Wireless Vehicular Communications Research Articles

Shared group session key-based conditional privacy-preserving authentication protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.

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.

Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC

Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.

Dual-Wideband Structure-Reused Antenna With Diverse Radiation and Polarization Features for Vehicular Wireless Communications

Dual-Wideband Structure-Reused Antenna With Diverse Radiation and Polarization Features for Vehicular Wireless Communications

BCGS: Blockchain-assisted privacy-preserving cross-domain authentication for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Secure authentication among vehicles in VANETs is an important requirement, but there are various limitations in many existing authentication protocols, for example, in terms of privacy protection, malicious entity tracking, and cross-domain authentication. In response, we first reconstruct a secure and effective short group signature scheme for realizing anonymous authentication and traceable identity within a domain. We then propose a secure blockchain-based privacy-preserving cross-domain authentication protocol for VANETs, namely BCGS, by integrating both blockchain and group signature. In BCGS, blockchain is leveraged to facilitate trusted information sharing and consequently supports cross-domain authentication among vehicles, and the group signature scheme is used to provide conditional privacy protection. Our comprehensive security and performance evaluations demonstrate that BCGS outperforms other similar approaches in terms of security, computation, and storage costs.

Reconfigurable Intelligent Surface Selection for Wireless Vehicular Communications

Deploying the reconfigurable intelligent surface (RIS) in wireless networks is getting tremendous attention due to its potential to provide alternative propagation paths for high-frequency signals and mitigate the impact of blockage and penetration losses. In this letter, we introduce the intelligent surface selection while considering a vehicular network scenario. Similar to the well-studied relay selection, we investigate the possibility of applying the selection concept to RIS, which enables the transmitter to select the best RIS among multiple surfaces. As evaluation metrics to investigate the performance of our scheme, we derive the ergodic capacity and the average symbol error probability (SEP). Moreover, we investigate the outage probability using Monte Carlo simulation.

Blockchain-Guided Dynamic Best-Relay Selection for Trustworthy Vehicular Communication

Considering the highly dynamic nature of the vehicular environment and the delay-sensitive wireless medium, enabling secure and reliable vehicle-to-vehicle communication becomes a very challenging task. In this paper, we propose a novel system design that uses blockchain technology to establish a high-level trust-management successful trustworthy cooperative vehicular wireless communication. The proposed system is a cross-layer approach that optimizes the best-relay selection process allowing only trustworthy relays to participate in data transmission. In this work, blockchain stores real-time information about relaying, transmitting, and receiving vehicles. The information includes channel characteristics and participation quality. The information is vetted and verified by mining vehicles. The result guides the relay selection process to block untrustworthy vehicles from participation. The entire system is comprehensively modeled and mathematically analyzed. Simulations using throughput and Bit Error Rate (BER) as evaluation metrics demonstrated the effectiveness and efficacy of the presented approach in enabling reliable wireless communication in presence of maliciously behaving vehicles. On average, the overall system throughput rate increased by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3bps$ </tex-math></inline-formula> , the BER was reduced by almost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2dB$ </tex-math></inline-formula> , and the percentage of false messages decreased by 90% considering high Signal to Noise Ratio (SNR).

A Blockchain-Based Multidomain Authentication Scheme for Conditional Privacy Preserving in Vehicular Ad-Hoc Network

Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose a solution, the vehicular safety support system (V3S), which exchanges BSMs reliably even when many vehicles are on the road. The V3S uses a clustering scheme to decrease network traffic by reducing the amount of data exchanged between a vehicle and the roadside unit (RSU). In addition, the V3S reduces the collision rate of wireless network packets by broadcasting the vehicle's BSM in an allocated timeslot using the time division multiple access (TDMA) MAC protocol. The V3S also deals with insufficient bandwidth for dedicated short-range communications (DSRC) by changing DSRC channels according to traffic flow. In evaluating the packet error rate for stable BSM packet delivery, the V3S demonstrates an excellent packet error rate of less than 1%, compared to the 802.11p with its packet error rate of 82%.

