Vehicular Communication Networks Research Articles

Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks

Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks

Retraction Note: Switching mode allocation in planning paths for vehicular network communication

Retraction Note: Switching mode allocation in planning paths for vehicular network communication

Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles

We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.

Application of Fuzzy Computing for VANET Routing

In the realm of vehicular ad hoc networks (VANETs), routing plays a pivotal role in ensuring efficient communication among vehicles and infrastructure. Traditional routing protocols often struggle to adapt to the dynamic and uncertain environment of VANETs, characterized by rapidly changing network topologies and unpredictable traffic conditions. In any case, the without a doubt solid and stunning nature of vehicular conditions presents superb difficulties for controlling shows, including unusual straightforwardness, changing association geologies, and dynamic traffic conditions [1, 2]. By incorporating fuzzy logic into routing algorithms, vehicles can make more informed decisions regarding route selection, considering factors such as traffic congestion, road conditions, and vehicle mobility patterns. This paper provides an overview of the key concepts of fuzzy computing and discusses its potential applications in VANET routing. Furthermore, it explores existing research efforts in this area, highlighting the benefits and challenges associated with fuzzy based routing approaches. Through a comprehensive review of the literature, this paper identifies current trends and future directions for leveraging fuzzy computing in VANET routing, aiming to improve the reliability, scalability, and efficiency of communication in vehicular networks.

Highly secured authentication and fast handover scheme for mobility management in 5G vehicular networks

The Fifth Generation (5 G) networks exhibit high flexibility and diversity in their design and deployment strategies. Transitions between base stations (BSs), heterogeneous networks (HetNets) and other cellular networks provide significant vulnerabilities and expose users to substantial risks associated with cybersecurity attacks. This article evaluates current handover authentication methods in the context of 5 G networks while proposing a set of security criteria for handover authentication. This study presents a novel authentication technique called SHK (Secure Handover Key) that utilizes SDNs (Software Defined networks). The proposed scheme integrates recycled lightweight dynamic key cryptography and combines security features such as perfect forward secrecy and robustness to leakages. The significance of the proposed approach is assessed in terms of computations, communications, signals, and energy costs on 5 G mobility applications and Vehicular Communication Networks (VCNs). The scheme employed in this study demonstrates enhanced security measures and improved changeover performance compared to conventional schemes.

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

INVISIBLE 2.0: An enhanced interference-based handover technique for visible light communications in vehicular networks

Since the last decade, Visible Light Communication (VLC) has represented a huge capacity to complement conventional Radio Frequency (RF) technologies in plenty of applications. The credibility of VLC in terms of power consumption has always been compared to RF. In this work, in addition to addressing this issue through a detailed power model, the co-existence of VLC and the Long-Term Evolution (LTE) cellular network in a vehicular environment has been empirically investigated. The coordination of data transmission among different technologies is managed through a novel vertical handover mechanism, namely INVISIBLE 2.0. This technique is triggered by the error probability to meet the 3GPP requirements on packet reception ratio while endorsing VLC as the preferred technology bearer. We demonstrate that INVISIBLE 2.0 achieves a more stable latency, and energy efficiency based on extensive simulation results.

An optimized energy management and load balancing system based on cluster head selection for the vehicular network communication

An optimized energy management and load balancing system based on cluster head selection for the vehicular network communication

Vehicle to Vehicle Communication for Crash Avoidance System

The Dramatic increase in the traffic flow raises demand on innovative technologies that can improve safety and efficiency of transportation systems. Road safety can be substantially enhanced by the deployment of wireless communication technologies for vehicular networks, which enable new services such as collision detection traffic management, and further communication facilities between moving vehicles. Aiming at providing reliable wireless communications for vehicular networks the RF communication will serve as an underlying protocol for future inter-vehicular applications worldwide. This paper presents an implementation of a complete vehicle to vehicle communication, designed according to the specification. In addition to this a blind spot detection system for protection against misshapen like vehicle collisions that causes loss of human lives is being implemented. The blind spot detection system will be useful while changing the lane. Ultrasonic sensors, Raspberry pi, RF module and GPS modules are used to implement the complete design The main aim of V2V communication is to prevent accidents by allowing vehicles in transit to send position and speed data to one another. The vehicle’s driver may simply receive a warning should there be a risk of an accident or the vehicle itself may take preemptive actions as braking to slow down.

