Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing

Kai XIONG; Supeng LENG; Ke ZHANG; Hao LIU

doi:10.11959/j.issn.2096-3750.2019.00108

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Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing

Theory and Technology | 更新时间：2024-06-05

- Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing
- Chinese Journal on Internet of Things Vol. 3, Issue 2, Pages: 20-27(2019)
- 作者机构：
  
  1. 电子科技大学信息与通信工程学院，四川成都 611731
  2. 北京市交通运行监测调度中心，北京 100161
  3. 北京市交通信息中心，北京 100161
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2019.00108
  CLC： TN929
- Published：30 June 2019，
  
  Published Online：2019-06，
- 稿件说明：
移动端阅览
KAI XIONG, SUPENG LENG, KE ZHANG, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing. [J]. Chinese journal on internet of things, 2019, 3(2): 20-27.
DOI：

KAI XIONG, SUPENG LENG, KE ZHANG, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing. [J]. Chinese journal on internet of things, 2019, 3(2): 20-27. DOI： 10.11959/j.issn.2096-3750.2019.00108.

摘要

随着智能交通的发展，自动驾驶、智能车载交互、安全预警等新型车载应用不断涌现，独立车辆依靠自身有限的计算资源难以运行这些种类繁多且具有大量计算需求和时延需求的应用。雾计算通过将计算任务分布在网络边缘的设备中，运用虚拟化、分布式计算和并行计算技术，使用户能够按需动态地获取计算能力、存储空间等服务。将雾计算架构应用于车联网能够有效缓解计算量大、低时延车载应用与车辆有限且不均的资源分布之间的矛盾。从分析车—车通信、车—基础设施通信以及车辆时延容忍网络通信的信道容量入手，建立车联网异构接入的多业务资源优化模型，通过联合调度各类车联雾资源，实现智能交通应用的高效处理。仿真结果表明，所提出的强化学习算法能够有效地应对异构车联雾架构下的资源优化。

Abstract

With the development of intelligent transportation and the constant emergence of new vehicular on-board applications

such as automatic driving

intelligent vehicular interaction and safety driving.It is difficult for an independent vehicle to run a wide variety of applications with a large number of computing needs and time delay needs relying on its own limited computing resources.By distributing computing tasks in devices on the edge of the network

fog computing applies virtualization technology

distributed computing technology and parallel computing technology to enable users to dynamically obtain computing power

storage space and other services on demand.Applying fog computing architecture to Internet of vehicles can effectively alleviate the contradiction between the large computing-low delay demands and limited vehicular resources.By analyzing the channel capacity of vehicle-to-vehicle communication

vehicle-infrastructure communication and vehicle-time-delay tolerant network communication

an optimization model of heterogeneous access to multi-service resources for the Internet of vehicles was established

and various vehicle-to-fog resources were jointly dispatched to realize efficient processing of intelligent transportation applications.The simulation results show that the proposed reinforcement learning algorithm can effectively deal with the resource allocation in the heterogeneous vehicular fog architecture.

关键词

车联网车联雾车辆时延容忍网络Q学习算法资源分配

Keywords

Internet of vehicles (IoV)vehicular fogvehicular delay tolerant network (VDTN)Q-learning algorithmresource allocation

references

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XIONG K, LENG S P, HU J ,et al. Smart network slicing for vehicular fog-RAN[J]. IEEE Transactions on Vehicular Technology, 2019,68(4): 3075-3085.

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