App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 7

Strategy on trusted task offloading for Internet of Vehicles based on multi-agent deep reinforcement learning

Wang Yali1,2
Lou Shihao1
1. College of Computer & Information Engineering, Henan Normal University, Xinxiang Henan 453000, China
2. Engineering Laboratory of Intellectual Business & Internet of Things Technologies, Henan Normal University, Xinxiang Henan 453000, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0546

Abstract

Due to the limitation of the storage and computing resources of the Internet of Vehicles devices, they usually need to offload data to edge nodes for processing, but the potential threats of malicious edge nodes are unavoidable. Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed, this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles, and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility, so as to help the terminal devices select reliable edge nodes for task offloading. At the same time, this paper modeled the offloading strategy as the time delay and energy consumption minimization problem under the reputation constraint, and the multi-agent deep deterministic policy gradient algorithm was used to solve the approximate optimal solution of the NP-Hard problem. The edge server received rewards based on the completion of task offloading, and then updated the reputation recorded on the blockchain based on this. Simulation experiments show that this algorithm reduces in terms of time delay and energy consumption by 25.58% to 27.44% compared with the benchmark testing schemes.

Foundation Support

国家自然科学基金资助项目(62072159)
河南省科技攻关资助项目(222102210011、232102211061)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0546
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 7

Publish History

[2024-01-24] Accepted Paper

Cite This Article

王亚丽, 娄世豪. 基于多智能体深度强化学习的车联网可信任务卸载策略 [J]. 计算机应用研究, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0546. (Wang Yali, Lou Shihao. Strategy on trusted task offloading for Internet of Vehicles based on multi-agent deep reinforcement learning [J]. Application Research of Computers, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0546. )

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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