V2X Applications Research Articles

Analysis of outage performance in a 6G‐V2X communications system utilising free‐space optical quantum key distribution

AbstractQuantum‐based technologies will provide system engineers with new capabilities for securing data communications. The UK AirQKD project has implemented a Free‐Space Optical Quantum Key Distribution (QKD) system to enable the continuous generation of symmetric encryption keys. One of the use cases for the generated keys is to secure Vehicle‐to‐Everything (V2X) communications. V2X applications would benefit from the certificate‐free security provided by QKD for a post‐quantum society. How FSO‐QKD could integrate into a V2X architecture is examined. An overview of V2X is provided with the role that FSO‐QKD could secure V2X data though some obstacles exist. One of the issues with 6G communications is the potential line‐of‐sight (LOS) considerations between the V2X devices. The modelling required for LOS is examined to analyse the outage performance of the building to infrastructure links in the 6G architecture. The results from the model show that further work is required if 6G LOS communications are going to be relied upon for future safety‐critical V2X applications.

TSM: Temporal segmentation and modules-based computation offloading using predictive analytics and NR-V2X

Recent increases in the usage of applications dependent on distributed computing have persuaded designers and entrepreneurs to utilize these solutions for diverse purposes, especially latency-constrained computation-intensive applications. Vehicular fog computing (VFC) is the innovative paradigm of distributed computing techniques, and therefore, a number of VFC offloading frameworks have been developed using AI-based advanced optimization procedures, with support from standards maintenance organizations like IEEE, 3rd Generation Partnership Project (3GPP), and some others. However, many of these strategies have not been adapted to specific application data and types, therefore these frameworks may function poorly despite their comprehensive offloading principles. Moreover, most computation offloading frameworks ignore the use of updated V2X protocols. We designed a temporal segmentation and modules (TSM)-based method specific for computation-intensive V2X applications that uses a four-tier hierarchy of resource-rich nodes and works in discrete time periods. TSM relies on status updates from previous time periods and uses predictive analytics to address the stochastic nature of vehicular networks using the latest 3GPP 5G V2X standards. Using an online modular computation offloading structure that heuristically manages the whole process, we were able to successfully and timely execute the latency-sensitive advanced vehicular applications. TSM supports computation-deficient devices in under a hundred milliseconds, makes use of smart vehicles’ processing units as fog nodes, and solves the optimization problem in short, discrete stages. We utilized Monte Carlo analysis, which confirmed that TSM outperformed the three other baseline methods.

Availability-aware multi-component V2X application placement

Vehicle-to-everything (V2X) applications are an integral part of the realization of autonomous and connected vehicles. Due to the large amount of data generated by the vehicles, the stringent delay requirements, and the sheer size of V2X applications, these applications are decomposed into several components that need to be placed in close proximity to requesting vehicles. Mobile-edge Computing (MEC) has been envisioned as a promising solution for vehicular task offloading. Despite the desirable properties of MEC, which can potentially satisfy the delay requirements, edge servers have limited computational capability and have a high cost. They also have availability concerns that should be taken into account. In this regard, Application Service Providers (ASPs) strive for a solution that can both satisfy the delay requirements of V2X applications and cut their expenses, which is reflected by the utilization of edge resources. To that end, this work formulates a Binary Linear Program (BLP) that minimizes the ratio of the experienced to the tolerable delay threshold and the utilization of edge resources under resource and V2X applications' availability constraints. To investigate the trade-off between these two objectives, they are parametrized by a coefficient that reflects their relative priorities. Additionally, the delay and the resource objectives are studied in light of different V2X applications' availability constraints. The experimental results show the effectiveness of our approach. On average, the delays of all V2X applications are satisfied with an acceptable level of edge server utilization.

A Hybrid Power-Rate Management Strategy in Distributed Congestion Control for 5G-NR-V2X Sidelink Communications.

