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Task placement and resource allocation for UAV and edge computing supported transportation systems

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Abstract

Air-ground Integrated Networks (AGINs) supported by Mobile Edge Computing (MEC) have shown great potential in Intelligent Railway Systems (IRSs). In this paper, we investigate task placement, task replacement, and resource management issues in an UAV-supported IRS, utilizing Unmanned Aerial Vehicles (UAVs) as edge nodes to address tasks offloaded from ground Internet of Things (IoT) devices, thus to maximize the success rate of task execution. Considering the complexity and dynamics of transportation systems, we adopt a Multi-Agent Deep Deterministic Policy Gradients(MADDPG) based Deep Reinforcement Learning (DRL) algorithm to cope with the changing environmental conditions and evolving task requirements in the transportation system, and achieve maximization of objective function. Since MADDPG is designed for problems with only continuous variables, we tailor the output into discrete variables in training according to probabilities, and in the final decision making according to the rounding principle, and thus improve it for our problem with both discrete and continuous variables. Through simulations, we demonstrate that the improved MADDPG algorithm exhibits fast convergence characteristics, and also performs well in maximizing the task execution success rate of the system.

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Acknowledgements

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Funding

This work was supported in part by the Natural Science Foundation of China under Grant 62271391, in part by the Serving Local Special Scientific Research Project of Education Department of Shaanxi Province under Grant 21JC032, in part by the National Natural Science Foundation of China under Grant 62301447, in part by the Natural Science Foundation of Sichuan Province under Grant 2023NSFSC1377, in part by the National Natural Science Foundation of China under 62341121, and in part by Science Research Project of Hebei Education Department QN2022086.

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Authors and Affiliations

  1. Shaanxi Key Laboratory of Information Communication Network and Security, School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Jianbo Du, Jiaju Lv, Aijing Sun & Jing Jiang

  2. Chinese Academy of Space Technology, Beijing, 100094, China

    Jianjun Zhang

  3. School of Physical Education, Xi’an Fanyi University, Xi’an, 710105, China

    Jie Li

  4. The Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control, Ministry of Education, Xihua University, Chengdu, 610039, China

    Pengfei Du

  5. The College of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Jing Bai

Authors
  1. Jianbo Du
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  2. Jianjun Zhang
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  3. Jie Li
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  4. Jiaju Lv
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  5. Aijing Sun
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  6. Jing Jiang
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  7. Pengfei Du
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Contributions

Jianbo Du conduced system model and algorithm design and writing the main manuscript text; Jiaju Lv, Jie Li, Pengfei Du, and Jing Bai performed simulation; Aijing Sun and Jing Jiang conducted performance analysis, Jianjun Zhang has improved the algorithm and polished the manuscript.

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Correspondence to Pengfei Du.

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Jianbo Du and Jianjun Zhang are co-first authors.

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Du, J., Zhang, J., Li, J. et al. Task placement and resource allocation for UAV and edge computing supported transportation systems. J Supercomput 81, 192 (2025). https://doi.org/10.1007/s11227-024-06647-z

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