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

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.

33 pages, 7 figures, IEEE single-column journal

References in corpus (3)

• Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
• Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications
• Multi-Drone 3D Trajectory Planning and Scheduling in Drone Assisted Radio Access Networks

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• A Survey on Intelligent Computation Offloading and Pricing Strategy in UAV-Enabled MEC Network: Challenges and Research Directions
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• A Novel Energy-aware Design for a D2D Underlaid UAV-Aided Cognitive Radio Network
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