The accelerated growth of 5G technology has facilitated substantial progress in the realm of vehicle-to-everything (V2X) communications. Consequently, achieving optimal network performance and addressing congestion-related challenges have become paramount. This research proposes a unique hybrid power and rate control management strategy for distributed congestion control (HPR-DCC) focusing on 5G-NR-V2X sidelink communications. The primary objective of this strategy is to enhance network performance while simultaneously preventing congestion. By implementing the HPR-DCC strategy, a more fine-grained and adaptive control over the transmit power and transmission rate can be achieved. This enables efficient control by dynamically adjusting transmission parameters based on the network conditions. This study outlines the system model and methodology used to develop the HPR-DCC algorithm and investigates its characteristics of stability and convergence. Simulation results indicate that the proposed method effectively controls the maximum CBR value at 64% during high congestion scenarios, which leads to a 6% performance improvement over the conventional DCC approach. Furthermore, this approach enhances the signal reception range by 20 m, while maintaining the 90% packet reception ratio (PRR). The proposed HPR-DCC contributes to optimizing the quality and reliability of 5G-NR-V2X sidelink communication and holds great promise for advancing V2X applications in intelligent transportation systems.

Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system

To achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging technology. The RIS-assisted mmWave massive MIMO vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of RIS-assisted mmWave V2X communications is though not exempt of challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted mmWave massive MIMO system to improving the safety and traffic efficiency of cooperative automated driving. The received signal of the vehicle is decomposed into four-dimensional tensors and study the channel, frequency and time attention (CFTA) algorithm using the low order tensor characteristics of mmWave channels. This algorithm can extract the frequency and time characteristics of the channel, and obtain the millimeter wave characteristic channel between the corresponding base station (BS) and the vehicles. The problem of jointly optimization of beamforming matrix at the BS and the phase shift matrix on the RIS is constructed and solved using deep reinforcement learning (DRL). Unlike traditional alternating optimization methods, the integration of DRL techniques into the optimal design of the system enables the observation of immediate rewards, learning from the environment and improving it to obtain optimal solutions to high-dimensional jointly beamforming optimization problems ultimately. The simulation results show that the proposed algorithm has meaningful performance compared with other methods.

A Compact Ultra-Wideband Monocone Antenna with Folded Shorting Wires for Vehicle-to-Everything (V2X) Applications

In this paper, a capacitively-fed, ultra-wideband (UWB), and low-profile monocone antenna is proposed for vehicle-to-everything (V2X) applications. The proposed antenna consists of a monocone design with an inner set of vias. Additionally, an outer ring is added with a small gap from the monocone and shorted with six folded wires of different lengths to extend the operating band. The proposed antenna covers the frequency range from 0.75 GHz to 7.6 GHz and has a 164% fractional bandwidth, with a gain value varying between 2 and 10 dBi. The dimensions of the antenna are 0.37λL × 0.37λL × 0.067λL. The antenna was fabricated using a 3D printer with low-cost polylactic acid plastic (PLA) material and then sprayed with aerosol copper nanoparticles. The efficiency was approximately 90% throughout the frequency bands of interest. Finally, the proposed antenna was installed on a vehicle and tested with an OBU (onboard unit) and a RSU (roadside unit) in the field. The results show a longer wireless communication range for V2X applications.

A comparative analysis of the semi-persistent and dynamic scheduling schemes in NR-V2X mode 2

