Enhancing Routing Efficiency in UAV-Assisted Vehicular Networks Via Integrating Fog Computing and Software-Defined Networking
Unmanned aerial vehicles (UAVs) have been used in heterogeneous vehicular networks to enhance performance on extremely congested roads and areas with low coverage. Nevertheless, when aerial relays are added to the routing process, the routing occurred in a more complex environment. Routing protocols often favour UAV relay routes because UAV relay route can have better link quality and a small number of hops, but the routes developed from these routing protocols can lead to load imbalance between the aerial and terrestrial elements of the network and sometimes the UAV can be the bottleneck itself. This paper aims to utilize modern networking paradigms, i.e., Software-Defined Networking (SDN) and Fog Computing—to achieve routing operations in a heterogeneous, cluster-based Vehicular Ad Hoc Network (VANET). Fog nodes will take responsibility for offloading/performing the computational tasks involved in cluster formation, inter-segment routing between the aerial and terrestrial paths, and determining the optimal number of cluster heads. Fog nodes will use fuzzy logic and reinforcement learning to execute these tasks. The role of the SDN controller will be to manage traffic flow across fog cells using its global view of the multi-tiered network architecture which integrates heterogeneous vehicles with fog-layer connectivity. The proposed model was assessed visa a variety of routing protocols designed for UAV (Unmanned Aerial Vehicle)-assisted networks as well all routings used in traditional vehicular networks in several scenarios. The performance has proven to be far superior in a variety of aspects, including: the packet delivery ratio as a function of vehicle density and the aerial relay density; network utilization efficacy as a function of the harvesting node speed; and end-to-end delay as a function of ground node density. Finally, the results provide strong evidence on the success of the selective clustering method taken up in our model, as based on the dwell time of the cluster.