Autonomous UAV Aided Vehicular Edge Computing for Service Offering

Abstract

High Dynamic Unmanned Aerial Vehicles (UAVs) are introduced to assist V2X networking and communication that requires ultra low latency and safety requirements (ULLC). In this paper, we propose a Follow Me UAV (FMU) architecture that aids Vehicular Edge Computing for service offering. Then, a communication protocol is proposed and associated with placement, routing, and optimization algorithms in small and dense networks (OFMU and AFMU). We use deep learning techniques (LSTM and GRU) to predict the connected vehicles trajectory, then the results are used to feed the optimization models. Then, we clarify through Reinforcement Learning based implementations autonomous UAV path planning. Optimization approaches are implemented and evaluated under different quality and computing scenarios. Then, the models are quantified under UAV selection time and energy cost. Results prove the feasibility of the optimization algorithms and suggest the use of mobile UAV as low latency edge servers for service offering.