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Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

Author

Listed:
  • Qijie Qiu

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

  • Lingjie Li

    (Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China)

  • Zhijiao Xiao

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

  • Yuhong Feng

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

  • Qiuzhen Lin

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

  • Zhong Ming

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
    Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China)

Abstract

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

Suggested Citation

  • Qijie Qiu & Lingjie Li & Zhijiao Xiao & Yuhong Feng & Qiuzhen Lin & Zhong Ming, 2024. "Joint UAV Deployment and Task Offloading in Large-Scale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm," Mathematics, MDPI, vol. 12(13), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:1966-:d:1421538
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    References listed on IDEAS

    as
    1. Yinghao Xu & Fukang Deng & Jianshan Zhang, 2023. "UDCO-SAGiMEC: Joint UAV Deployment and Computation Offloading for Space–Air–Ground Integrated Mobile Edge Computing," Mathematics, MDPI, vol. 11(18), pages 1-19, September.
    2. Tran Van Tung & To Truong An & Byung Moo Lee, 2022. "Joint Resource and Trajectory Optimization for Energy Efficiency Maximization in UAV-Based Networks," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    3. Dong Wei & Renjun Wang & Changqing Xia & Tianhao Xia & Xi Jin & Chi Xu, 2022. "Edge Computing Offloading Method Based on Deep Reinforcement Learning for Gas Pipeline Leak Detection," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    Full references (including those not matched with items on IDEAS)

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