A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges

As we move towards autonomous vehicles, a reliable Vehicle-to-Everything (V2X) communication framework becomes of paramount importance. In this paper we present the development and the performance evaluation of a real-world vehicular networking testbed. Our testbed, deployed in the heart of the City of Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe the testbed architecture and its operational modes. Then, we will provide some insight pertaining the firmware operating on the network devices. The system performance has been evaluated under a series of large-scale field trials, which have proven how our solution represents a low-cost high-quality framework for V2X communications. Our system managed to achieve high packet delivery ratios under different scenarios (urban, rural, highway) and for different locations around the city. We have also identified the instability of the packet transmission rate while using single-core devices, and we present some future directions that will address that.Comment: Accepted for publication to AdHoc-Now 201

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

Performance analysis of mmWave vehicle-to-vehicle communications : a stochastic geometry approach

Advanced sensory systems are the driving force behind autonomous vehicles. They have led to such vehicles producing and processing much more content-intensive visual data. It is foreseen that traffic consisting solely of autonomous vehicles will enable people and goods to travel more safely. Using millimetre wave (mmWave) frequencies for wireless communication between vehicles is a rising research topic, both to enhance safety and reliability of autonomous vehicles and to meet the data rate required by the content-intensive data-set produced by their advanced sensory systems. The main goal of this thesis is to make a stochastic analysis of the signal quality that can be achieved by vehicles when using mmWave frequencies for the wireless communication between them. To address this issue, the tools of stochastic geometry is used in this thesis. Firstly, stochastic geometry provides advanced mathematical properties to model the system fairly accurate and tractable. Secondly, resulting analytical model helps system designers to develop insights to understand relationships between system parameters. The first academic contribution of this thesis is a two-fold probabilistic connectivity analysis of an autonomous vehicle fleet for a single-lane road. In other words, it stochastically models the connectivity probability of the sensory data of the head and tail vehicles of the fleet shared over intermediate fleet vehicles by using a Poisson point process and geometric probability tools. The proposed model takes into account both the threshold of the obtained Signal-to-interference-plus-noise ratio in terms of the critical distance requirements and the beam misalignment issues caused by the lateral displacement of the vehicles in the lane. The second contribution of this thesis is a mean interference analysis for a typical receiver if the vehicles employ in-lane and closest vehicle routing schemes for a two-lane road. By comparing these two routing schemes, a strategy is proposed for different antenna beam widths and vehicle densities. Additionally, the distribution of autonomous vehicular traffic, that is modelled by the shifted Poisson point process, gains more realism by taking into account platooning-based headway distance requirements. The third contribution of this thesis is a derivation of the coverage probability and road spectral efficiency for multi-lane vehicle-to-vehicle communications, by taking into account the blockage impact of adjacent lane vehicles and antenna-placement dependent path loss behaviour under an in-lane routing scheme. Finally, all the analytical models are verified by Monte Carlo simulations

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G

6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies