Vehicular Communications最新文献

This paper proposes a new architecture Social Vehicular Edge Computing (SoVEC) by integrating three domains: social network, vehicular ad-hoc network, and mobile edge computing. The users access various mobile applications and share various types of information on the social network during travel time. Using SoVEC three categories of social networks are generated based on the type of information shared among the users such as traffic information, professional information, and personal interests. To reach the destination in minimal time, this paper proposes an optimum route selection strategy based on TOPSIS method and genetic algorithm. The SoVEC is simulated using the network simulator Qualnet 7, and average delay, jitter, and throughput are determined. A case study of generating social network based on road traffic-related information is also demonstrated. Finally, the effectiveness of the proposed approach for selecting the optimum route is assessed, and the results present that the proposed method outperforms the existing algorithms.

The emergence of the Vehicular Ad-hoc Networks (VANETs) has greatly enhanced the efficiency and safety of transportation systems. However, due to the dynamicness and openness of vehicular networks, it is vulnerable for VANETs to confront various security threats. The dissemination of disinformation and the occurrence of attacks within these networks can result in severe damage and compromise the overall security of transportation systems. To mitigate such challenges, this paper presents an efficient certificateless aggregate signature scheme with dynamic revocation in VANETs. Taking use of the early-stopping factorial bitwise divisions (EFBD) algorithm, it achieves the invalid signatures tracking, and revokes the malicious vehicles dynamically. In addition, by combining the fingerprint counting bloom filter (FP-CBF), the trust assessment mechanism and the cloud server, it enables the vehicle to remedy its incorrect signatures. At last, through the comprehensive security and performance analysis, it demonstrates that this protocol enjoys improved communication security and computational efficiency.

Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

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.