Vehicular Communications最新文献

Identity and access management frameworks address user access rights and data governance for organizations, vendors and users. In response to the problems associated with centralized authorities (e.g. single point of failure, limited scalability, lack of user control), new identity management models have emerged, such as Self-Sovereign Identity (SSI), which relies on verifiable data registers to validate Decentralized Identifier (DIDs) and can be achieved in many different ways, e.g. through Distributed Ledger Technology (DLT), distributed databases or other decentralized systems. The main goal of SSI is to enable users to take control of managing their data shared with different services. In this paper, we examine a possible application of the SSI concept to aerial base station (ABS)- integrated networks. The paper presents the effective use of DID implementation to provide a secure and decentralized way to create, associate and verify credentials and identities of ABSs, ensuring secure communication between Ground Base stations (GBSs) and other nodes in the network in a multi-operator scenario. In the numerical results, the average values of various metrics (namely, the average credential presentation time, the average credential offer time, the average DIDcomm connection creation time, the average DIDcomm signing time, and the average DIDcomm revoke credential time) related to credential operations in a DID management system are given for three different number of requests (50 K, 75 K, and 100 K). We have also provided the values of the different status codes that occurred in 100 K operations in the same DID management system. Towards the end of the paper, a comparison is made between SSI-based and Non-fungible token (NFT)-based blockchain solutions, also discussing the challenges and future directions of SSI solutions in the context of ABS-integrated networks.

Unmanned Aerial Vehicles (UAVs) can effectively and reliably complete aerial tasks in various fields. However, the high-speed mobility of UAVs and the 6G ultra-dense heterogeneous network architecture in future will lead to frequent handover of UAVs during aerial tasks in cellular networks, increasing the potential service interruption rate. Therefore, there is an urgent requirement to address the lack of service adaptability in the overall time-space dimension. To achieve the seamless handover in highly dynamic and stochastic scenarios, we firstly transform the handover problem into a stable matching model. We then extend the stable matching of single time slot to the overall time-space dimension, and transform the dynamically changing time-space information into the preference relations evolution. By flexibly adapting the evolution of the preference lists to current network topology, we propose a Dynamic Stable Matching based Adaptive Handover (DSMAH) algorithm to find stable matching efficiently in a dynamic environment. Simulation results show that the proposed algorithm can achieve a better trade-off between communication quality, handover frequency, and convergence speed and significantly improves the stability of the cellular-connected network as compared to benchmark schemes. The proposed scheme not only effectively addresses the challenges posed by frequent handovers and ping-pong effect, but also shows its notable advantages in dynamic environments.

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

Nowadays, the increase in the number of vehicles on the roads has brought about several problems such as an increase in traffic congestion and, consequently, in polluting emissions. These problems are especially severe in urban environments. It is crucial to perform a sustainable urban mobility plan to improve the traffic and therefore, reduce the negative impacts caused by traffic jams. To this end, this paper presents a smart mobility plan that employs a collaborative driving strategy. Each vehicle tries to infer traffic conditions using its own status and the information shared by other peers. Using a fuzzy logic approach, vehicles perform decisions in accordance with the traffic levels inferred in real time. The designed mobility plan has been tested through a simulation environment and considering two types of urban areas in a typical European city (a peripheral area and a more congested city centre). If we compare the performance of traffic with and without the system designed, with our approach average speeds increase by up to 11.20 % and CO₂ emissions are reduced by up to 12.27 %. Thus, our results show that the mobility plan has helped to enhance the ability of cars to be able to solve problems caused by traffic congestion and traffic jams.

Every year, millions of people lose their lives due to road accidents, and countless others suffer from severe injuries. Moreover, these accidents cause economic losses worldwide. Primary reasons for these accidents include over speeding, doziness, distracted driving, drugs, and psychoactive substances. Many efforts are made to automate vehicles to save losses. Autonomous vehicles are expected to revolutionize transportation by reducing the likelihood of accidents resulting from human mistakes, increasing efficiency, and reducing congestion. Like any other computer-based system, autonomous vehicles can be susceptible to attacks that could compromise their safety, security, and privacy. Denial-of-Service (DoS) attack is a cyberattack that aims to disturb a network's typical or standard functioning. A successful DoS attack could cause the autonomous vehicle to malfunction or stop functioning altogether, leading to accidents or other safety risks. To mitigate the risk of DoS attacks, protective measures that have been proposed include encryption, firewalls, and intrusion detection systems. The previous approaches to detect and prevent Denial-of-Service (DoS) attacks in autonomous vehicles are associated with imprecise and inaccurate results and high computational costs. The main goal of this research is to present a novel approach that utilizes fuzzy logic to effectively detect and mitigate Denial of Service (DoS) attacks. The proposed approach has achieved 99.4% detection and prevention rate by attaining optimal levels of security with testing error upto 0.6%. Furthermore, comprehensive execution and validation of security measures have been conducted by adopting the attack surface and rules representation. The presented approach delivers more precise and accurate results while maintaining a lower computational cost than existing methodologies. In future the proposed scheme can be used for detection of other attacks in autonomous vehicles by incorporating relevant input variables and their respective ranges.

