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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Vydáno v:	IEEE transactions on intelligent transportation systems Ročník 23; číslo 7; s. 8435 - 8450
Hlavní autoři:	Liu, Yang, Zhou, Jianshan, Tian, Daxin, Sheng, Zhengguo, Duan, Xuting, Qu, Guixian, Leung, Victor C. M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Duan, Xuting Sheng, Zhengguo Leung, Victor C. M. Qu, Guixian Tian, Daxin Liu, Yang Zhou, Jianshan
Author_xml	– sequence: 1 givenname: Yang orcidid: 0000-0002-0053-8287 surname: Liu fullname: Liu, Yang email: xdfliuyang1@126.com organization: Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Jianshan orcidid: 0000-0001-5331-6162 surname: Zhou fullname: Zhou, Jianshan email: jianshanzhou@foxmail.com organization: Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China – sequence: 3 givenname: Daxin orcidid: 0000-0001-7796-5650 surname: Tian fullname: Tian, Daxin email: dtian@buaa.edu.cn organization: Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China – sequence: 4 givenname: Zhengguo orcidid: 0000-0003-2143-4003 surname: Sheng fullname: Sheng, Zhengguo email: z.sheng@sussex.ac.uk organization: Department of Engineering and Design, University of Sussex, Brighton, U.K – sequence: 5 givenname: Xuting orcidid: 0000-0001-5931-8310 surname: Duan fullname: Duan, Xuting email: duanxuting@buaa.edu.cn organization: Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China – sequence: 6 givenname: Guixian orcidid: 0000-0003-3450-7056 surname: Qu fullname: Qu, Guixian email: guixianqu@foxmail.com organization: Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Transportation Science and Engineering, Beihang University, Beijing, China – sequence: 7 givenname: Victor C. M. orcidid: 0000-0003-3529-2640 surname: Leung fullname: Leung, Victor C. M. email: vleung@ieee.org organization: Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
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Snippet	Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for...
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SubjectTerms	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
Title	Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles
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