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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Reinforcement learning-based approach for maximizing service fairness in emergency drone networks in disaster areas

Li Huaicheng
Peng Jian
Huang Wen
Shen Qunli
Liao Sirui
College of Computer Science, Sichuan University, Chengdu 610065, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0498

Abstract

Existing methods for Unmanned aerial vehicle (UAV) -based emergency communication services in disaster areas optimize network performance under global environmental information. However, these methods suffer from low networking efficiency and unbalanced resource allocation, which hinder the maintenance of stable communication services in dynamic disaster environments. As a result, some users may not receive timely rescue. This study addressed the problem of maximizing UAV communication quality. The method modeled the problem as a Partially Observable Markov Decision Process (POMDP) and designed a deep reinforcement learning-based approach to optimize UAV path planning and resource allocation. The method used network throughput as the service quality metric and Jain's fairness index as the balancing criterion. A reward function mechanism based on objective decoupling was developed, and a parameterized deep graph reinforcement learning network was constructed to achieve joint optimization of UAV trajectory planning and resource allocation. Extensive comparative experiments were conducted under 16 different simulation conditions. The proposed method significantly outperformed four baseline methods, improving the fairness index by 9.6% and demonstrating effectiveness across multiple performance metrics.

Foundation Support

国家自然科学基金资助项目(82474394)
四川省重点研发计划(2023YFG0112,2023YFG0115)
四川省省级工业发展资金产业基础攻关任务项目(2023JB06)
四川大学自贡市合作项目(2022CDZG-6)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0498
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-14] Accepted Paper

Cite This Article

李槐城, 彭舰, 黄文, 等. 基于强化学习的灾区应急无人机网络服务公平性最大化方案 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0498. (Li Huaicheng, Peng Jian, Huang Wen, et al. Reinforcement learning-based approach for maximizing service fairness in emergency drone networks in disaster areas [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0498. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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