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
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Zusammenfassung: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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2968343