Over the last years, the evolution of Vehicle-to-Everything (V2X) services from basic safety-related to enhanced V2X (eV2X) applications prompted the development of the 5G New Radio (NR)-V2X technology. Standardized by the Third Generation Partnership Project (3GPP) in Release 16, NR-V2X features a distributed resource allocation mode, known as Mode 2, that allows vehicles to autonomously select their transmission resources employing a Semi-Persistent Scheduling (SPS) or a Dynamic Scheduling (DS) scheme. The SPS approach relies on the periodic reservation of resources, whereas the DS scheme is a reservation-less solution that forces the selection of new transmission resources for every generated message. 3GPP standards do not indicate under which conditions each scheduling scheme should be used. In this context, this study analyzes and compares the performance of SPS and DS under different traffic types and Packet Delay Budget (PDB) requirements. Simulation results demonstrate that the SPS scheme represents the best solution for serving fixed size periodic traffic, whereas DS is more adequate for aperiodic traffic (of fixed or variable size). The study shows that the superiority of DS over SPS becomes more evident when tighter PDB requirements are considered, and that the performance of the DS scheme is independent of the PDB. It is also demonstrated that an adaptive scheduling strategy, which allows vehicles to select the scheduling scheme that best suits the type of generated traffic, is the best solution in mixed traffic scenarios where fixed size periodic traffic and variable size aperiodic traffic sources coexist.

5G-MEC Testbeds for V2X Applications

Fifth-generation (5G) mobile networks fulfill the demands of critical applications, such as Ultra-Reliable Low-Latency Communication (URLLC), particularly in the automotive industry. Vehicular communication requires low latency and high computational capabilities at the network’s edge. To meet these requirements, ETSI standardized Multi-access Edge Computing (MEC), which provides cloud computing capabilities and addresses the need for low latency. This paper presents a generalized overview for implementing a 5G-MEC testbed for Vehicle-to-Everything (V2X) applications, as well as the analysis of some important testbeds and state-of-the-art implementations based on their deployment scenario, 5G use cases, and open source accessibility. The complexity of using the testbeds is also discussed, and the challenges researchers may face while replicating and deploying them are highlighted. Finally, the paper summarizes the tools used to build the testbeds and addresses open issues related to implementing the testbeds.

A Roadside Millimeter-Wave Radar Calibration Method Based on Connected Vehicle Technology

Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project.

RetroV2X: A new vehicle-to-everything (V2X) paradigm with visible light backscatter networking

The Vehicle-to-Everything (V2X) technology has become essential with the increasing interest in intelligent transportation systems. It connects multiple vehicles and infrastructure by digitizing road traffic signs, signals, and other facilities and arranging sensing devices. However, conventional V2X solutions, such as Celluar Vehicle-to-Everything (C-V2X), have been slow to deploy due to their high cost, external power requirement, and dependence on Internet backhaul connectivity. Recently, Visible Light Backscatter Communication (VLBC) technology has emerged as a promising solution to overcome the practical issues of conventional V2X technologies. This paper presents RetroV2X, a protocol designed to support a networked VLBC-based V2X system that facilitates the transmission of road sensory data between vehicles and road infrastructure. We have designed several mechanisms, including backoff, virtual ID assignment, and excitatory carrier sensing, to handle different types of collisions, namely downlink, synchronous uplink, and asynchronous uplink collisions. Overhear and relay mechanisms are also designed to improve the performance at high vehicle densities. Our evaluation demonstrates that RetroV2X is capable of meeting the needs of V2X applications in typical rural road, town road, and highway scenarios with varying road sign densities, vehicle densities, and velocities.

DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics.

Threshold Cryptography-Based Secure Vehicle-to-Everything (V2X) Communication in 5G-Enabled Intelligent Transportation Systems

Implementing 5G-enabled Vehicle-to-Everything (V2X) intelligent transportation systems presents a promising opportunity to enhance road safety and traffic flow while facilitating the integration of artificial intelligence (AI) based solutions. Yet, security and privacy concerns pose significant challenges that must be addressed. Therefore, researchers have focused on improving the security and integrity of vehicle data sharing, with a particular emphasis on V2X application layer security and privacy requirements. This is crucial given that V2X networks can consist of vehicles manufactured by different companies and registered in various jurisdictions, which may only be within communication range for a few seconds. Thus, it is necessary to establish a trusting relationship between vehicles quickly. The article proposes a threshold cryptography-based key exchange protocol that meets the key requirements for V2X data sharing and privacy, including the rapid establishment of trust, the maintenance of vehicle anonymity, and the provision of secure messages. To evaluate the feasibility and performance of the proposed protocol, a tailored testbed that leverages the NS-3 network simulator, a commercial 5G network, and public cloud infrastructure is used. Overall, the proposed protocol provides a potential solution for addressing security and privacy concerns in V2X networks, which is essential for successfully implementing and adopting this technology.

