Roadside Units Research Articles

Hybrid Mutual Authentication for Vehicle-to-Infrastructure Communication Without the Coverage of Roadside Units

The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety.

Advanced System for Optimizing Electricity Trading and Flow Redirection in Internet of Vehicles Networks Using Flow-DNET and Taylor Social Optimization

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework.

Dynamic multimedia transmission scheduling by unmanned aerial vehicle–mounted base stations for a vehicular ad hoc network

On highways, vehicle speed and topology changes are likely to cause transmission delays. In this study, a multimedia transmission system for highways is proposed; this system combines a next-generation cloud radio access network, which has a centralised architecture, with unmanned aerial vehicle (UAV)-mounted base stations (UAV-BSs), cellular network base stations, and roadside units. The deployment characteristics of the UAV-BSs are examined to improve the service of vehicular ad hoc networks and the quality of experience of users. A UAV location assignment algorithm and a transmission resource allocation algorithm for multiple bit rates, communication channels, and relay stations are proposed to reduce multimedia playback lag. We implemented a simulation programme to evaluate how the proposed algorithms outperformed other relevant methods. The simulation results indicate that the proposed algorithms achieve lower lag time than do existing demand- and transmission-based methods. The findings of this study have implications for the practical application of multimedia transmission decision support on highways.

A Secure and Decentralized Using Blockchain and Ring‐Based Cryptosystem

ABSTRACTThe advancement of Intelligent Transport Systems (ITSs) and the Internet of Vehicles (IoVs) introduces significant security challenges, particularly in ensuring secure communication between vehicles and vehicles (V2V), vehicles and Road Side Units (V2RSU). These vulnerabilities could potentially compromise the integrity of vehicular networks, making robust security measures essential. This paper aims to develop a secure communication framework for IoV environments that addresses these security concerns by enhancing authentication and encryption processes. The proposed approach combines the Ring‐Based Degree Truncated Polynomial Cryptosystem (RNTPC) with a Blockchain framework incorporating an enhanced proof‐of‐authentication (BEPoAuth) consensus algorithm. The approach is initiated from the registration phase and managed by a Trusted Authority (TA), ensuring secure vehicle and RSU registration. Authentication is carried out in two ways: V2V authentication through vehicle signatures and V2RSU authentication via batch verification of cluster members. Group keys are generated using RNTPC for secure communication, and blockchain technology is integrated to secure transactions within the IoV environment. The proposed methodology is evaluated using various performance metrics, demonstrating a 15%–20% improvement in throughput compared to existing techniques such as Practical Byzantine Fault Tolerance (PBFT), Secure and Highly Efficient Blockchain PBFT Consensus Algorithm (SG‐PBFT), credit‐based PBFT consensus algorithm (CPBFT) and Geographic‐PBFT (G‐PBFT). The system also exhibited minimal computational and communication latency while enhancing V2V and V2RSU communication efficiency. The RNTPC‐BEPoAuth framework offers a robust and secure solution for IoV communications, significantly outperforming existing methods in terms of throughput and efficiency. This approach provides a reliable foundation for secure ITSs, addressing critical security concerns in vehicular networks.

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price Algorithm

Optimizing the Deployment of Static and Mobile Roadside Units Using a Branch-and-Price Algorithm

Seamless Connectivity with Fuzzy-Based Unmanned Aerial Vehicle for VANET in Congested Zone

Providing seamless connectivity during transit in vehicular ad hoc networks (VANETs) is the most promising and challenging task. In VANET, the vehicles are able to communicate with other vehicles and also with the infrastructure units such as road side unit (RSU). The coverage range of RSU is not sufficient to cover the whole road. The vehicle can drive to an area which is not covered by any of the RSUs, and it is termed as congested zone (CZ). This will extremely lead to traffic congestion and maximize blocking probability. In order to provide connectivity between the road segments and to avoid long delay in message transmission, unmanned aerial vehicles (UAVs) are placed in CZs, which act as relay nodes. To enhance the connectivity of the vehicles, a fuzzy-based UAV-assisted handoff scheme (F-UAV-HO) is proposed in this work. By adopting fuzzy, the unwanted participation of UAV is limited in non-congested traffic, thereby saving the battery resource. To validate the proposed work, the simulations are executed in Network Simulator 2.35. The proposed system outperforms other conventional UAV-assisted schemes in congested traffic in terms of average end-to-end delay, PDR and throughput.

EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET

Vehicular ad hoc network (VANET) has been a promising technology in smart transportation system, which can enable information exchange between vehicles and roadside units (RSUs). However, the privacy of vehicles and RSUs is a critical challenge in VANET, as they may expose sensitive information to malicious attackers or unauthorized parties. Many existing authenticated key agreement (AKA) schemes aim to protect the privacy of vehicles and RSUs, but they often neglect the physical security of the devices involved in the communication. Therefore, we propose an efficient and privacy-preserving AKA scheme in VANET, which embeds physical unclonable function (PUF) and fuzzy extraction (FE) technology. PUF is a physical device that generates random strings based on their intrinsic characteristics and external inputs, which can protect the secrets in the devices from being stolen by attackers. FE can compensate for the drawbacks of PUF affected by environmental factors. Our scheme preserves the identity privacy of legitimate RSUs and vehicles, as well as intercepts and traces the identity of malicious attackers. In addition, we eliminate the involvement of the third party (TP) in the AKA phase to better meet the high-speed driving of vehicles. Finally, we conduct formal and informal security analyses in random oracle model (ROM), which prove that our scheme can resist various attacks. We also show in the performance analysis that our scheme has the lowest computational cost, communication overhead, and total energy consumption.

