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面向车联网环境的以服务为中心的异构车载网络建模

Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments.

机构信息

Department of Cybersecurity, Amman Arab University, Amman 11953, Jordan.

Faculty of Information Technology, Al Istiqlal University, Jericho 4728, Palestine.

出版信息

Sensors (Basel). 2022 Feb 7;22(3):1247. doi: 10.3390/s22031247.

Abstract

Heterogeneous vehicular communication on the Internet of connected vehicle (IoV) environment is an emerging research theme toward achieving smart transportation. It is an evolution of the existing vehicular ad hoc network architecture due to the increasingly heterogeneous nature of the various existing networks in road traffic environments that need to be integrated. The existing literature on vehicular communication is lacking in the area of network optimization for heterogeneous network environments. In this context, this paper proposes a heterogeneous network model for IoV and service-oriented network optimization. The network model focuses on three key networking entities: vehicular cloud, heterogeneous communication, and smart use cases as clients. Most traffic-related data-oriented computations are performed at cloud servers for making intelligent decisions. The connection component enables handoff-centric network communication in heterogeneous vehicular environments. The use-case-oriented smart traffic services are implemented as clients for the network model. The model is tested for various service-oriented metrics in heterogeneous vehicular communication environments with the aim of affirming several service benefits. Future challenges and issues in heterogeneous IoV environments are also highlighted.

摘要

车联网(IoV)环境中的异构车辆通信是实现智能交通的新兴研究主题。由于道路交通环境中各种现有网络的异构性日益增强,需要进行集成,因此它是现有车辆自组网架构的演进。现有的车辆通信文献在异构网络环境的网络优化方面存在不足。在这种情况下,本文提出了一种面向服务的 IoV 异构网络模型和网络优化。该网络模型侧重于三个关键网络实体:车辆云、异构通信和作为客户端的智能用例。大多数面向交通相关数据的计算都是在云服务器上进行的,以做出智能决策。连接组件支持在异构车辆环境中以切换为中心的网络通信。面向用例的智能交通服务作为网络模型的客户端实现。该模型在异构车辆通信环境中针对各种面向服务的指标进行了测试,以确认几种服务优势。还强调了异构 IoV 环境中的未来挑战和问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5970/8840583/08c58ed6ef28/sensors-22-01247-g001.jpg

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