Mobility and Resource Allocation with Intelligent Clustering in UAVs Assisted VANETs

In the past decade, there has been significant development and utilization of Unmanned Aerial Vehicles (UAVs) in Vehicular Ad Hoc Networks (VANETs) across various applications. The integration of UAVs in VANETs has provided vehicles with enhanced performance by enabling communication through an aerial medium, thereby bypassing ground-level obstacles. Efficient management of mobility and network resources becomes crucial when dealing with a large number of highly dynamic vehicles. To address this, the proposed approach in this paper is Mobility and Resource Allocation with Intelligent Clustering in UAVs-assisted VANETs (MRAIC-UAVs). The key components of the proposed approach include the network model, mobility model, clustering strategy, and UAVs Cluster Head (CH) selection process. The selection of CHs is based on the evaluation of parameters such as residual energy, UAVs mobility, UAVs degree difference, distance, and UAVs stability. This approach significantly improves network energy efficiency and packet delivery ratio. The simulation is conducted using OMNET++ with the SUMO mobility generator, and a comparison is made with earlier models such as PRO-UAVs and RJEDC-UAVs. The performance analysis considers parameters such as packet delivery ratio, end-to-end delay, energy efficiency, and energy consumption. The simulation results demonstrate that the proposed MRAIC-UAVs approach achieves higher energy efficiency and packet delivery ratio while exhibiting lower end-to-end delay and energy consumption compared to earlier approaches.