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

By Mushu Li, Nan Cheng, Jie Gao, Yinlu Wang, Lian Zhao, Xuemin Shen. IEEE Transactions on Vehicular Technology, 2020

392 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/61b559cb6d4c843d19e7f5340b88d018bb8594cc

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Citations in the corpus

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “In a similar line, a study in [179] considered an aerial cloudlet at UAV to carry out the computations for ground users.”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, methodology): “Optimization methods formulate the objective and constraints in a closed form, and the corresponding network management problems are solved using optimization algorithms [225]–[227].”
Resource Scheduling in Edge Computing: A Survey (2021, methodology): “, things), such as UAVs [40], CAVs [16], AR equipment [41], surveillance cameras for smart city [42], sensors for smart health [43],”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “A handful of works have considered energy-efficient UAV design for different applications such as UAV-assisted mobile edge computing and UAV-assisted backscatter communication [124], [158], [244], [245].”