Simulation-Based Cybersecurity Testing and Evaluation Method for Connected Car V2X Application Using Virtual Machine.

In a connected car, the vehicle's internal network is connected to the outside through communication technology. However, this can cause new security vulnerabilities. In particular, V2X communication, to provide the safety of connected cars, can directly threaten the lives of passengers if a security attack occurs. For V2X communication security, standards such as IEEE 1609.2 define the technical functions that digital signature and encryption to provide security of V2X messages. However, it is difficult to verify the security technology by applying it to the environment with real roads because it can be made up of other safety accidents. In addition, vehicle simulation R&D is steadily being carried out, but there is no simulation that evaluates security for the V2X application level. Therefore, in this paper, a virtual machine was used to implement a V2X communication simulation environment that satisfies the requirements for the security evaluation of connected cars. Then, we proposed scenarios for cybersecurity testing and evaluation, implemented and verified through CANoe Option.Car2X. Through this, it is possible to perform sufficient preliminary verification to minimize the variables before verifying security technology in a real road environment.

Application-centric Network Management - Addressing Safety and Real-time in V2X Applications

The current roadmaps and surveys for future wireless networking typically focus on communication and networking technologies and use representative applications to derive future network requirements. Such a benchmarking approach, however, does not cover the application integration challenge that arises from the many distributed applications sharing a network infrastructure, each with their individual topology and data structure. The paper addresses V2X networks as an important example. Crucial end-to-end application constraints including real-time and safety encourage a closer look at application interference and systematic integration. This perspective paper proposes a two-layer resource management that divides the problem into an application integration and a network management task. Valet parking with high-resolution infrastructure camera support is elaborated as a use case that overarches vehicle network and wireless network management. Experiments demonstrate the benefits of complementing the current network-centric management by an application-centric integration.

User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO

Sixth-generation mobile communication (6G) aims to further improve capacity and reliability by controlling the radio propagation environment. Millimeter wave (mmWave) high-frequency band communication offers large bandwidth at the cost of high attenuation, even for smaller distances. Due to this, fewer multiple input multiple outputs (MIMO) multiplexing is possible at the base station (BS). Distributed analog relay nodes with beamforming capability improve the received power and MIMO multiplexing of mmWave communication. Due to limited signal processing, the analog relay node cannot perform beam search and tracking using these mmWave reference signals. The beam search and tracking are possible at BS or user equipment at the cost of increased control overhead. To reduce this overhead and provide relay-based 6G communication, we propose user-driven relay beamforming methods which can obtain the benefits of a massive analog relay MIMO. Assuming vehicular-to-everything (V2X) as a 6G application, we considered a relay-beam control method that uses the user information (location, velocity, acceleration, and direction of the terminal) contained in intelligent transport systems (ITS) messages called Cooperative Awareness Message (CAM). Simulation results show that the proposed method significantly reduces the overhead and the obtains benefits of the massive analog-relay MIMO. Furthermore, the accuracy of CAM's location information, the control period, and the effects of UE mobility are evaluated and presented. The results also show that the proposed method can work effectively in future V2X applications.

