articleIEEE Transactions on Vehicular TechnologyJan 21, 2020GREEN OA

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

MLMushu LiNCNan ChengJGJie GaoYWYinlu WangLZLian Zhao

University of Waterloo · Xidian University · +2 more institutions

Indexed inarxivcrossref

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…

Citation impact

478
total citations
FWCI
474.05
Percentile
100%
References
38
Citations per year

Authors

6
  • ML
    Mushu LiCorresponding

    University of Waterloo

  • NC
    Nan Cheng

    Xidian University

  • JG
    Jie Gao

    University of Waterloo

  • YW
    Yinlu Wang

    Southeast University

  • LZ
    Lian Zhao

    Toronto Metropolitan University

Topics & keywords

Keywords
  • Mobile edge computing
  • Cloudlet
  • Computation offloading
  • Computation
  • Resource allocation
  • Optimization problem
  • Edge computing
  • Convex optimization
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