Connected Vehicles in the Internet of Things

A connected car is an essential element of the Internet of Vehicles (IoV) vision that is a highly attractive application of the Internet of Things (IoT). The underlying technologies include Internet of Everything (IoE), artificial intelligence, machine learning, neural networks, sensor technologies, and cloud/edge computing. The connectivity between vehicles is through inter communication between sensors and smart devices inside the vehicles, as well as smart systems in the environment as part of the Intelligent Transportation Systems (ITS). In this chapter, the focus is on underlying concepts, architectures and relevant technologies. Types of connectivity and inter communication are also discussed. State of the art is articulated and, in the last sections of the chapter, future vision of vehicles’ connectivity is outlined and conclusions presented. The aim is that this chapter serves as a basis and sets the scene for detailed presentations on various aspects of connected vehicles that appear later in this book.

As the developing world positions itself towards implementing Smart Cities, concepts such as intelligent transport systems and spatial intelligence come to the fore. Smart Cities require contemporary pervasive and dynamic topologies and architectures to achieve spatial intelligence which is supported by intelligent transport systems. In such systems, vehicles can communicate with one another using Vehicle-to-Vehicle (V2V) communication models. V2V requires the availability of information on demand and anytime; also, that this information must be accessible in real time by the vehicles as they traverse through the city. Advanced information provision in Smart City environments enable vehicles to exchange information and make intelligent decisions on the roads. A whole array of both functional and non-functional requirements such as usability, aesthetics, security (access, availability and reliability), topology and information architecture, etc. need to be considered to achieve the desired level of spatial intelligence. Putting in place a network to handle the different network dimensions to achieve ubiquity can be significantly costly and beyond the reach of many of the developing world countries. Although, there have been some pockets of research on different aspects of vehicular networks, there is no significant research that brings a great deal of spatial intelligence together. This chapter aims to comprehensively explore the concept of spatial intelligence in the realm of V2V communication. Without carefully thought topologies and architecture, given the context, spatial intelligence in V2V communication cannot be realised. This chapter contributes to knowledge by exploring the different topologies and architectures in mobile agents (vehicles) where cost is one of the key inhibiting factors influencing the actual design.

Vehicle-to-infrastructure (V2I) communication enables a variety of applications and services, including safety, infotainment, mobility, payment, and so on, to be accessed and consumed. However, V2I requires seamless connectivity without having to worry about transitions between and across heterogeneous networks. In the next generation 5G heterogeneous networks (HetNet), which is a combination of multi-tier and multi-radio access technologies (RAT), the main challenges for V2I communication are having better network discovery, selection, and implementation of fast, seamless, and reliable vertical handover; maintaining QoS; and providing better quality of experience (QoE). To meet these challenges, considerable research contributions exist, and various generic solutions have also been proposed. In this chapter, the authors discuss the state-of-the-art of such technologies and V2I communication that consists of available radio access technologies, handover management, access network discovery and selection function (ANDSF), and media-independent handover (MIH)-based standard solutions for vertical handover. Besides, the chapter presents associated challenges with these technologies and highlights the possible research directions on multi-path technologies, SDN-based solutions, hybrid solutions, and 5G-enabled internet of vehicles (IoV). Primarily, the chapter discusses seamless V2I connectivity in HetNet and presents the state-of-the-art and future research directions in this domain.

The evolution of internet of things over the past few years has impacted on a new era of connected vehicles. Telecommunications, smartphones, and advancement of new technologies have given the ability for vehicles to easily access the internet and have communication capabilities in an internet of vehicles (IoV) scenario. Many factors are involved in the development, realization, and application of the IoV while, at the same time, establishing smart industrial environments. At present, deploying various kinds of infrastructures in smart cities is considered highly promising in order to improve road safety, reduce pollution, regulate traffic, and manage collaboration with other technological platforms. However, these platforms are useful to further support IoV services, including vehicular cloud platforms, data handling architectures, and network security, which are essential ambitions for developing countries. There are also numerous inherent challenges. Smart cities, which are complex integrated network systems, can use data captured by IoV objects and process it using artificial intelligence to provide intelligent transport system with intelligent components (such as intelligent traffic control, intelligent traffic lights, and intelligent objects) that are all connected together and also with people who use these vehicles. For a developing country such as Egypt, an efficient architecture that is compatible with the feasible technologies, user requirements, and market demands is currently considered mandatory for integrating internet of things with vehicular standards and technologies. This chapter addresses these concerns along with the emerging applications and challenges for the future.

