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<p>The expansion of the Internet of Things (IoT) has significantly improved the human welfare by allowing the intelligent sensing, automation, and real-time decision support across the diverse application domains. However, the large-scale deployment of IoT devices have generated the dynamic and heterogeneous workloads that has imposed a substantial pressure on the fog computing infrastructures. The fog layer, which bridges the IoT and cloud layers, must handle the fluctuations in service demands while it maintains the strict quality of service (QoS) constraints related to latency, response time, bandwidth utilization, and the energy efficiency. With time-varying workload intensity and an uneven task distribution, the conventional resource management strategies often have suffered from the resource under-provisioning, increased delay, and excessive energy consumption. The existing optimization-based approaches show a limited adaptability when the workload density increases, which leads to a degraded QoS performance under the large-scale IoT operations. Therefore, an efficient and an adaptive resource management policy that jointly optimizes the multiple QoS parameters remains as a critical challenge in the fog computing systems. This paper presents an Enhanced Political Optimizer-based Resource Management Strategy for Fog Computing (EPO-RMS-FC). The proposed model has formulated the resource allocation as a multi-objective optimization problem, and it employs an enhanced political optimizer that tends to improve the convergence stability and an exploration–exploitation balance. A unified fitness function has combined the energy consumption, bandwidth utilization, response time, and computational delay to guide the optimal task-to-resource mapping. The fog system model also considers the heterogeneous fog nodes and the dynamically arriving IoT requests, which allows an adaptive decision-making under the varying operational loads. Simulation-based evaluation has shown that the EPO-RMS-FC consistently outperforms the recent state-of-the-art methods. The proposed model has achieved a minimum latency of 2.50&#xa0;s for 5 operations, and it maintains 39.84&#xa0;s at 100 operations, while the comparative models has exceeded 140&#xa0;s under the identical conditions. The response time is reduced to 0.29&#xa0;s, with an average improvement of more than 60% over the competing techniques. Energy consumption remains as low as 17.93 kWh for light workloads and 77.07 kWh for high workloads, which results in an energy saving of approximately 50–55%. The convergence behavior has confirmed a stable optimization within fewer than 50 iterations, while it validates the robustness and scalability.</p>

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An Enhanced Political Optimizer-Based Resource Management Strategy for Energy-Efficient and Low-Latency Fog Computing

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Sunakshi Mehta,
  • Supriya Raheja,
  • Manoj Kumar

摘要

The expansion of the Internet of Things (IoT) has significantly improved the human welfare by allowing the intelligent sensing, automation, and real-time decision support across the diverse application domains. However, the large-scale deployment of IoT devices have generated the dynamic and heterogeneous workloads that has imposed a substantial pressure on the fog computing infrastructures. The fog layer, which bridges the IoT and cloud layers, must handle the fluctuations in service demands while it maintains the strict quality of service (QoS) constraints related to latency, response time, bandwidth utilization, and the energy efficiency. With time-varying workload intensity and an uneven task distribution, the conventional resource management strategies often have suffered from the resource under-provisioning, increased delay, and excessive energy consumption. The existing optimization-based approaches show a limited adaptability when the workload density increases, which leads to a degraded QoS performance under the large-scale IoT operations. Therefore, an efficient and an adaptive resource management policy that jointly optimizes the multiple QoS parameters remains as a critical challenge in the fog computing systems. This paper presents an Enhanced Political Optimizer-based Resource Management Strategy for Fog Computing (EPO-RMS-FC). The proposed model has formulated the resource allocation as a multi-objective optimization problem, and it employs an enhanced political optimizer that tends to improve the convergence stability and an exploration–exploitation balance. A unified fitness function has combined the energy consumption, bandwidth utilization, response time, and computational delay to guide the optimal task-to-resource mapping. The fog system model also considers the heterogeneous fog nodes and the dynamically arriving IoT requests, which allows an adaptive decision-making under the varying operational loads. Simulation-based evaluation has shown that the EPO-RMS-FC consistently outperforms the recent state-of-the-art methods. The proposed model has achieved a minimum latency of 2.50 s for 5 operations, and it maintains 39.84 s at 100 operations, while the comparative models has exceeded 140 s under the identical conditions. The response time is reduced to 0.29 s, with an average improvement of more than 60% over the competing techniques. Energy consumption remains as low as 17.93 kWh for light workloads and 77.07 kWh for high workloads, which results in an energy saving of approximately 50–55%. The convergence behavior has confirmed a stable optimization within fewer than 50 iterations, while it validates the robustness and scalability.