Multi-objective cooperative particle swarm optimization resource scheming technique in vehicular cloud infrastructure as a service platform

As automobiles and their devices generate more data, Vehicular Ad Hoc Networks (VANET) can help enhance network performance. VANETs provide connectivity between vehicles and infrastructure, facilitating the exchange of information and the sharing of resources. To support VANETs, Vehicular Cloud Computing (VCC) leverages cloud concepts in this environment. Vehicles in the Vehicular Cloud processing (VCC) network frequently seek resources such as processing power, bandwidth, and storage, which they (vehicles) are unable to process on their own due to resource limitations. They seek these services, which are sometimes provided, sometimes blocked because the resource is already in use by another vehicle, and sometimes rejected owing to a shortage of available resources. In the same circumstance, some resources may remain idle simply because no proper technique was employed to allocate these resources to the cars, causing a challenge in VCC. This study introduces the Cooperative Particle Swarm Optimization (CPSO) Algorithm, an enhanced variant of Particle Swarm Optimization (PSO) resource allocation mechanism for vehicular clouds. The technique employs metaheuristics to optimize search and allocate resources in a vehicular cloud. A fog-based paradigm to help with the allocation process was established. The CPSO was compared to four different algorithms: MARIA, GREEDY, FRACTAL, and WORST. During the comparison process, we consider the number of blocked, attended, and denied services, as well as throughput. Simulation results indicate that the CPSO outperformed other techniques in all four performance aspects: blocking fewer, attending more, rejecting fewer services and increasing throughput.