Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues Khondokar Fida Hasan * Tarandeep Kaur Md. Mhedi Hasan Yanming Feng School of EECS, QUT School of EECS, QUT Dept. of ICT, CoU School of EECS, QUT Brisbane, Australia Brisbane, Australia Cumilla, Bangladesh Brisbane, Australia Abstract—Over the past few years, we have experienced great and traffic management system was envisioned since the year technological advancements in the information and communi- 1990 by combining different sensors, and different mode cation field, which has significantly contributed to reshaping of applications under the technological evolution of Intelli- the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect gent Transportation System (ITS). At its earlier stage, this data, the data exchanged paradigm among vehicles is shifted technological endeavour evolved with the integration of co- from the local network to the cloud. With the introduction of employable and assisting correspondence innovation termed as cloud and edge computing along with ubiquitous 5G mobile Co-operative Intelligent Transportation System (CITS), which network, it is expected to see the role of Artificial Intelligence basically incorporates the Information and Communication (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge Technologies (ICT) with transportation infrastructure [5]. The of industrial revolution 4.0, it is necessary first of all to get a vehicle is enabled to communicate with other vehicles, road- clear understanding of the cutting-edge technologies that going to side infrastructures and other road entities by creating vehic- take place in the automotive ecosystem so that the cyber-physical ular ad-hoc networks (VANET). However, both of these two impact on transportation system can be measured. CIoV, which technological advancements lead the concept of Autonomous is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in and Connected vehicular technologies in parallel to enrich the transportation, and it has amassed great attention. It introduces idea of intelligence in the transport sector so as to increase cloud-based artificial intelligence and machine learning into the user comfort and road safety. transportation system. What are the future expectations of CIoV? With the evolution of the Internet of Things (IoT), in the To fully contemplate this architectures future potentials, and early 2010s, meanwhile, vehicles are being connected to the milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet Internet, aiming at providing ubiquitous access to information the security requirements of its practical implementation. Aiming alike to the drivers and passengers. This leads to another to that, this paper presents the evolution of CIoV along with the technological break-through termed as Internet of Vehicle layer abstractions to outline the distinctive functional parts of (IoV). the proposed architecture. It also gives an investigation of the Even though there has been a noticeable advancement in prime security and privacy issues associated with technological evolution to take measures. terms of automation and connectivity, still, it is not sufficient Index Terms—Cognitive Internet of Vehicles, Automotive, to reduce the road causalities to zero. Driving errors, as well as Transportation, Industrial Revolution 4.0, Security, Intelligent the drivers misjudgement being the prime reasons associated Transportation System with this causalities where a recent research [6] reveals that 90% of road accidents are caused by human factors. Take, for I. INTRODUCTION instance, fatigue while driving, overspreading, blocked line of The transport sector plays a key role in modern civilisation, sight, etc., are ranked among the most common factors that arXiv:1912.03356v1 [cs.NI] 20 Nov 2019 and over the past years, it has experienced rapid growth. cause accidents. This encourages the necessity of applying Ma- According to a recent survey; 2019s Motor Vehicle Census, chine Learning (ML), Neural Network (NN), Deep Learning there is an annual increase rate of 1.7% (average) in Australia, (DL), and Artificial Intelligence (AI) that can take control of with 19.5 million registered motor vehicles over a population wheel which can enable error-free driving, resulting to the idea of 25 million people [1]. This number for the USA and of Cognitive Internet of Vehicles (CIoV) shown in Fig.1. the UK stands at 281.3 million [2] and 39.4 million [3] The advancement that the CIoV offers toward the use of respectively. In turns, the whole world has over 1.4 billion internet and machine intelligence are also associated with new registered vehicles and come the year 2040, this figure is security risks and privacy issues, realising the transportation expected to shoot by double [4]. The critical issue is, as the need to address properly. Since different technologies are number of vehicles on roads increases, traffic-related problems playing a role in different layers, it is vital to understand such as traffic congestion, accident, and road fatalities are the existing vulnerabilities in the generic domain of those on the rise as well. To counter-attack this, a smart transport technologies and their application. This paper aims at giving an overview of the evolution of *Corresponding Author Email: k.fi[email protected] Cognitive Internet of Vehicles (CIoV) and its technological ITS systems. Mostly, all the Automated vehicles are equipped Smart Transport and IOV with a number of sensors, cameras, Lidar, Radar, etc., that Traffic Management System Connected to the Cloud collects raw data from the external environment. This data Combining Different Sensors in the form of Internet of Things and modes of applications then serves as the input to the sophisticated system software Cognitive IoV which is used in vehicles to decide for specific courses of Inception of Autonomous Car actions, such as, lane changing, acceleration and overtaking Internet of Vehicles other vehicles [9]. In connected vehicular technology, however, C-IOV Cooperative Intelligent Cloud-based Advanced Transportation the vehicles communicate with internal and external environ- Transportation System Solution using ML, AI, etc. ments utilising a different kind of communication technologies Intelligent Inception of LTE network predominantly wireless communication technologies. These Transportation System C-ITS Inception of VANET vehicles use wireless networks to create interactions within the Connected Vehicular Technology added with Evolution of Transportation System Transportation of Evolution devices built in the vehicle itself that is On-Board sensors and Wireless Networking e.g. VANET outside the vehicle; that is Vehicle to Vehicle (V2V) commu- Year 1990 2000 2010 2020 nications or Vehicle to Infrastructure (V2I) communications [10]. The concept of connected technologies fundamentally Fig. 1. Evolution of Cognitive Internet of Vehicles (CIoV). propelled the evolution of the transportation system to form the Internet of Vehicles (IoV), thus creating an opportunity to apply modern technological developments to apply on data related reviews. It presents a five-layer model to envisage the such as machine learning and artificial intelligence, to creates architecture of future transportation system to identify their insights on transportation management and to take measures distinctive functional parts. This paper also discusses security on providing better services. risks, including different threats, attacks, and vulnerabilities may associate with different layers to understand the measures B. Network Communication and Data Acquisition required. Layer-2 in CIoV is responsible for network-based com- II. COGNITIVE INTERNET OF VEHICLES (CIOV) AND munications among different transportation entities aiming LAYERED ARCHITECTURE at the transport-related data acquisition. A wide variety of With the advent of the ever-growing vehicular applications, communication interaction that takes place are defined in the technical challenges are growing too to meet the de- this layer. Broadly, all interaction can be classified as Intra- mands from both communication and computation. Without vehicular communication and Inter-vehicular communications. persuasive communication and computational support, a good Communication that takes place within the vehicle is termed number of foreseeing vehicular applications and services will as Intra-vehicular communication. Generally, smart vehicles only still stay in the idea phase and cannot be seen into are equipped with numerous sensors, such as sensors detect practice. the road condition, drivers fatigue, monitoring of the tire pressure, and autonomous control sensors, etc. [10]. The A. Sensing and Participation primary objectives of those sensors in vehicles are to monitor The Layer-1 of the structure represents all the techno- the internal operation of vehicle. Those sensors communicate logically evolved entities that are capable of sensing and with each other and take intelligent decision for the human communicating and also responsible for interacting within the driver. Smart vehicles use technologies that allow them to transportation system. Such entity includes smart vehicles and make decisions for the driver.