Md. Noor-A.-Rahim, Zilong Liu, Haeyoung Lee
et al.
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.
The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users’ identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.
Nowadays, advanced communication technologies are being utilized to develop intelligent transportation management and driving assistance. Through the ability to exchange traffic and infotainment information between road infrastructure and vehicles, vehicular ad-hoc networks (VANETs) promise to improve transport efficiency, accident prevention, and pedestrians comfort. The deployment of VANETs in the real world is based on the message’s correctness and timely delivery and assuredness of privacy protection and data security. In this regard, many researchers have conducted surveys and studies that present models and solutions related to the improvement of VANET from different aspects such as architectural design, networking, and data security. Motivated by these influences, this study presents a detailed survey of VANETs to provide a complete picture of particular VANET applications, networking, and challenges. None of them collected all data in one survey. VANET communication techniques and their improvements are the focus of this study. The contributions of this paper are as follows. First, a complete taxonomy of VANET wireless access techniques has been provided based on various parameters. Second, a detailed discussion and classification VANET services and applications are provided. Third, the challenges related to VANET according to the applicability area, data networking, and resource management are explored in detail. Based on this classification, a complete description of the challenges for each category, including the proposed solutions and development models, is provided to overcome such challenges. Finally, the integration of evolutionary technologies with VANET is comprehensively presented. In this regard, a thorough explanation is provided for each technology, including the challenges, solutions, and suggestions for further improvements. This study enables various users working in the vehicular networking domain to select one of the proposals based on its relative advantages.
With the emergence of communication services with stringent requirements such as autonomous driving or on- flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.
The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.
Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.
Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs' compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing Socially Compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An informal expert interview was also conducted to discuss the literature review results and identify critical research gaps and expectations towards SCAVs. Based on the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated using an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the significance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.
Alejandro Castilla, Saúl Fenollosa, Monika Drozdowska
et al.
Integrated Sensing and Communication (ISAC) design is crucial for 6G and harmonizes environmental data sensing with communication, emphasizing the need to understand and model these elements. This paper delves into dual-channel models for ISAC, employing channel extraction techniques to validate and enhance accuracy. Focusing on millimeter wave (mmWave) radars, it explores the extraction of the bistatic sensing channel from monostatic measurements and subsequent communication channel estimation. The proposed methods involve interference extraction, module and phase correlation analyses, chirp clustering, and auto-clutter reduction. A comprehensive set-up in an anechoic chamber with controlled scenarios evaluates the proposed techniques, demonstrating successful channel extraction and validation through Root Mean Square Delay Spread (RMS DS), Power Delay Profile (PDP), and Angle of Arrival (AoA) analysis. Comparison with Ray-Tracing (RT) simulations confirms the effectiveness of the proposed approach, presenting an innovative stride towards fully integrated sensing and communication in future networks.
Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. In this paper, we propose a new rotatable antenna (RA) model to improve the performance of wireless communication systems. Different from conventional fixed antennas, the proposed RA system can flexibly and independently alter the boresight direction of each antenna via mechanical or electronic means to exploit new spatial degrees-of-freedom (DoFs). Specifically, we investigate an RA-enabled uplink communication system, where the receive beamforming and the boresight directions of all RAs at the base station (BS) are jointly optimized to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the users. In the special single-user and free-space propagation setup, the optimal boresight directions of RAs are derived in closed form with the maximum-ratio combining (MRC) beamformer applied at the BS. In the general multi-user and multipath channel setup, we first propose an alternating optimization (AO) algorithm to alternately optimize the receive beamforming and the boresight directions of RAs in an iterative manner. Then, a two-stage algorithm that solves the formulated problem without the need for iteration is proposed to further reduce computational complexity. Moreover, we extend the channel model to incorporate polarization effects and frequency-selective fading while catering to antenna boresight rotation. Simulation results are provided to validate our analytical results and demonstrate that the proposed RA system can significantly improve the communication performance as compared to other benchmark schemes.
An accurate and robust localization system is crucial for autonomous vehicles (AVs) to enable safe driving in urban scenes. While existing global navigation satellite system (GNSS)-based methods are effective at locating vehicles in open-sky regions, achieving high-accuracy positioning in urban canyons such as lower layers of multi-layer bridges, streets beside tall buildings, tunnels, etc., remains a challenge. In this paper, we investigate the potential of cellular-vehicle-to-everything (C-V2X) wireless communications in improving the localization performance of AVs under GNSS-denied environments. Specifically, we propose the first roadside unit (RSU)-based cooperative localization framework, namely CV2X-LOCA, that only uses C-V2X channel state information to achieve lane-level positioning accuracy. CV2X-LOCA consists of four key parts: data processing module, coarse positioning module, environment parameter correcting module, and vehicle trajectory filtering module. These modules jointly handle challenges present in dynamic C-V2X networks. Extensive simulation and field experiments show that CV2X-LOCA achieves state-of-the-art performance for vehicle localization even under noisy conditions with high-speed movement and sparse RSU coverage environments. While focusing on AV localization, CV2X-LOCA also can extend to other C-V2X-equipped road users. The study results also provide insights into future investment decisions for transportation agencies regarding deploying RSUs cost-effectively.