Vehicular Channel Characterization in Urban Environment at 30 GHz considering Overtaking and Traffic Flow

To realize reliable and stable millimeter-wave (mmWave) vehicular wireless communication, the research of vehicular channel characteristics in the dense urban environment is becoming increasingly important. A comprehensive research on the channel characteristics for 30 GHz vehicular communication in the Beijing Central Business District (CBD) scenario is conducted in this paper. The self-developed high-performance ray-tracing (RT) simulator is employed to support intensive simulations. Based on simulation results, the effects of multiantenna and beam switching on the key channel parameters are analyzed, as well as the impact of different traffic flows. The results can provide theoretical and data support for the evaluation of vehicular channel characteristics and will help for the design of the vehicular communication system enabling future intelligent transportation.

Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications

Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications.

Vehicular Communication Management Framework: A Flexible Hybrid Connectivity Platform for CCAM Services

In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems’ (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity.

Driving Environment Perception Based on the Fusion of Vehicular Wireless Communications and Automotive Remote Sensors.

Driving environment perception for automated vehicles is typically achieved by the use of automotive remote sensors such as radars and cameras. A vehicular wireless communication system can be viewed as a new type of remote sensor that plays a central role in connected and automated vehicles (CAVs), which are capable of sharing information with each other and also with the surrounding infrastructure. In this paper, we present the design and implementation of driving environment perception based on the fusion of vehicular wireless communications and automotive remote sensors. A track-to-track fusion of high-level sensor data and vehicular wireless communication data was performed to accurately and reliably locate the remote target in the vehicle surroundings and predict the future trajectory. The proposed approach was implemented and evaluated in vehicle tests conducted at a proving ground. The experimental results demonstrate that using vehicular wireless communications in conjunction with the on-board sensors enables improved perception of the surrounding vehicle located at varying longitudinal and lateral distances. The results also indicate that vehicle future trajectory and potential crash involvement can be reliably predicted with the proposed system in different cut-in driving scenarios.

Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors

Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surrounding the target vehicle, and then the row and the column that will be occupied by the target vehicle at future time steps are determined using the RF algorithm and the LSTM encoder-decoder architecture, respectively. For the collection of training data, the test vehicle was equipped with a camera and LIDAR sensors along with vehicular wireless communication devices, and the experiments were conducted under various driving scenarios. The vehicle test results demonstrate that the proposed method provides more robust trajectory prediction compared with existing trajectory prediction methods.

Performance Enhancement of Low-Profile Wideband Multi-Element MIMO Arrays Backed by AMC Surface for Vehicular Wireless Communications

Performance Enhancement of Low-Profile Wideband Multi-Element MIMO Arrays Backed by AMC Surface for Vehicular Wireless Communications

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than 3.6 dB, 78 ns, and 52 Hz, respectively, for 80% of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for 86% of the simulation duration.

Optimal Exploitation of On-Street Parked Vehicles as Roadside Gateways for Social IoV—A Case of Kigali City

Vehicular Ad Hoc Network (VANET) is a subclass of Mobile Ad Hoc Network that mainly consists of moving and/or stationary vehicles, connected through wireless protocols such as IEEE 802.11p and wireless access in vehicular environments (WAVE). With the evolution of the Internet of Things (IoT), ordinary VANET has turned to the Internet of Vehicles (IoV), with additional social aspects, a novel extension themed SIoV has become common in urban areas. However vehicular wireless communication paradigms exhibit short radio communication. This problem has always been approached by supplementing moving vehicles with stationary Road Side Infrastructures, commonly known as roadside units (RSUs). The penetration of such RSUs on the global market is very low; furthermore, their procurement, deployment, and maintenance costs are prohibitively very high. All mentioned challenges have discouraged the widespread deployment of roadside infrastructure especially within large urban scenarios. With this research, we leverage on-street parked vehicles to allow them to exist as temporal gateways in the case study area. A novel modeling technique is introduced to enable a specific Percentage of parked vehicles to take up the role of roadside gateways for a certain percentage of their parking time. A mobile application is implemented that manages parking duration of the vehicle, based on the arrival, and departure time frames. Two more existing strategies were discussed (road-intersection RSUs deployment approach and Inter-vehicle scheme) to validate our proposed method through comparative studies. To evaluate the network performance evaluation, we compare two performance metrics, that is, Packets success delivery rate, and overall packets throughput under numerous vehicle densities. Using parked vehicles as temporal roadside gateways has demonstrated better results in comparison to intersection based RSUs deployment approach, and free vehicle to vehicle communication approach.

