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.