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<p>Vehicular fog computing (VFC) is a key technology for enhancing Internet of Vehicles (IoV) applications by providing distributed computing resources at the network edge. However, the dynamic nature of vehicular environments poses challenges in optimizing task offloading and resource allocation, particularly in minimizing response time and maintaining Quality of Service (QoS). This paper proposes a mobility-aware task scheduling and offloading model that addresses these challenges by optimizing task processing under varying mobility conditions. The model combines static and mobile vehicular fog nodes, using queuing theory and a Markov Modulated Service Process (MMSP) to account for service rate variability caused by vehicular mobility. The numerical results demonstrate that the proposed approach significantly reduces response time and improves server utilization, particularly in low mobility scenarios. The method proves effective in managing dynamic vehicular environments, making it a robust solution for future IoV applications.</p>

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Mobility-aware task scheduling in vehicular fog computing networks: a combined DTMC and MMSP approach

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Hibat Eallah Mohtadi,
  • Mohamed Hanini,
  • Said El Kafhali,
  • Abdelkrim Haqiq

摘要

Vehicular fog computing (VFC) is a key technology for enhancing Internet of Vehicles (IoV) applications by providing distributed computing resources at the network edge. However, the dynamic nature of vehicular environments poses challenges in optimizing task offloading and resource allocation, particularly in minimizing response time and maintaining Quality of Service (QoS). This paper proposes a mobility-aware task scheduling and offloading model that addresses these challenges by optimizing task processing under varying mobility conditions. The model combines static and mobile vehicular fog nodes, using queuing theory and a Markov Modulated Service Process (MMSP) to account for service rate variability caused by vehicular mobility. The numerical results demonstrate that the proposed approach significantly reduces response time and improves server utilization, particularly in low mobility scenarios. The method proves effective in managing dynamic vehicular environments, making it a robust solution for future IoV applications.