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借助多无线接入技术的5G网络切片赋能车联网

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing.

作者信息

Sanchez-Iborra Ramon, Santa José, Gallego-Madrid Jorge, Covaci Stefan, Skarmeta Antonio

机构信息

Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain.

Department of Electronics, Computer Technology and Projects, Technical University of Cartagena, 30202 Cartagena, Spain.

出版信息

Sensors (Basel). 2019 Jul 13;19(14):3107. doi: 10.3390/s19143107.

DOI:10.3390/s19143107
PMID:31337087
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6679261/
Abstract

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.

摘要

车联网(IoV)是一个热门研究领域,它利用协同智能交通系统(C-ITS)与物联网(IoT)之间的协同作用,这能极大地受益于即将到来的5G技术发展。车联网中终端设备、应用和无线接入技术(RAT)的多样性需要新的网络方案来确保用户所需的服务质量(QoS)。为此,网络切片技术实现了流量区分,旨在确保流隔离、资源分配和网络可扩展性。这项工作填补了车联网5G网络切片的空白,并在实际车辆场景中进行了验证。它提供了具有完全流隔离的精确带宽控制,这对车辆关键系统至关重要。该开发基于分布式多接入边缘计算(MEC)架构,该架构为负责管理网络流量的虚拟化网络功能(VNF)的动态部署提供了灵活性。该解决方案能够集成异构无线技术,如蜂窝网络和在车辆领域具有潜力的特定物联网通信,创建隔离的网络切片而不会危及核心网络(CN)的可扩展性。验证结果展示了该框架在短且可预测的切片创建时间、性能/QoS保证以及高达一百万个连接设备的服务可扩展性方面的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/e281fcd8a0ab/sensors-19-03107-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/bf2664611c9b/sensors-19-03107-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/8027c847da4e/sensors-19-03107-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/aef533fea629/sensors-19-03107-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/72a4aed0e1ad/sensors-19-03107-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/6c300c7f7b01/sensors-19-03107-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/92ee6bf0acab/sensors-19-03107-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/41ffdbd0f349/sensors-19-03107-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/9c2ce0c16758/sensors-19-03107-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/4cb451b48769/sensors-19-03107-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/e281fcd8a0ab/sensors-19-03107-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/bf2664611c9b/sensors-19-03107-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/8027c847da4e/sensors-19-03107-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/aef533fea629/sensors-19-03107-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/72a4aed0e1ad/sensors-19-03107-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/6c300c7f7b01/sensors-19-03107-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/92ee6bf0acab/sensors-19-03107-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/41ffdbd0f349/sensors-19-03107-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/9c2ce0c16758/sensors-19-03107-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/4cb451b48769/sensors-19-03107-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3d9/6679261/e281fcd8a0ab/sensors-19-03107-g010.jpg

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