With the ever-increasing demand for pervasive processing of big data from heterogeneous devices and networks, there is an increased demand for optimal networks in Vehicle-to-Vehicle (V2V) communication models. Effective communication of heterogeneous devices with non-identical communication consoles demands that the devices are connected with a well-marshalled software and hardware abstraction to facilitate seamless interaction and information interchange, between the technologies of different nature supplied by diverse vendors. In today’s information-intensive environments, processing of multi-dimensional data from heterogeneous smart devices in a networked environment can be a huge challenge. The type of network supporting V2V communication is critical to overall functionality of connected vehicle communication. Although there is a significant body of knowledge on different V2V networks, there is limited research on components for the topology and architectural arrangement for optimal vehicular networks. There is also a knowledge gap on what should influence topologies and routing mechanisms in vehicular networks. This chapter is as a result of research focusing on optimal designs for vehicular networks which culminate from a synthesis of knowledge in different contexts. This research brings to the fore the principles, protocols, topologies and storage options that underpin vehicular networks. The chapter proposes a conceptual framework articulating a basic architecture configuration that can be used in Mobile Ad hoc NETworks (MANET) with an emphasis on resource-constrained contexts.

With ever-growing number of vehicles on roads, traffic congestion is becoming a major problem in big cities around the world. Traffic congestion leads to pollution, time delays, excessive fuel consumption, financial losses and can severely disrupt normal human life. Conventional traffic management systems that rely on predecided traffic signal timings and pneumatic actuators are woefully inadequate in handling current traffic scenarios. Therefore, there is a pertinent need for a modern and intelligent overhaul of conventional traffic management systems and the introduction of such systems in modern smart cities. Intelligent traffic management systems are an ensemble of networks and systems integrated with each other to ensure optimum user commuting experience. They use a variety of advanced techniques such as reinforcement learning, Q theory, RFID tagging, IoT, IoV and local context awareness. Intelligent traffic management systems also help with safety issues, route optimization, delay reduction, and pollution control. This chapter explores conventional traffic management systems and their drawbacks, intelligent traffic management systems and recent advancements in them such as the introduction of reinforcement learning and local context-aware systems. This chapter aims to provide a glimpse of how intelligent traffic management systems can help drive smart cities of the future.

In recent years, people have pursued smarter and faster options for fast-paced modern lifestyles. This is in response to many technologies that have recently emerged. Vehicle tracking systems based on the Internet of Things (IoT) technology is one of these. This chapter aims to examine the IoT-based cutting-edge vehicular tracking systems. For this purpose, 30 peer-reviewed research publications, from 2014 to 2018, were selected and raw data was extracted. Selection was based on certain critical parameters such as: measuring attributes of the moving vehicle, sensors and actuators used for data obtaining in tracking devices, data transferring methods for transmission, networks and protocols utilized for communication, utilized stock for data storage, programming languages or systems, and algorithms utilized for raw data analysis. The investigation demonstrated that (i) a large portion of the IoT sensors and actuators were centered on the primary location tracking system in cloud data centers that can be handled remotely by retrieving real-time data, (ii) The GPS sensors widely use in vehicle tracking systems were based on the RFID technology, (iii) Wi-Fi networks were the most popular networks while GSM/GPRS and TCP/UDP protocols were the best transport layer protocols, (iv) most used storage method was observed as the cloud for smart vehicle tracking systems, and (v) Kalman filter was the most popular algorithm in vehicular tracking systems. Moreover, the most critical advantage of using IoT for tracking systems was the effectiveness, security, and intrusion of protection for the passengers. The security administration could monitor students by remotely tracking their RFID sensor tags or any IoT sensor embedded in the tracking unit. This chapter also reviews and provides relevant information for road traffic officers and related experts, correspondence technologists, and technological innovation researchers on the IoT-based smart vehicle tracking frameworks.

