A GWO-based approach for task scheduling in heterogeneous vehicular fog computing environments

Key Points

• Task scheduling efficiency improved through the grey wolf optimization algorithm, leading to better resource allocation.
• The algorithm significantly reduced task execution time by 30% under heterogeneous vehicular fog computing conditions.
• Observational analysis within diverse network scenarios reveals effective management of resources among different vehicle types.
• Results suggest potential benefits for future vehicular networks, indicating a need for scalable resource management solutions.