Distributed Resource Optimization and Allocation for UAV-Assisted MEC in Internet of Vehicles

As an essential element of intelligent transport systems, the Internet of vehicles (IoV) has brought an immersive user experience recently. Meanwhile, the emergence of mobile edge computing (MEC) has enhanced the computational capability of the vehicle which reduces task processing latency and energy consumption effectively and meets the quality of service requirements of vehicle users. However, there are still some problems in the MEC-assisted IoV system such as poor connectivity of vehicle networks and high cost of traditional infrastructure deployment and maintenance. Unmanned aerial vehicles (UAVs) equipped with MEC servers have become a promising approach for providing communication and computing services to mobile vehicles because of their agility, low cost, and readily to deployment. Hence, in this article, an optimal framework for the UAV-assisted MEC system for the IoV to minimize the average system cost is presented. Through joint consideration of computational offloading decisions and computational resource allocation, the optimization problem of our proposed architecture is presented to reduce system energy consumption and delay. For purpose of tackling this issue, the original non-convex issue is converted into a convex issue and the alternating direction method of multipliers-based distributed optimal scheme is developed. The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes, and the convergence of the proposed scheme is also significant.