4.7 Article

5G-Enabled UAV-to-Community Offloading: Joint Trajectory Design and Task Scheduling

Journal

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 39, Issue 11, Pages 3306-3320

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2021.3088663

Keywords

Trajectory; Monitoring; Heuristic algorithms; Throughput; Edge computing; COVID-19; Approximation algorithms; Dynamic scheduling; Unmanned aerial vehicles; Task analysis; UAV; mobile edge computing; trajectory design; task scheduling; 5G communications

Funding

  1. National Key Research and Development Program of China [2018YFE0206800]
  2. Natural Science Foundation of China [62025105, 61971084, 62001073]
  3. Chongqing Talent Program [CQYC2020058659]
  4. National Natural Science Foundation of Chongqing [cstc2019jcyjmsxmX0208]
  5. Fundamental Research Funds for the Central Universities [2019SJ02]
  6. U.S. National Science Foundation [CCF-1908308]

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This paper introduces a 5G-enabled UAV-to-community offloading system by clustering multiple users into independent communities to maximize system throughput. By solving two subproblems, an auction algorithm and dynamic task admission algorithm are designed to ensure user truthfulness and system throughput maximization. Performance analyses demonstrate the effectiveness of the proposed algorithm in real-world scenarios.
Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

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