车联雾计算中的异构接入与资源分配算法研究

doi:10.11959/j.issn.2096-3750.2019.00108

物联网学报 ›› 2019, Vol. 3 ›› Issue (2): 20-27.doi: 10.11959/j.issn.2096-3750.2019.00108

车联雾计算中的异构接入与资源分配算法研究

熊凯¹,冷甦鹏¹,张可²,刘浩³

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

修回日期:2019-04-20 出版日期:2019-06-30 发布日期:2019-07-17
作者简介:熊凯（1991-），男，四川巴中人，电子科技大学博士生，主要研究方向为车联网资源分配、移动边缘计算和机器学习。|冷甦鹏（1973-），男，四川资中人，电子科技大学教授、博士生导师，主要研究方向为物联网、车联网、新一代宽带无线网络、无线自组织网、智能交通信息系统的资源管理、介质访问控制、路由、组网与互联、智能算法理论及技术应用等。|张可（1974-），男，河南新乡人，博士、研究员，北京市交通运行监测调度中心副主任，主要研究方向为智能交通技术研究与应用、北京市综合交通运行监测服务和运行分析。|刘浩（1977- ），男，四川资中人，博士，北京市交通信息心副主任，主要研究方向为智能交通技术交通建模和交通仿真等。
基金资助:
国家重点研发项目(2018YFC0807101);国家自然科学基金重点资助项目(61731006);四川省科技计划项目(2019YFH0007);欧盟地平线2020计划项目COSAFE(MSCA-RISE-2018-824019)

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

Kai XIONG¹,Supeng LENG¹,Ke ZHANG²,Hao LIU³

¹ School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
² Beijing Municipal Transport Operation Coordinate Center,Beijing 100161,China
³ Beijing Transportation Information Center,Beijing 100161,China

Revised:2019-04-20 Online:2019-06-30 Published:2019-07-17
Supported by:
The National Key R＆D Program of China(2018YFC0807101);The State Key Program of National Natural Science Foundation of China(61731006);Sichuan Province Science and Technology Program(2019YFH0007);The European Union’s Horizon 2020 Research(MSCA-RISE-2018-824019)

摘要/Abstract

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。

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

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.

Key words: Internet of vehicles (IoV), vehicular fog, vehicular delay tolerant network (VDTN), Q-learning algorithm, resource allocation

中图分类号:

TN929

熊凯,冷甦鹏,张可,刘浩. 车联雾计算中的异构接入与资源分配算法研究[J]. 物联网学报, 2019, 3(2): 20-27.

Kai XIONG,Supeng LENG,Ke ZHANG,Hao LIU. 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.

图/表 4

参考文献 16

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车联雾计算中的异构接入与资源分配算法研究

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

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