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2026

Failure-Aware Intelligent Task Offloading for Dynamic Vehicular Fog Computing Enabled by GNN-Based Federated Advantage Actor-Critic Learning

Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading scheme (OFA-offloading). Although the exact offloading failure probability is difficult to obtain, it is determined by the service capability of the selected service vehicle (SV). Thus a new tractable metric, i.e., vehicle service capability (VSC), is defined to reflect the offloading failure probability, which is a function of vehicle mobility, resource availability, and link status. Based on VSC of each SV and considering that delay is important for VFC networks, an OFA delay utility is designed. Aiming to maximize this utility, a joint offloading SVs selection and computing resource allocation optimization problem is formulated. Since it is NP-hard and the VFC network is highly dynamic, a novel Graph Neural Network based federated Advantage Actor-Critic (GNN-FAC) algorithm is proposed to solve the problem. GNN-FAC can proactively predict environmental dynamics and incorporate VSC as a critical criterion for offloading decisions. Simulation results demonstrate that compared with existing offloading algorithms, OFA-offloading can improve the OFA delay utility by up to 40%.

Yihao Wu, Yanli Qi, Yiqing Zhou et al. · 0 citations
Open access Jul 2026

The SDN for fog computing in VANETs

The rapid expansion of Internet of Things (IoT) applications has increased the demand for high computing power, often beyond the capabilities of mobile devices limited by processing speed and battery life. Vehicular fog computing offers a solution by utilizing parked vehicles as computational nodes, bringing resources closer to users. The main objective of this study is to explore the use of electric vehicles as computational nodes in a fog computing architecture, allowing mobile users to offload tasks to these idle vehicles. To manage these distributed resources efficiently, a Software-Defined Networking (SDN) controller is integrated to monitor vehicle parameters such as power, energy, and parking duration, and to dynamically allocate computation requests. Simulation experiments conducted in MATLAB Simulink demonstrate that the proposed system reduces computation time by up to 45%, decreases energy consumption by nearly 30%, and improves fog resource utilization by more than 40% compared with local execution. The results also reveal how the weighting factors (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}) influence the decision-making process and help maintain a balance between performance, energy efficiency, and stability. Overall, integrating SDN control with parked electric vehicles enables faster, energy-aware, and more reliable computing services for mobile IoT applications.

Ali Kies, Amal Boumedjout, Zoulikha Mekkakia Maaza et al. · 0 citations
Jul 2026

Task Offloading and Resource Scheduling in a Vehicle-RSU-Cloud Resource Environment

This paper proposes a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources, and designs an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions.

Liqun Yang · 0 citations
Conference Jun 2026

Toward Mobility-Aware and Explainable Orchestration in MEC-Enabled Vehicular Networks

With the emergence of autonomous vehicles and the ever-increasing volume of generated data, the Mobile Edge Computing paradigm has been proposed to address challenges related to latency and computational capacity. However, static vehicle-to-MEC association policies fail to meet these requirements due to the highly dynamic nature of vehicular networks. To address these challenges, this PhD research focuses on mobilityaware orchestration in MEC-enabled vehicular networks. In our first contribution, we introduce an ETSI-compliant proactive migration framework based on proximity-triggered migration notifications and a mobility-aware task migration strategy to relocate vehicular applications during mobility. The second contribution extends this work toward adaptive orchestration by formulating allocation and migration as a decision-making problem and developing a learning-based framework with a Simu5G–Python interface and a fairness- and delay-aware Maskable PPO agent. Results show improved response time, lower deadline miss rate, and balanced resource utilization under dense vehicular conditions. Our current work focuses on explainability methods to understand the learned policy and support trustworthy deployment while improving performance in terms of E2E delay, deadline miss rate, and fairness in resource utilization.

Rim Sayegh, Hela Marouane, Sahar Hoteit et al. · 0 citations
Open access Jun 2026

Enhancing IoT Communication Efficiency Through a Vehicular Fog Resource Schedule

    The growth of Internet of Things (IoT) devices has revealed significant issues with cloud-based infrastructures, including excessive latency, network congestion, and inefficient resource utilization. Fog computing helps with these challenges by deploying computing resources near IoT endpoints. However, the growing number of IoT workloads means that infrastructure needs to grow in ways that aren't sustainable. This study introduces an intelligent, hierarchical resource management framework that improves existing fog infrastructure without requiring additional hardware installation. A Master Fog (MF) layer handles the distribution of resources between cloud and Vehicular Fog Resources (VFRs) using the Comparative Attributes Algorithm (CAA) for multi-criteria task prioritization and the Grey Wolf Optimizer (GWO) for optimal VFR selection. the proposed framework enables sustainable, responsive, and cost-effective IoT-fog ecosystems, advancing practical solutions for intelligent resource scheduling in distributed computing environments.

Raghad Ghalib Alsultan · 0 citations