Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks of...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 69; H. 3; S. 3424 - 3438
Hauptverfasser:	Li, Mushu, Cheng, Nan, Gao, Jie, Wang, Yinlu, Zhao, Lian, Shen, Xuemin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms alternating direction method of multipliers (ADMM) Computation offloading Computational modeling Computer simulation Edge computing Energy consumption Energy efficiency Internet of Things (IoT) Mathematical programming Mobile computing mobile edge computing (MEC) Optimization Power efficiency Problem solving Resource allocation Resource management Spatial distribution Task analysis Trajectory Trajectory optimization Unmanned aerial vehicle Unmanned aerial vehicles User requirements
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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Abstract	In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.
AbstractList	In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum UAV energy consumption. In the considered scenario, a UAV plays the role of an aerial cloudlet to collect and process the computation tasks offloaded by ground users. Given the service requirements of users, we aim to maximize UAV energy efficiency by jointly optimizing the UAV trajectory, the user transmit power, and computation load allocation. The resulting optimization problem corresponds to nonconvex fractional programming, and the Dinkelbach algorithm and the successive convex approximation (SCA) technique are adopted to solve it. Furthermore, we decompose the problem into multiple subproblems for distributed and parallel problem solving. To cope with the case when the knowledge of user mobility is limited, we adopt a spatial distribution estimation technique to predict the location of ground users so that the proposed approach can still be applied. Simulation results demonstrate the effectiveness of the proposed approach for maximizing the energy efficiency of UAV.
Author	Gao, Jie Wang, Yinlu Li, Mushu Zhao, Lian Shen, Xuemin Cheng, Nan
Author_xml	– sequence: 1 givenname: Mushu orcidid: 0000-0002-9694-3294 surname: Li fullname: Li, Mushu email: m475li@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 2 givenname: Nan orcidid: 0000-0001-7907-2071 surname: Cheng fullname: Cheng, Nan email: dr.nan.cheng@ieee.org organization: State Key Laboratory of Information Security, School of Telecommunications Engineering, Xidian University, Xian, China – sequence: 3 givenname: Jie orcidid: 0000-0001-6095-2968 surname: Gao fullname: Gao, Jie email: jie.gao@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada – sequence: 4 givenname: Yinlu orcidid: 0000-0003-1121-0743 surname: Wang fullname: Wang, Yinlu email: yinluwang@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China – sequence: 5 givenname: Lian orcidid: 0000-0002-5602-1738 surname: Zhao fullname: Zhao, Lian email: l5zhao@ryerson.ca organization: Department of Electrical, Computer and Biomedical Engineering, Ryerson University, Toronto, ON, Canada – sequence: 6 givenname: Xuemin surname: Shen fullname: Shen, Xuemin email: sshen@uwaterloo.ca organization: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
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Snippet	In this paper, we study unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) with the objective to optimize computation offloading with minimum...
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SubjectTerms	Algorithms alternating direction method of multipliers (ADMM) Computation offloading Computational modeling Computer simulation Edge computing Energy consumption Energy efficiency Internet of Things (IoT) Mathematical programming Mobile computing mobile edge computing (MEC) Optimization Power efficiency Problem solving Resource allocation Resource management Spatial distribution Task analysis Trajectory Trajectory optimization Unmanned aerial vehicle Unmanned aerial vehicles User requirements
Title	Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization
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