Vehicular Communication Research Articles

Cluster Based Energy Efficient Optimal Relay Selection Strategy for Multi Hop Reliable Cooperative Communication in Vehicular Communication

Cluster Based Energy Efficient Optimal Relay Selection Strategy for Multi Hop Reliable Cooperative Communication in Vehicular Communication

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

5G Network Slicing as a Service Enabler for the Automotive Sector

ABSTRACTNetwork slicing, a key technology introduced in 5G standards, enables mobile networks to simultaneously support a wide range of heterogeneous use cases with diverse quality of service (QoS) requirements. This work discusses the potential benefits of network slicing for the automotive sector, encompassing manufacturing processes and vehicular communications. The review of the state of the art reveals a clear gap regarding the application of network slicing from the perspective of industrial verticals such as automotive use cases and their specific requirements. Departing from this observation, we first identify limitations of previous cellular technologies and open challenges for supporting the data services required. Then we describe network slicing as an enabler to face these challenges. We present an analysis of the cost equilibrium for network slicing to be effective for car manufacturers, and tests in real 5G networks that demonstrate the performance improvement in OTA updates coexisting with other services.

Investigation of optically transparent metasurfaces for gain improvement of an ultrawideband antenna for wireless body area networks and vehicular communications

Abstract Due to higher frequencies which cause signal deterioration, 5G enabled antennas are prone to substantial attenuation. To circumvent this problem, several access points, signal repeaters, and base stations must be installed closer together on building windows, streetlights, and other infrastructure. Optically transparent antennas preserve the aesthetics of the windows by offering the requirements for access points and base stations for providing network connectivity to the cars and pedestrians passing by those buildings. Here we report an ultrawideband (UWB) high gain optically transparent antenna operating from 4-12 GHz. Indium tin oxide (ITO) coated glass has been used as an optically transparent substrate for designing the antenna and metasurface. Two optically transparent metasurfaces have been proposed for band-stop filtering action across the UWB frequency range 4-12 GHz. In absence of the metasurface, the antenna operates in the frequency band ranging from 3.8 to 15 GHz. Average gain enhancement is 5.2 dB with radiation efficiency higher than 93% across the UWB frequency band from 4-12 GHz. Numerical simulations have been carried out for analysis of the proposed antenna and metasurfaces. These simulations have been validated by experimental measurements of the fabricated prototypes.

Construction and analysis of the EMC evaluation model for vehicular communication systems

With the development of wireless communication technologies, vehicle-mounted wireless communication systems are changing with each passing day, and their EMC has become the key to constraining vehicles to adapt to complex electromagnetic environments. Aiming at the limitations of existing EMC evaluation methods or models for wireless communication systems and the actual needs of EMC evaluation and analysis for vehicular communication systems, a five-level novel evaluation model including working environment, signal spectrum, receiver sensitivity, antenna isolation and communication performance is constructed considering the completeness and accuracy of EMC evaluation for vehicular communication systems. The performance of the constructed model is validated using the vehicular communication system of an armored vehicle as an example. The model can evaluate whether there is interference between the working environment and the signal spectrum of the vehicle-mounted radio station. error in the reduction of the receiver sensitivity between calculation and measurement is 5.8%. The calculated isolation of the vehicle-mounted antenna is in good agreement with the measurements. The modulation mode and coding mode of the vehicle-mounted digital communication system with better performance are simulated and analyzed. When the receiver sensitivity is reduced by 6 dB, the vehicle communication distance is reduced by 50%. The simulation and measurement results show that the proposed model is suitable for the evaluation of electromagnetic compatibility of vehicular communication systems for armored vehicles.

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

Design of a compact quad-port wideband printed MIMO antenna for vehicular communication environment

ABSTRACT A wideband quad-port Multiple Input Multiple Output (MIMO) antenna with efficient isolation characteristics for 5 G New Radio (5 G-NR) applications is presented. The proposed antenna works in the span of 3–6 GHz frequency covering the n77/n78/n79 bands also covering the 5 G-V2X band (3300–5000 MHz), LTE-46 band (5150–5925 MHz) and Dedicated Short Range Communication (DSRC) at 5.9 GHz band. The single element of the MIMO antenna structure is modelled by an elliptical structure stacked on a rectangular patch along with the feedline on the conducting plane. The ground plane is modelled as a rhombic design providing a Defected Ground Structure (DGS) to provide the resonance at the desired frequency band. Later, the antenna is evolved as a four-element MIMO system by arranging the antenna elements orthogonal to each other with a distance of separation between the adjacent antenna elements considered to be 10 mm. The all-inclusive dimension of the proposed antenna is 65 × 65 mm2. The simulated MIMO structure is fabricated and measured for antenna parameters and MIMO parameters such as isolation characteristics and MIMO diversity performances. The simulated values are obtained at acceptable limits with the measured values for the proposed wideband frequency range of operation. The compact, wideband antenna is found to be suitable for various 5 G-NR applications, especially Wi-Fi-assisted Vehicular Communication in the sub-6 GHz frequency range.

