Trends, Issues, and Challenges in the Domain of IoT-Based Vehicular Cloud Network

Abstract

Since the past few years, the vehicular network has gained significant attention because of its powerful potential applications such as traffic management, surveillance, and safety. The modern vehicles are equipped with smart sensors, actuators, and efficient communication devices such as GPS and embedded hardware. The vehicular network potential application outcomes are achieved by using vehicles onboard computational, communication, and storage capabilities with the help of cloud computing. Thus, the aim of the vehicular cloud network is to improve the traditional transportation system. The smart vehicle is equipped with smart devices such as computer on wheels, GPS devices, collision radars, and intelligent radio transceivers. The Internet of Things (IoT) and cloud computing have provided a solution to handle the increasing traffic congestion and vehicular safety. The cloud-based vehicular IoT network uses a number of software services which include sensor service, cloud service, and platform service. These services, when interacting with each other, provide a basic architecture to build traffic control and cloud-based vehicular data processing system. The IoT-based vehicular cloud network allows automobile manufacturers to innovate smart features into the vehicles with low cost, which also increases their market competitiveness. Automobile companies are utilizing cloud services from different cloud providers to support various service-level agreements. Since more and more vehicles are equipped with sensors that can access the Internet, vehicular services are combined with different cloud services to map, encapsulate, and aggregate the vehicular data to form the vehicular network platform. With the increasingly growing data in the cloud-based vehicular network, there are fundamental engineering challenges such as big data collection, data analysis for traffic management, real-time decision-making, and the ability to understand each other’s application formats and service-level agreement templates. The vehicular network has combined various technologies to handle these issues, such as machine learning, artificial intelligence, database management, and data mining. Similarly, cloud interoperability issues arise to support heterogeneous programming interfaces, programming languages, data models, and operating systems in an efficient and reliable manner. With the advancement in the mobile communication system, the vehicular cloud network can facilitate the scalable system with a reduced cost, efficient routing, resource sharing, and monitoring in a secure and efficient manner. The IoT-based vehicular cloud network is a complex system of interconnected sensors and communication tools and cloud platform. We can divide such a system into a number of subsystems and dimensions which include traffic management, data information processing, and service routing. The cloud-based vehicular network layered these services into cloud computing three distinct dimensions that include software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). In this chapter, we study the advances in the use of IoT in the transportation system via a cloud platform for developing an efficient vehicular cloud network. Thus, IoT and cloud computing in the automotive domain are studied. Similarly, the effectiveness of the vehicular network depends on its ability to handle large heterogeneous sensors and heterogeneous cloud platforms. Hence, the interoperability challenges at various cloud service levels and global standard issues are discussed. We also provided an analysis of how machine learning and blockchain can be applied to IoT-based vehicular cloud networks for self-learning and security mechanism, respectively.