App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 9

Deep reinforcement learning-based UAV crowdsensing: explicitly coupled scheduling strategy for multimodal Fusion gain and resource consumption

Yang Guisonga
Wu Zhiguoa
He Xingyub
a. School of Optical-Electrical and Computer Engineering, b. College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0026

Abstract

To address the strong coupling challenge between sensing accuracy and resource consumption in UAV Crowdsensing (UCS) for smart traffic scenarios, this paper proposed a dynamic scheduling method based on the Coupled Gain-Resource Deep Q-Network (CGR-DQN) . Unlike traditional separated optimization strategies, this method constructed a two-tier collaborative architecture comprising sensing nodes and the crowdsensing platform. It introduced a marginal gain coefficient to quantify node value and designed a composite reward function that integrates fusion accuracy with resource consumption. Based on this, the CGR-DQN intelligent decision engine can adaptively search for the optimal solution between multi-modal data value and physical resource consumption within a Markov process. Simulation results demonstrate that the proposed scheme achieves efficient physical resource management while ensuring high accuracy in traffic flow prediction. Quantitative results indicate that the system reduces the comprehensive energy consumption per task cycle to approximately 12.6 kW·h, stably controls the average CPU utilization of edge computing nodes at 55%, and achieves a marginal benefit of resource input of 0.31. Compared with PPO and traditional heuristic algorithms, the proposed method exhibits superior performance boundaries and engineering robustness in complex dynamic environments, such as communication-constrained scenarios and adverse weather conditions.

Foundation Support

国家自然科学基金资助项目(61602305,61802257)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0026
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 9

Publish History

[2026-05-26] Accepted Paper

Cite This Article

杨桂松, 吴志国, 何杏宇. 基于深度强化学习的无人机群智感知：多模态融合增益与资源消耗的显式耦合调度策略 [J]. 计算机应用研究, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0026. (Yang Guisong, Wu Zhiguo, He Xingyu. Deep reinforcement learning-based UAV crowdsensing: explicitly coupled scheduling strategy for multimodal Fusion gain and resource consumption [J]. Application Research of Computers, 2026, 43 (9). (2026-06-02). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0026. )

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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