Blockchain Integration for IoT-Enabled V2X Communications: A Comprehensive Survey, Security Issues and Challenges

In V2X (vehicle-to-everything) communication, there is a two-way communication among the vehicle(s) and other Internet of Things (IoT)-enabled smart devices around it that may change how we need to drive. Due to the advancement of Information and Communications Technology (ICT) and the rapid development of IoT in transportation, traditional applications are converted to intelligent applications. In V2X communications, the collected information from the IoT smart devices and other sources passes through low-latency, high-bandwidth, high-reliability links. With the future adoption of the 5th generation mobile network (5G) and beyond networks, V2X continues to produce a huge volume of data. However, collecting and storing data securely in blockchain-based storage are extremely needed for immutability and transparency. In this survey article, the convergence of IoT, V2X and blockchain technologies, and various security challenges and their countermeasures are discussed. Next, we discuss various V2X applications and their respective services. Moreover, IoT-V2X architecture and its enabling technologies are discussed in this article. In addition, we also provide a comprehensive analysis of various security mechanisms. Finally, we provide some important challenges and issues of Blockchain for Intelligent Transportation System (BITS).

Analysis and Design of Filtering Series-Feed Omni-Directional Antenna for V2X Application

In general, the vehicle-to-everything (V2X) series-feed antenna excites the spurious mode radiation in the adjacent bands and thus causes the strong mutual coupling between the vehicle antennas. To solve this problem, this article proposes a method to realize a filtering series-feed V2X antenna. This antenna consists of two radiators connected to a double-sided parallel strip line (DSPSL). The DSPSL operates as the series-feed structure and the first resonator. The radiators function as the second resonator. An equivalent filtering network is analyzed and derived. Then, based on the filter synthesis, the initial design parameters are obtained to realize a second-order bandpass response. However, the synthesis method only involves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> and efficiency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> ). The radiation performances are not concerned and satisfied. To solve this problem, the antenna currents are manipulated to generate the taper exciting. Thus, the upper sidelobe and the gain are improved. Moreover, to further enhance the out-of-band suppression level, three radiation zeros are generated by manipulating the current distribution on the DSPSL. The fabrication prototype demonstrates that the out-of-band suppression of the proposed antenna is better than 8.8 dB, which significantly reduces the mutual coupling.

Ultrasonic-Aided Fast-Layered Alternating Iterative Tensor Channel Estimation for V2X Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of millimeter-wave V2X communications is, however, not exempt from challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an ultrasonic-aided tensor channel estimation for V2X millimeter-wave massive MIMO systems to improve the safety and traffic efficiency of cooperative automated driving. At the receiver, the dimension of multidimensional complex V2X information is reduced by the subspace tensor decomposition model. In order to quickly track beam angle changes caused by vehicle position changes, the ultrasonic-aided direction of arrival (DOA) tracking method is adopted to provide information about the surrounding environment. Based on the ultra-high resolution quantization grid and adaptive iterative update of the dictionary matrix, the DOA of the ultrasonic signal can be tracked. The angle update information of the millimeter-wave signal can be obtained by converting the estimated angle information. Using the cost function with global characteristics and ultrasonic-aided DOA tracking, a fast-layered alternating iterative tensor algorithm is proposed for joint iterative channel estimation. Simulation results show that the proposed solution outperforms some advanced alternative methods.

BDTwin: An Integrated Framework for Enhancing Security and Privacy in Cybertwin-Driven Automotive Industrial Internet of Things

The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users’ digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.

Autonomous driving under V2X environment: state-of-the-art survey and challenges

Abstract As Vehicle-to-Anything (V2X) is viewed as emerging technology, which could improve the mobility and efficiency of urban traffic operations, and enhance the safety of drivers. Supported by the construction of vehicular network facilities, V2X has been applied to various urban traffic management systems. However, due to the limitation of wireless communication, outstanding issues on privacy and security in V2X applications are still urgent. The purpose of our studies is to provide a comprehensive review of V2X-aided autonomous driving. Firstly the standards and protocols on V2X technologies for mobile communication and high-definition map are provided. The research scenarios of V2X-aided automobiles on urban links and highways are discussed. Furthermore, the several challenges of legislation in different countries, privacy issues, and security are discussed. Finally, the research directions for V2X-aided autonomous driving are further given.