A Reinforcement Learning-Based Incentive Scheme for Multi-Hop Communications in Vehicular Networks

A Reinforcement Learning-Based Incentive Scheme for Multi-Hop Communications in Vehicular Networks

Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach

Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach

Transformer-Based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

Transformer-Based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks

Enabling Reliable Water-Air Direct Optical Wireless Communication for Uncrewed Vehicular Networks: A Deep Reinforcement Learning Approach

Enabling Reliable Water-Air Direct Optical Wireless Communication for Uncrewed Vehicular Networks: A Deep Reinforcement Learning Approach

Secure V2V Communications in Relay-Assisted Cognitive Radio Vehicular Networks With Imperfect CSI

Secure V2V Communications in Relay-Assisted Cognitive Radio Vehicular Networks With Imperfect CSI

An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks

An Intelligent Path Loss Prediction Approach Based on Integrated Sensing and Communications for Future Vehicular Networks

Enhancing V2I and V2V Communication in Vehicular Networks through GSA-BPSO Optimized Neural Network

Enhancing V2I and V2V Communication in Vehicular Networks through GSA-BPSO Optimized Neural Network

Reliable cooperative communication in cognitive vehicular networks for intelligent transportation systems

SummaryRapid intelligent transportation systems (ITS) innovations need a reliable MAC protocol to enable massive nonsafety message delivery and high‐priority safety broadcasts. The significant rise in spectrum need is regulated by collaboration in cognitive vehicular networks (CVNs). For QoS improvement, a reliable cooperative MAC for CVNs (CCVN‐MAC) is presented in this study. CCVN allows vehicles to collaborate and share channel status information, allowing for proactive channel switching in case of a legacy user (LU) appearance. To enable transmission mode selection, additional control signals are included. Using the suggested cooperative makeup technique, the helper nodes resend failed transmission. A Markov chain represents the protocol, and NS‐2 is used to evaluate it for several performance characteristics. Compared with conventional MAC techniques, the proposed protocol depicts improved performance, including depreciation of 70% in average latency and an increment of 42.4% in throughput.

Fundamentals of Vehicular Communication Networks With Vehicle Platoons

Vehicular platooning is a promising way to facilitate efficient movement of vehicles with a shared route. Despite its relevance, the interplay of platooning and the communication performance in the resulting vehicular network (VN) is largely unexplored. Inspired by this, we develop a comprehensive approach to statistical modeling and system-level analysis of VNs with platooned traffic. Modeling the network of roads using the by-now well-accepted Poisson line process (PLP), we place vehicles on each road according to an independent Matérn cluster process (MCP) that jointly captures randomness in the locations of platoons on the roads and vehicles within each platoon. The resulting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">triply-stochastic</i> point process is a PLP-driven-Cox process, which we term the PLP-MCP. We first present this new point process’s distribution and derive several fundamental properties essential for the resulting VN’s analysis. Assuming that the cellular base-stations (BSs) are distributed as a Poisson point process (PPP), we derive the distribution of the loads served by the typical BS and the BS associated with the typical user. In deriving the latter, we also present a new approach to deriving the length distribution of a tagged chord in a Poisson Voronoi tessellation. Using the derived results, we present the rate coverage of the typical user while considering partial loading of the BSs. We also provide a comparative analysis of VNs with and without platooning of traffic.

Deep Learning Enabled IRS for 6G Intelligent Transportation Systems: A Comprehensive Study

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.

Switching mode allocation in planning paths for vehicular network communication

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.