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

With the constant development of wireless communication technologies, the Vehicular Ad-hoc Networks (VANETs) will become an inevitable paradigm for intelligent transportation systems. Privacy-preserving and authentication security has been a core concern in VANETs, and pseudonym-based and group signature-based privacy-preserving authentication schemes have been widely proposed. However, most research schemes rely excessively on the Trusted Authority, and vehicle request and authentication information is almost transparent in front of TAs, while they cannot guarantee the security of vehicle request information after it is intercepted. To address these issues, this paper proposes a lightweight privacy-preserving authentication scheme that mitigates TA dependency, aiming to achieve mutual privacy protection and authentication between vehicles and TAs in VANETs by improving the oblivious transfer algorithm. The security analysis proves that this scheme has good privacy protection and attack resistance, and in addition, the performance simulation and evaluation show that this scheme spends lower computation and communication overheads while ensuring lower authentication delay and packet loss rate, which is a better overall performance compared with other schemes.

The recent advancement of smart UAVs (Unmanned Aerial Vehicles) allows its applicability to wide-range fields covering time-critical reports, emergent item delivery, virtual emotion services and so on. In particular, the surveillance can be supported by a fleet of smart UAVs in cooperation with various system components including sensors, mobile robots, autonomous vehicles and so on. Additionally, it is crucial to consider the reconfigurable capabilities of smart UAVs leveraging for rapid movement in disregarded airspace. In this paper, we introduce a reconfigurable UAV-enabled 3D sustainable surveillance in classified air-spaces to provide energy-efficient monitoring and security surveillance enforcement. We formally define a problem to maximize the reconfigurable UAV-aided sustainable surveillance efficiency with optimal height in 3D classified air-spaces avoiding excessive resource consumption and waste. To solve this issue, two different schemes of Lined-Up and Zig-Zag are proposed. Furthermore, we analyze their performances based on the gained numerical results through expanded simulations with detailed discussions.

The significant contribution of human errors, accounting for approximately 94% (with a margin of $\pm 2.2\text{\%}$), to road crashes leading to casualties, vehicle damages, and safety concerns necessitates the exploration of alternative approaches. Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human drivers with advanced computer-aided decision-making systems. However, for AVs to effectively navigate the road, they must possess the capability to predict the future behaviour of nearby traffic participants, similar to the predictive driving abilities of human drivers. Building upon existing literature is crucial to advance the field and develop a comprehensive understanding of trajectory prediction methods in the context of automated driving. To address this need, we have undertaken a comprehensive review that focuses on trajectory prediction methods for AVs, with a particular emphasis on machine learning techniques including deep learning and reinforcement learning-based approaches. We have extensively examined over two hundred studies related to trajectory prediction in the context of AVs. The paper begins with an introduction to the general problem of predicting vehicle trajectories and provides an overview of the key concepts and terminology used throughout. After providing a brief overview of conventional methods, this review conducts a comprehensive evaluation of several deep learning-based techniques. Each method is summarized briefly, accompanied by a detailed analysis of its strengths and weaknesses. The discussion further extends to reinforcement learning-based methods. This article also examines the various datasets and evaluation metrics that are commonly used in trajectory prediction tasks. Encouraging an unbiased and objective discussion, we compare two major learning processes, considering specific functional features. By identifying challenges in the existing literature and outlining potential research directions, this review significantly contributes to the advancement of knowledge in the domain of AV trajectory prediction. Its primary objective is to streamline current research efforts and offer a futuristic perspective, ultimately benefiting future developments in the field.

Ambient backscatter communication (AmBC) has recently attracted a lot of attention as a solution for next-generation internet of things (IoT) applications due to its capability to interact with peer devices with extremely little power usage (in μW). Security being a major concern in IoT applications, we look at the AmBC system from a physical layer security standpoint. In this paper, we focus on a realistic scenario in which numerous tags, a multi-antenna reader, and a multi-antenna eavesdropper are present, and the nodes are in motion concerning each other. The influence of speed, estimation error, number of antennas at nodes, and number of tags on the AmBC system's secrecy performance in terms of secrecy outage probability and intercept probability is investigated. Finally, the theoretical findings are supported with Monte-Carlo simulations.