Weighted energy efficiency maximization for a UAV-assisted multi-platoon mobile edge computing system

Duan, Xuting, Zhou, Yukang, Tian, Daxin, Zhou, Jianshan, Sheng, Zhengguo and Shen, Xuemin (2022) Weighted energy efficiency maximization for a UAV-assisted multi-platoon mobile edge computing system. IEEE Internet of Things Journal. p. 1. ISSN 2327-4662

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With the rapid development of mobile computing, mobile edge computing has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this paper, we studied a UAV-assisted mobile edge computing system with multi-platoon vehicles. Our paper aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a two-dimensional path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the non-convexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming method. The simulation results show that the proposed method significantly surpasses conventional schemes.

Item Type: Article
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 04 Mar 2022 08:48
Last Modified: 04 Mar 2022 13:44
URI: http://sro.sussex.ac.uk/id/eprint/104690

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