基于深度强化学习的多方式协同车联网边缘计算任务卸载
作者:
作者单位:

中国民用航空飞行学院

基金项目:

中央高校基本科研业务费基金项目(J2023-027);中国民航飞行技术与飞行安全重点实验室开放基金(No.FZ2022KF06);中国博士后科学基金(No.2022M722248)


Multi Mode Collaborative Internet of Vehicles Edge Computing Task Unloading Based On Deep Rein-forcement Learning
Author:
Affiliation:

Civil Aviation Flight University of China

Fund Project:

The Central University Basic Research Business Fee Fund Project (J2023-027); Open Fund of Key Laboratory of Flight Techniques and Flight Safety; CAAC(No. FZ2022KF06) and China postdoctoral science foundation( No. 2022M722248).

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    摘要:

    随着车联网技术的快速发展,边缘计算在车联网信息处理应用中的作用日益增强。然而,车联网中车辆设备的计算资源和存储能力有限,而车辆需要处理大量数据和复杂的计算任务,如何高效利用边缘计算资源是一个重要问题,单一的卸载方式可能无法满足场景的需求,需要考虑多种卸载方式的协同,以提高系统的整体性能和灵活性。针对以上挑战,提出了一种基于深度强化学习的多方式协同车联网边缘计算任务卸载方案。通过采用车辆对车辆(Vehicle to Vehicle,V2V)、车辆对车辆联盟(Vehicle to Vehicle Alliance,V2A)、车辆对路侧单元(Vehicle to Roadside Unit, V2R)以及路侧单元对基站(Roadside Unit to Base Station, R2B)等通信方式,实现了对计算密度大和时延要求高的任务的协同处理。为应对动态网络环境的复杂性,利用马尔可夫决策过程(Markov Decision Process,MDP)进行问题建模与优化,并引入深度强化学习来处理连续动作和状态空间。特别地,提出了MCTO算法,该算法能够高效适应车联网的动态环境,显著降低了整个车联网系统的时延。仿真结果表明,提出的MCTO算法具有良好的收敛性,并且在系统时延方面,相比其他强化学习算法有显著提升,整体性能提高了28.67%。

    Abstract:

    With the rapid development of Internet of Vehicles technology, edge computing plays an increasingly important role in information processing applications of Internet of Vehicles. However, the computing resources and storage capa-bilities of vehicle equipment in the Internet of Vehicles are limited, and vehicles need to process large amounts of data and complex computing tasks. How to efficiently utilize edge computing resources is an important issue. A single offloading method may not be able to meet the needs of the scenario. Consider the collaboration of multiple offload-ing methods to improve the overall performance and flexibility of the system. In response to the above challenges, a multi-mode collaborative vehicle network edge computing task offloading solution based on deep reinforcement learning is proposed. By using Vehicle to Vehicle (V2V), Vehicle to Vehicle Alliance (V2A), Vehicle to Roadside Unit (V2R) and Roadside Unit to Base (Roadside Unit to Base) Station, R2B) and other communication methods realize collaborative processing of tasks with high computing density and high latency requirements. In order to cope with the complexity of the dynamic network environment, the Markov Decision Process (MDP) is used for problem modeling and optimization, and deep reinforcement learning is introduced to handle continuous actions and state space. In particular, the MCTO algorithm is proposed, which can efficiently adapt to the dynamic environment of the Internet of Vehicles and significantly reduce the delay of the entire Internet of Vehicles system. The simulation results show that the proposed MCTO algorithm has good convergence and is significantly improved in terms of system delay compared with other reinforcement learning algorithms, with the overall performance improved by 28.67%.

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刘建华,魏金城,涂晓光.基于深度强化学习的多方式协同车联网边缘计算任务卸载[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-05-23
  • 最后修改日期:2025-01-12
  • 录用日期:2025-02-17

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