A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people). Although localization and sensing (L&S) are envisioned as core functions of future communication systems, they have inherently different demands in terms of infrastructure compared to communications. Wireless communications generally requires a connection to only a single access point (AP), while L&S demand simultaneous line-of-sight propagation paths to several APs, which serve as location and orientation anchors. Hence, a smart city deployment optimized for communication will be insufficient to meet stringent L&S requirements. In this article, we argue that the emerging technologies of reconfigurable intelligent surfaces (RISs) and sidelink communications constitute the key to providing ubiquitous coverage for L&S in smart cities with low-cost and energy-efficient technical solutions. To this end, we propose and evaluate AP-coordinated and self-coordinated RIS-enabled L&S architectures and detail three groups of application scenarios, relying on low-complexity beacons, cooperative localization, and full-duplex transceivers. A list of practical issues and consequent open research challenges of the proposed L&S systems is also provided.
Amir Aghaei Anvigh, Yashar Khavan, Behrouz Pourghebleh
Vehicular Ad-hoc Networks (VANETs) have enabled intelligent transportation systems by facilitating communication between vehicles and roadside infrastructure. However, the current 5G and 4G networks that support VANETs have certain limitations that hinder the full potential of VANET applications. These limitations include constraints in bandwidth, latency, connectivity, and security. The upcoming 6G network is expected to revolutionize VANETs by introducing several advancements. 6G will provide ultra-fast communication with significantly reduced latency, enabling real-time and high-bandwidth data exchange between vehicles. The network will also offer highly reliable and secure connectivity, ensuring the integrity and privacy of VANET communications. Precise localization and sensing capabilities will be enhanced in 6G-based VANETs, enabling accurate positioning of vehicles and improved situational awareness. This will facilitate collision avoidance, traffic management, and cooperative driving applications. Moreover, integrating edge computing in 6G networks will bring computing resources closer to the edge, lowering response times and facilitating faster decision-making in time-critical scenarios. This paper explores the key features and capabilities of 6G technology and how it can revolutionize intelligent transportation, addressing challenges and opportunities for adopting 6G in VANETs.
Mahmoud A. Shawky, S. T. Shah, Mohammed Abdrabou
et al.
in VANETs, referred to as “cross-layer authentication”. This comprehensive survey thoroughly evaluates the state-of-the-art of crypto-based, PHY-layer-based, and cross-layer-based authentication methods in VANETs. Furthermore, this survey delves into integrating different sixth-generation (6G) and beyond technologies, such as reconfigurable intelligent surfaces (RIS) and federated learning, for enhancing PHY-layer authentication performance in the presence of active attackers. Furthermore, in-depth insights into the advantages of cross-layer authentication methods are presented, along with exploring various state-of-the-art VANET security techniques. A detailed technical discussion is provided on these advanced approaches, and it is concluded that they can significantly enhance the security of intelligent transportation systems, ensuring safer and more efficient vehicular communications.
Priyanka Kaushik, S. Rathore, Lakshay Sachdeva
et al.
Standard encryption cannot be utilized in practical communications due to time and storage limits. Dedicated lane keeping (DRL) is a technique that helps self-driving cars navigate congested roads by keeping them in a specific lane (CAVs). Researchers have developed separate networks for distinct types of mixed traffic to cut down on the time and effort typically spent on instruction and coordination. A deep reinforcement learning technique boosts the efficiency of each part and the entire fleet. There is a common misconception that the blockchain is a secure database for private information. A distributed database system in which nodes are directly connected to consensus mechanisms. To ensure data integrity, block blocks on a blockchain network use cryptography and other computer safeguards (such as smart contracts and time stamps). Because of its decentralized design, data storage facilitates collaboration. Digitally signed data records can also be checked to ensure they are accurate. Using hashes to connect individual blocks protects data against tampering by hackers. There is no need for a centralized authority or third party to verify the ledger’s accuracy because everyone can access it anytime. The blockchain allows for a transparent, trustworthy, and auditable system sharing information between entities. Like many other industries, transportation may benefit from the broader implementation of blockchain technology. Based on our findings, a state-run blockchain tailored to the transportation industry was developed and made available to the public. Because of blockchain technology, the car-sharing business model may need to be revised. Blockchain technology utilizes a distributed ledger to record transactions in a way that makes it impossible to alter the underlying data while still allowing for fast access for verification and auditing.
Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.