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<p>In recent years, fog computing has emerged as a prominent research because of the widespread adoption and continuous advancements in Internet of Things (IoT) technologies. Fog nodes (FNs) offer storage and computational capabilities to resource-constrained IoT devices, enabling them to support IoT applications with high computing needs. Moreover, closeness FNs to IoT devices ensure they meet the latency demands of IoT applications. However, increasing the need to offload the tasks, combined with limited IoT resources, requires to development an efficient task offloading. To address this challenge, a task offloading approach utilizing the modified elk herd optimizer (MEHO) is proposed to assign tasks to FNs. MEHO is designed as an optimization approach aimed at minimizing response time. Comprehensive simulations show that MEHO outperforms other methods under various numbers of FNs, service rate, and rate of arrival data. MEHO achieves a reduction in average response time for maximum tasks by 12%, 16%, 18%, 19%, 26%, and 41% compared to modified sparrow search algorithm, sparrow search algorithm, artificial bee colony optimization, ant colony optimization, particle swarm optimization, and round robin, respectively.</p>

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An efficient task offloading based on modified elk herd optimizer for minimizing response times in fog-enabled IoT

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Oruba Alfawaz,
  • Ahmed M. Khedr,
  • Reham R. Mostafa

摘要

In recent years, fog computing has emerged as a prominent research because of the widespread adoption and continuous advancements in Internet of Things (IoT) technologies. Fog nodes (FNs) offer storage and computational capabilities to resource-constrained IoT devices, enabling them to support IoT applications with high computing needs. Moreover, closeness FNs to IoT devices ensure they meet the latency demands of IoT applications. However, increasing the need to offload the tasks, combined with limited IoT resources, requires to development an efficient task offloading. To address this challenge, a task offloading approach utilizing the modified elk herd optimizer (MEHO) is proposed to assign tasks to FNs. MEHO is designed as an optimization approach aimed at minimizing response time. Comprehensive simulations show that MEHO outperforms other methods under various numbers of FNs, service rate, and rate of arrival data. MEHO achieves a reduction in average response time for maximum tasks by 12%, 16%, 18%, 19%, 26%, and 41% compared to modified sparrow search algorithm, sparrow search algorithm, artificial bee colony optimization, ant colony optimization, particle swarm optimization, and round robin, respectively.