AI/ML-based services and applications for 6G-connected and autonomous vehicles

AI and ML emerge as pivotal in overcoming the limitations of traditional network optimization techniques and conventional control loop designs, particularly in addressing the challenges of high mobility and dynamic vehicular communications inherent in the domain of connected and autonomous vehicles (CAVs). The survey explores the contributions of novel AI/ML techniques in the field of CAVs, also in the context of innovative deployment of multilevel cloud systems and edge computing as strategic solutions to meet the requirements of high traffic density and mobility in CAV networks. These technologies are instrumental in curbing latency and alleviating network congestion by facilitating proximal computing resources to CAVs, thereby enhancing operational efficiency also when AI-based applications require computationally-heavy tasks. A significant focus of this survey is the anticipated impact of 6G technology, which promises to revolutionize the mobility industry. 6G is envisaged to foster intelligent, cooperative, and sustainable mobility environments, heralding a new era in vehicular communication and network management. This survey comprehensively reviews the latest advancements and potential applications of AI/ML for CAVs, including sensory perception enhancement, real-time traffic management, and personalised navigation.

Securing the vetaverse: Web 3.0 for decentralized Digital Twin-enhanced vehicle–road safety

The rapid evolution of vehicular communication technologies in recent times necessitates robust security measures and enhanced road safety protocols. Integrity of data shared between vehicles, their Digital Twins (DT) and road side units is at stake. Intrusion in these data can potentially lead to misinformation in Advanced Driving Assistance Systems (ADAS) causing serious consequences upon road safety. These include improper detection of drunk driving behaviors. In this domain, Web 3.0 emerges as the overarching approach that can transform security of vehicles and ensure road safety. This paper explores the potential of Web 3.0 and its key enabling technologies to establish a VEhicular meTAVERSE (Vetaverse) utilizing edge-based DTs of the vehicles to process their dynamics shared in real-time, and based on its deep learning models, predict whether the driving behavior is drunk or sober. This Deep Neural Network (DNN) performs these predictions with 96% accuracy. It secures all Vehicle-to-Digital Twin (V2DT) communications via Multichain - a horizontally scaled parallel blockchains platform that tamper proofs each bit of sensor data, and optimizes transaction validation time to leverage vetaverse security. Results reveal that this framework is accurate and computationally lightweight in comparison to existing state-of-the-art, and brings Web 3.0 to the crucial road safety use-case.

AnonMAKE: Toward Secure and Anonymous Mutually Authenticated Key Exchange Protocol for Vehicular Communications

AnonMAKE: Toward Secure and Anonymous Mutually Authenticated Key Exchange Protocol for Vehicular Communications

Adaptive Mobility and Reliability-based Routing Protocol for Smart Healthcare Management Systems in Vehicular Ad‐hoc Networks

Ambulances enable digital and widespread remote care for older people in the healthcare sector. The Vehicular Ad Hoc Network (VANET) is critical for allowing the digital and Intelligent Transportation System (ITS) for the smart healthcare world. Enhancing the dependability and flexibility of an adaptive routing algorithm improves vehicular communications' Quality of Service (QoS) effectiveness. An essential concern in vehicular technology is enabling drivers to make reliable judgments. Developing an effective routing system that ensures a suitable QoS takes time and effort. The vehicular network ecosystem is characterized by limited mobility, fast vehicle speeds, and constantly changing configurations. This study makes four distinct contributions. Initially, it presents adaptive routing protocols, capable of recognizing changes in the network environment, aware of the mobility of nodes, and dependable in delivering data packets. The experimental configuration is implemented inside a discrete Network Simulator (NS-3) scenario to enhance the QoS. Mobility-aware routing algorithms can provide ITS with satisfactory mobility, vital dependability, high Packet Delivery Ratio (PDR), high throughput, and low End-to-End (E2E) Delay. The proposed method has an average throughput of 664.3 kbps, a delay of 151.3 ms, and a packet delivery ratio (PDR) of 94.23%.

