4.4 Article

UAV-Aided Mobile Edge Computing Systems With One by One Access Scheme

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGCN.2019.2910590

Keywords

Mobile edge computing; UAV trajectory; bit allocation; power allocation; resource partitioning; TD-UAV scheduling

Funding

  1. National High Technology Project of China [2015AA01A703]
  2. National Thirteen Five Equipment Research Fund [614031103020]
  3. Cyrus Tang Foundation Endowed Young Scholar Program [SEU-CyrusTang-201801]
  4. Scientific and Technological Key Project of Henan Province [192102210246]
  5. China Post-Doctoral Science Foundation [2018M633733, 2018M633351]
  6. Scientific Research Foundation of Graduate School of Southeast University [YBPY1859]
  7. National Natural Science Foundation of China [61801435, 61671144, 61801170, 61372101, 61720106003]
  8. Scientific Key Research Project of Henan Province for Colleges and Universities [19A510024]

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In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to process the application tasks migrated from the terminal devices (TDs) for saving TDs' energy consumption. To tackle the huge volume of data at TDs, we propose a resource partitioning strategy scheme where one portion of bits at TD is computed locally and the remaining portion is transmitted to UAV for computing. Our goal is to minimize the total energy consumption of TDs by jointly optimizing bit allocation, resource partitioning, power allocation at TDs/UAV, TD-UAV scheduling, and UAV trajectory with One-by-One access scheme. Due to the non-convexity of the original problem with mixed integer variables, we decompose the problem into two sub-problems. Specifically, in the first sub-problem, the TD-UAV scheduling is obtained by solving dual problem with given UAV trajectory. In the second sub-problem, the UAV trajectory is obtained by using successive convex optimization techniques with given TD-UAV scheduling. Then, an iterative algorithm is proposed to optimize the TD-UAV scheduling and UAV trajectory alternately. Numerical results are provided to demonstrate the superiority of our proposed scheme over the benchmarks.

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