-aware method incorporating a dynamic thresholding mechanism. We enhance the Heat Transfer Relation-based Optimization Algorithm (HTOA) to create an Improved HTOA (IHTOA), which adaptively schedules tasks across fog and cloud layers. Simulation results demonstrate that IHTOA substantially improves task scheduling in FCIoT environments. The proposed method significantly outperforms other approaches, demonstrating notable improvements over Extended Classifier System (XCS), Golden Eagle Optimizer (GEO), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and HTOA in resource allocation efficiency and average response time. Furthermore, it substantially reduces deadline violation occurrences and extends network lifetime compared to the other approaches.</p>

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An Improved Heat Transfer Relation-based Optimization Algorithm for Energy-Efficient Internet of Things’ Resource Allocation

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Pouneh Janmohammadi,
  • Touraj BaniRostam,
  • Parvaneh Asghari

摘要

The growing number of Internet of Things (IoT) devices and interconnected systems has significantly increased computational demands, necessitating efficient task scheduling in Fog-Cloud-IoT (FCIoT) networks. While fog computing offers reduced latency, optimal performance is challenged by energy constraints and dynamic network conditions. This work formulates the FCIoT task scheduling problem and proposes an energy-efficient, deadline-aware method incorporating a dynamic thresholding mechanism. We enhance the Heat Transfer Relation-based Optimization Algorithm (HTOA) to create an Improved HTOA (IHTOA), which adaptively schedules tasks across fog and cloud layers. Simulation results demonstrate that IHTOA substantially improves task scheduling in FCIoT environments. The proposed method significantly outperforms other approaches, demonstrating notable improvements over Extended Classifier System (XCS), Golden Eagle Optimizer (GEO), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and HTOA in resource allocation efficiency and average response time. Furthermore, it substantially reduces deadline violation occurrences and extends network lifetime compared to the other approaches.