A High-Gain, High-Efficiency Transparent and Flexible Antenna for Vehicular Communication

A high-gain high-efficiency flexible and transparent film antenna in millimeter-wave is proposed in this paper. Comb structure which can be regarded as developed from the grid array structure is employed to mitigate the loss of the feeding network caused by the lossy metal mesh. The gain of the antenna has low sensitivity to the sheet resistance, which is promising in developing large-scale and high-performance transparent antenna. A large-scale ([Formula: see text]@28[Formula: see text]GHz) antenna is designed and fabricated using transparent metal mesh film with sheet resistance of 0.1[Formula: see text][Formula: see text]/sq and a thin PET film substrate. Bending measurement shows the antenna has excellent mechanical flexibility. A peak gain of 15.92[Formula: see text]dBi, radiation efficiency of 65.1%, optical transparency of 84% and 1,000-times bending are obtained. To the best of our knowledge, it is the highest gain and radiation efficiency flexible-transparent antenna reported thus far, which provides a glass-integrated antenna solution for vehicular communication.

Enhancing Security and Performance in Vehicular Adhoc Networks: A Machine Learning Approach to Combat Adversarial Attacks

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of using Machine Learning to improve VANET security and reliability when new threats appear. Through this research, the significance of the application of ML in the protection of vehicular communication in order to enhance both traffic safety and flow has been articulated.

LBCSC: Lattice‐based chameleon signcryption scheme for secure and privacy‐preserving vehicular communications

LBCSC: Lattice‐based chameleon signcryption scheme for secure and privacy‐preserving vehicular communications

ZeroVCS: An efficient authentication protocol without trusted authority for zero-trust vehicular communication systems

Vehicular communication systems can provide two types of communications: Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V). However, in both cases, there is zero-trust between the communicating entities. This may give privilege to the unauthorized vehicles to join the network. Hence, a strong authentication protocol is required to ensure proper access control and communication security. In traditional protocols, such tasks are typically accomplished via a central Trusted Authority (TA). However, communication with TA may increase the overall authentication delay. Such delay may be incompatible with the future generation vehicular communication systems, where dense deployment of small-cells are required to ensure higher system capacity and seamless mobility (e.g., 5G onward). Further, TA may suffer from denial-of-service when the number of access requests becomes excessively large, because each request must be forwarded to TA for authentication and access control. In this article, we put forward an efficient authentication protocol without trusted authority for zero-trust vehicular communication systems, called ZeroVCS. It does not involve TA for authentication and access control, thus improving the authentication delay, reducing the chance of denial-of-service, and ensuring compatibility with the future generation vehicular communication systems. ZeroVCS can also provide communication security under various passive and active attacks. Finally, the performance-based comparison proves the efficiency of ZeroVCS.

A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

Enhancing vehicular NOMA communication security through reconfigurable intelligent surfaces

Physical layer security (PLS) aims to ensure the confidentiality and authenticity of transmitted data by capitalizing on the inherent randomness of wireless channels. Owing to the popularity of intelligent transportation systems (ITSs), PLS research has sparked renewed interest in the wireless research community. This paper investigates the performance of secure communication in the context of a vehicle-to-vehicle (V2V) communication scenario by employing a reconfigurable intelligent surface (RIS). Further, we introduce the concept of non-orthogonal multiple access (NOMA) to reduce latency and improve communication efficiency in V2V networks. This study aims to comprehensively analyze secrecy performance, encompassing parameters like average secrecy capacity (ASC), secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNZSC). Our research aims to highlight the efficacy of RIS in providing secure and reliable communication within V2V NOMA networks. Ultimately, our study contributes to advancing secure communication protocols in intelligent transportation systems.

Maximising communication reliability in vehicular visible light communication systems: a novel Allan variance—based adaptive Kalman filtering approach

Maximising communication reliability in vehicular visible light communication systems: a novel Allan variance—based adaptive Kalman filtering approach

Performance evaluation of vehicular communication in terahertz band

Abstract The Terahertz (THz) band is seen as one of the potentially essential technologies to meet the data throughput requirements of terabits per second (Tbits) in future networks. Considering vehicular communication in particular, the current vehicular communication technologies cannot cope with the rapidly increasing data traffic. To deliver increased capacity and data rate with reduced latency, new technologies and methodologies are therefore required. It is recognized that one possible technology to meet these needs is THz technology. Since the change in distance between vehicles due to their speed affects THz communication, channel modeling was carried out for single-lane and multi-lane vehicular communication scenarios in this study. The system’s performance was also examined. Performance metrics were determined as path loss, signal-to-noise ratio (SNR), capacity and bit error rate (BER). While performing these analyses, the absorption of THz waves is taken into account and the High-Resolution Transmission Molecular Absorption Database (HITRAN) was used to model it. As a result, the most appropriate circumstances for successful vehicular communication have been identified and studied.