Enhanced hybrid PSO-FA: Joint optimization of resource allocation for VFC in 6G networks

Abstract

Mobile vehicular ad hoc networks with fog computing in 6 G present a novel distributed architecture to enhance network computing capabilities in dynamic environments. However, these self-organizing networks face significant quality-of-service challenges including large average latency, high total energy consumption, and poor load balancing among fog nodes during multi-task offloading in mobile scenarios. This paper proposes a cross-layer resource and information management approach based on enhanced hybrid particle swarm optimization and firefly algorithm (EH-PSO-FA) for joint optimization. A fitness function incorporating load balancing and local computation penalties is designed to address mobility-aware resource allocation. To enhance global search capability and robustness in dynamic ad hoc environments, both PSO and FA algorithms are enhanced with adaptive mechanisms to avoid premature convergence and local optima. Extensive performance analysis and simulation demonstrate that EH-PSO-FA significantly outperforms baseline protocols, achieving up to 26.18% reduction in average latency and 57.74% improvement in energy-efficient design. The scalable solution effectively supports power-aware computing in mobile wireless ad hoc networks for urban vehicular scenarios.