Graded Warning for Rear-End Collision: An Artificial Intelligence-Aided Algorithm

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under the condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm for rear-end collision avoidance. Three key issues are addressed by applying the neural network approach, including noises in positioning, inaccurate risk assessment, and enhanced comfort level of passengers. First, to filter the noises in positioning, wireless vehicular communications are leveraged; accurate relative lane positioning can be achieved to justify when two vehicles are in the same lane. Second, an online neural network model is developed to assess the risk of collisions in real time while driving. The algorithm can converge fast to a globally optimal solution and adapt to different traffic environments. Third, to maximize the comfort of passengers during the braking process, a graded warning strategy is developed at the prerequisite of guaranteed safety. With the above schemes sewed in to one framework, our proposal can achieve rear-end warning with reduced missing alarm rate, accurate risk assessment and enhanced comfort to passengers. The extensive simulations validate the effectiveness and accuracy of our proposal in terms of relative lane positioning, risk assessment, and collision avoidance.

Polarization and Frequency-Selective Surface for Vehicular Beamforming Communications Requiring Near-Zero Profile

A polarization-selective and reconfigurable Fresnel zone plate consisting of frequency selective surfaces (FSS) loaded with varactor diodes is devised for compact vehicular wireless communication devices. The unit cell constituting the impedance zone exhibits rapid impedance transitions from 0 ohm (electrically-opaque) to 377 ohm (electrically-transparent). An I-shape configuration is employed for the resonant unit cell structure, featuring high compatibility with DC bias voltage network. In addition, double-sided FSS is employed on the top and bottom faces for polarization-selective beamforming. The FSS comprise of a dielectric with near zero thickness ( λ /250 of 2.40 GHz) resulting in intentionally high mutual coupling between the layers. The devised unit cell is designed using an equivalent circuit model analysis and optimized using finite element solver, achieving over 30 dB impedance switching. The proposed unit cell is experimentally confirmed in both vertical and horizontal polarizations. By configuring the DC voltage bias lines, the unit cells of the FSS are transformed into electrically-driven impedance zones formulating alternating and repetitive gratings. The width and the interval of the grating manipulate the constructive and destructive interference resulting in beam scanning range of 30° in both vertical and horizontal polarizations. The proposed approach can provide concise and compact beamforming solutions for future vehicular communications.

A hybrid integrity monitoring method using vehicular wireless communication in difficult environments for GNSS

Vehicular communication using the DSRC (Dedicated Short Range Communication) standard is becoming an enabling factor for a wide range of Cooperative Intelligent Transportation System (C-ITS) applications, especially those with high requirements of safety assurance. The location information of connected vehicles is fundamental, and significant for ensuring safe driving and preventing traffic accidents under complex and uncertain conditions. In order to alleviate the drawbacks of GNSS (Global Navigation Satellite System) based positioning due to satellite signal non-availability under difficult environments such as dense urban areas, a hybrid integrity monitoring technique is proposed. The range-rate measurement from the DSRC unit in the vehicle is employed to assist integrity monitoring of observable satellites, and the DSRC measurements are sent to the target vehicle through a vehicle-to-vehicle channel along with the identified state of neighboring vehicles. With the assistance from surrounding neighborhoods and a road map database, a virtual satellite ranging solution is possible to expand the measurements for enabling autonomous GNSS integrity monitoring and fault detection without the limitation of satellite visibility. Simulations conducted with different vehicle operation conditions and GNSS fault models demonstrate that the proposed solution outperforms conventional RAIM (Receiver Autonomous Integrity Monitoring) methods making it promising for many location-based cooperative applications.