Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems

By Qiyu Hu, Yunlong Cai, Guanding Yu, Zhijin Qin, Minjian Zhao, Geoffrey Y. Li. IEEE Internet of Things Journal, 2019

326 citations recorded by the source corpus.

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified ${l}_{0}$ -norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ffd797d670726b024da2653f305f04b7c04919d1

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Citations in the corpus

Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “More importantly, UAV trajectory design, computation load allocation, and communication resource management are coupled in the MEC system [13], which makes the system”
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks (2021, background): “potential technologies, multi-access edge computing (MEC) and unmanned aerial vehicle (UAV) [9], [10], have been”
Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing (2020, background): “[22], Jeong et al.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “To address this problem, UAVs with highly controllable mobility can be used as the flying cloudlets to achieve more efficient computation offloading for the users by moving significantly closer to them [154], [250]–[255].”
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (2019, methodology): “The new setup by utilizing UAV to assist computing in MEC systems poses new opportunities to solve the challenges in communication and computation design, and several prior related works have been done for this [18]- [28].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “In [133], the delay minimization among all users was studied by jointly optimizing the UAV trajectory, the ratio of offloading tasks and the user scheduling.”