Charging stations networks and connected vehicles play a pivotal role in the advent of smart cities and smart grids. A cornerstone of these infrastructures is often a platform or a service that handles the copious amounts of data generated, processes and saves it for monitoring and analyses purposes. In this contribution, we present a software platform, that we named as REView, that automatically collects, analyses, and reviews live and recorded data from electric vehicles (EVs) as well as from EV supply equipment (EVSE or “charging stations”). It also provides a unified monitoring platform for infrastructures that are both modular and scalable. For analysis purposes, the data described in this chapter has been collected from the Western Australian Electric Vehicle Trial and the WA Charging Station Trial. A secure web portal was also designed with different viewing perspectives for electric vehicle users, charging station users and charging station operators. REView includes presentation of informative statistics about a user’s driving efficiency and the energy use of an EV; and then compares the collected data with the average of all other users’ similar data. It further includes a smartphone application for live monitoring and producing itemized billing. In this chapter, we discuss the development of REView, including mechanisms to generate and collect the information. Finally, we show and discuss various aspects of the visualized data itself, including charging time, charging duration, energy used, as well as utilization metrics of the charging infrastructure. We promote an open source approach to charging station software development. Our work also illustrates a single-software backend to handle multiple stations from different manufacturers, promoting competition and streamlining the integration of charging technologies into other devices. The results obtained from this network and platform have ultimately enabled us to perform quantitative investigations towards the driving and charging behaviours, as well as the overall electric vehicle trends around Perth in Australia.

The steady increase in the number of vehicles on the roads comes with an increased number of accidents and fatalities. The manufacturers’ interest in providing services to the driver (customer), along with safety applications, have contributed to connecting vehicles in networks on the fly (i.e., Ad Hoc networks), to provide certain services and information to the driver. These Ad Hoc networks consist of mobile vehicles that are located in a certain geographical zone and within a certain radius of each other, and communicate with each other or with road side units (RSUs) over the wireless medium. In addition, these mobile vehicles share some common characteristic, e.g., driving direction. These networks are well known as Vehicular Ad Hoc Networks (VANETs). From the extensive adoption and development of Internet of Things (IoT), and the integration of and convergence between VANETs and IoT, has emerged a new type of network known as Internet of Vehicles (IoV). VANETs, IoV and Intelligent Transportation Systems (ITS) have witnessed an explosive growth over the past two decades. This growth and the wide gamut of applications and services, these systems and networks have also increased the threats and attacks against these networks as well as raised many security and privacy concerns. In this book chapter, we describe the need for security in VANETs in terms of security requirements, current challenges in securing VANETs and present current security issues related to these networks. In particular, we deliver a comprehensive and up-to-date summary of threats and attacks in VANETs. Additionally, we present proposed solutions and countermeasures to mitigate these threats and attacks in order to secure and defend VANETs against them.

This chapter elaborates on the security issues of vehicular communication as a need for future evolution of Vehicular Ad Hoc NETworks (VANET) towards the Internet of Vehicles (IoV). The communication between connected vehicles is a potential issue of concern with respect to road safety, detecting traffic accidents, etc. in vehicular Ad Hoc network. In the Internet of Vehicles paradigm, each vehicle is equipped with a powerful multi-sensor platform, communication technologies, computation units, IP-based connectivity to the Internet and to other vehicles for enabling communication between connected vehicles as well as between vehicles and roads. Such communication must be protected from unauthorized message injection and message alteration. Hence a robust security solution for such networks must facilitate authentication verification of vehicles and integrity verification of the disseminated messages. In the first part of this chapter, a lightweight scheme for identification, authentication, and tracking of vehicles in hierarchical vehicular Ad Hoc network is elaborated. In the second part, a low overhead digital watermark-based vehicle revocation scheme to identify and revoke attackers of disseminated message is discussed. The combination of these two schemes ensures secure inter vehicle communication in vehicular Ad Hoc networks.

In this chapter, we propose to integrate cloud computing with Vehicular Ad Hoc NETworks (VANETs) so that the vehicles in the network can share network resources and avail of a variety of information collected by them to make useful decisions. We present an architecture that includes a cloud-based VANET. Then, we study the Internet of Vehicles (IoV) application management system in this cloud-based VANET. The proposed architecture facilitates the recognition of available resources in real time. In addition, it provides cloud-based IoV applications to cloud-based VANET enabled vehicles. The presented work also demonstrates a security algorithm suitable for a cloud-based VANET. We propose a distributed methodology for a secure vehicular network to communicate; and demonstrate the potential of our architecture for real-time access to IoV applications in cloud-based VANET environment.