The effects of transport development on people’s lives are diverse, ranging from economy to tourism, health care, etc. Great progress has been made in this area, which has led to the emergence of the Internet of Vehicles (IoV) concept. The main objective of this concept is to offer a safer and more comfortable travel experience through making available a vast array of applications, by relying on a range of communication technologies including the fifth-generation mobile networks. The proposed applications have personalized Quality of Service (QoS) requirements, which raise new challenging issues for the management and allocation of resources. Currently, this interest has been doubled with the start of the discussion of the sixth-generation mobile networks. In this context, Network Slicing (NS) was presented as one of the key technologies in the 5G architecture to address these challenges. In this article, we try to bring together the effects of NS implications in the Internet of Vehicles field and show the impact on transport development. We begin by reviewing the state of the art of NS in IoV in terms of architecture, types, life cycle, enabling technologies, network parts, and evolution within cellular networks. Then, we discuss the benefits brought by the use of NS in such a dynamic environment, along with the technical challenges. Moreover, we provide a comprehensive review of NS deploying various aspects of Learning Techniques for the Internet of Vehicles. Afterwards, we present Network Slicing utilization in different IoV application scenarios through different domains; terrestrial, aerial, and marine. In addition, we review Vehicle-to-Everything (V2X) datasets as well as existing implementation tools; besides presenting a concise summary of the Network Slicing-related projects that have an impact on IoV. Finally, in order to promote the deployment of Network Slicing in IoV, we provide some directions for future research work. We believe that the survey will be useful for researchers from academia and industry. First, to acquire a holistic vision regarding IoV-based NS realization and identify the challenges hindering it. Second, to understand the progression of IoV powered NS applications in the different fields (terrestrial, aerial, and marine). Finally, to determine the opportunities for using Machine Learning Techniques (MLT), in order to propose their own solutions to foster NS-IoV integration.

中文翻译：

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5G 及其他网络中基于网络切片的 IoV 学习技术


交通发展对人们生活的影响是多方面的，从经济到旅游、医疗保健等。这方面已经取得了长足的进步，从而催生了车联网（IoV）概念的出现。这一概念的主要目标是依靠包括第五代移动网络在内的一系列通信技术，通过提供大量应用程序，提供更安全、更舒适的旅行体验。所提出的应用程序具有个性化的服务质量（QoS）要求，这给资源管理和分配带来了新的挑战性问题。目前，随着第六代移动网络讨论的开始，这种兴趣已经加倍。在此背景下，网络切片（NS）被提出作为 5G 架构中应对这些挑战的关键技术之一。在本文中，我们尝试汇总 NS 对车联网领域的影响，并展示其对交通发展的影响。我们首先回顾车联网中 NS 的最新技术，包括架构、类型、生命周期、支持技术、网络部件以及蜂窝网络内的演进。然后，我们讨论在这种动态环境中使用 NS 带来的好处以及技术挑战。此外，我们还对 NS 部署车联网学习技术的各个方面进行了全面回顾。随后，我们通过不同领域展示了网络切片在不同车联网应用场景中的运用；陆地、空中和海洋。 此外，我们还审查了车辆到一切（V2X）数据集以及现有的实施工具；此外，还简要总结了对车联网有影响的网络切片相关项目。最后，为了促进网络切片在车联网中的部署，我们为未来的研究工作提供了一些方向。我们相信这项调查将对学术界和工业界的研究人员有所帮助。首先，获得基于车联网的 NS 实现的整体愿景，并找出阻碍其实现的挑战。其次，了解车联网驱动的 NS 应用在不同领域（陆地、空中和海洋）的进展。最后，确定使用机器学习技术（MLT）的机会，以便提出自己的解决方案来促进 NS-IoV 集成。