Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks

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.

I. Introduction

The convergence of smart solutions such as electric vehicles, autonomous cars, connected vehicles, unmanned aerial and marine vehicles, vehicular communications and more, has led to the emergence of Smart Mobility (SM) concept [1]. It is a vision of sustainable, technology-driven and citizen-oriented mobility with smart governance, resulting from the combination of smart technologies with mobility solutions [1], [2]. Intelligent Transportation Systems (ITS) and Internet of Vehicles are building blocks of the Smart Mobility ecosystem. ITS are the integration of information and communications technology into transport infrastructures and vehicles [3]. IoV results from the combination of Vehicular Ad-Hoc Networks (VANETs) with Internet connection, in order to enable vehicles to exchange information with their surroundings (vehicles, roads, human, sensors, and networks) [4]. According to the European Telecommunications Standards Institute (ETSI), the deployment of the ITS and the provision of corresponding services are not restricted to the road transport sector alone, but include other domains such as railways, aviation, and maritime as well [3]. On the grounds of this, we adopt the IoV system presented by [1]. As depicted in Fig. 1, IoV combines not only classical means of ground transportation such as vehicles, bikes, buses, and trains that constitute VANETs, but also air transportation means such as Unmanned Aerial Vehicles (UAVs), taxis and delivery drone forming Flying Ad-hoc Networks (FANETs), and maritime transportation systems such as Unmanned Marine Vehicles (UMVs), gondola, autonomous cargo ship and cruise ships constituting Ship Ad-hoc Networks (SANETs) [1] Fig. 1.

Smart Mobility Ecosystem [1].