Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles

Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial veh...

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Veröffentlicht in:IEEE transactions on intelligent transportation systems Jg. 23; H. 7; S. 8435 - 8450
Hauptverfasser: Liu, Yang, Zhou, Jianshan, Tian, Daxin, Sheng, Zhengguo, Duan, Xuting, Qu, Guixian, Leung, Victor C. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerial-ground cooperative vehicular network
Cloud computing
Communication
Computational geometry
Computational modeling
convex optimization
Convexity
Edge computing
Energy harvesting
Feasibility
Mathematical programming
Mobile computing
mobile edge computing
Optimization
Optimization models
Platooning
Processor scheduling
Quadratic programming
Resource scheduling
Task analysis
unmanned aerial vehicle
Unmanned aerial vehicles
Vehicle dynamics
Wireless power transmission
ISSN:1524-9050, 1558-0016
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Zusammenfassung:Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) for a platoon of wireless power transmission (WPT)-enabled vehicles. Our objective is to maximize the system-wide computation capacity under both communication and computation resource constraints. We incorporate the coupled effects of the platooning vehicles and the flying UAV, air-to-ground (A2G) and ground-to-air (G2A) communications, onboard computing and energy harvesting into a joint scheduling optimization model of communication and computation resources. To tackle the resulting optimization problem, we propose a successive convex programming method based on a second-order convex approximation, in which feasible search directions are obtained by solving a sequence of quadratic programming subproblems and used to generate feasible points that can approach a local optimum. We also theoretically prove the feasibility and convergence of the proposed method. Moreover, simulation results are provided to validate the effectiveness of our proposed method and demonstrate its superior performance over other conventional schemes.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3082539