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

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.

An Internet of Vehicles road traffic data sharing scheme based on signcryption and editable blockchain

Aiming at the problems of low real-time update efficiency of traffic data and privacy leakage of vehicle users in the Internet of Vehicles (IoV) data sharing, an IoV road traffic data sharing scheme based on signcryption and editable blockchain is proposed. Road-Side Unit (RSU) implements proxy signcryption for sharing data based on the authorization of the vehicle, ensuring the reliability of data and reducing the frequent interaction between vehicles and authorized map companies when sharing data. The editable blockchain is implemented by the chameleon hash function, and no new blocks need to be generated when updating data, thereby reducing the storage overhead caused by updating data. Combined with editable blockchain and fog computing server, the storage, sharing and update of traffic data are better realized. Security analysis shows that the proposed scheme satisfies the confidentiality, integrity, verifiability and unforgeability. Simulation results show that the proposed scheme has less communication overhead and computing overhead.

Truck Platooning Merging Control Method in Merge Areas Based on the IDM Model and V2X Communication

Truck platooning technology has been widely studied for its advantages in traffic efficiency and energy savings. Although recent studies have made significant progress in specific scenarios, research on merging ramp trucks into the main road truck platoons remains insufficient. This paper proposes an innovative cooperative control method for merging ramp trucks and main road truck platoons based on vehicle-to-everything (V2X) technology. The method integrates the intelligent driver model (IDM), acceleration control logic for merging trucks before merging, lane-changing decision logic, steering control methods, and on-board unit (OBU) and roadside unit (RSU) communication technologies. This ensures that autonomous merging trucks can smoothly join the main road truck platoon under various insertion positions and acceleration lane lengths. After successfully joining the platoon, the merging trucks can maintain appropriate spacing and high speed, thereby improving overall traffic efficiency. Further analysis on different insertion positions and acceleration lane lengths reveals that when using the proposed method, merging trucks have the least impact on the overall platoon when they join at the head or tail of the main road truck platoon. Although the proposed method performs better with longer acceleration lanes, these lanes are costly to construct. The proposed method can also enable successful platoon joining with shorter acceleration lanes, optimizing the use of acceleration lanes to some extent.

Robust License Plate Recognition in OCC-Based Vehicle Networks Using Image Reconstruction

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of 81.58% and 79.35% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about 31.18% and 24.26%, respectively, compared with the conventional method.

Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

Zigbee-Based Wireless Sensor Network of MEMS Accelerometers for Pavement Monitoring

In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor.

A Blockchain‐Based Proxy Re‐Encryption Scheme With Cryptographic Reverse Firewall for IoV

ABSTRACTAs the internet of vehicles (IoV) technology develops, it promotes the intelligent interaction among vehicles, roadside units, and the environment. Nevertheless, it also brings vehicle information security challenges. In recent years, vehicle data sharing is suffering to algorithm substitution attacks (ASA), which means backdoor adversaries can carry out filtering attacks through data sharing. Therefore, this paper designs a blockchain‐based proxy re‐encryption (PRE) scheme with cryptographic reverse firewall (BIBPR‐CRF) for IoV. In our proposal, CRF can promise the internal safety of vehicle units. More specifically, it can prevent ASA attacks while ensuring chosen plaintext attack (CPA)‐security. Meanwhile, the PRE algorithm can provide the confidential sharing and secure operation of data. Moreover, we use a consortium blockchain service center (CBSC) to store the first ciphertext and re‐encrypt it with smart contracts on the blockchain, which can avoid single point of failure and achieve higher efficiency compared to proxy servers. Finally, we evaluate the performance of BIBPR‐CRF with regard to communication cost, computational cost, and energy consumption. Our proposal is the most fitting for IoV application, in contrast with the other three schemes.

Enhancing the Vehicular Public Key Infrastructure to Develop Privacy-Preserving Authentication Scheme for Vanet by Using PCM and MSS Scheme

This article proposes a security-based authentication as well as efficient certificate management approach for VANET to detect fraudulent nodes with better precision, less latency and overhead. The primary purpose of the developed system is to establish effectual and heftiness of VANET security that lead to the stability of overall network. VANETs are composed of vehicles and Road Side Units (RSUs) assisting with network management and the vehicles connect with one another and RSUs to furnish roadside information and safety solutions. Security is an essential factor in VANETs because the confidentiality of humans (passengers) is paramount; hence, Vehicular Public Key Infrastructure (VPKI) is utilised to offer authentication and safety services in VANETs. The developed structure provides an encrypted VANET transmission infrastructure by utilising the concepts of Merkle Signature Scheme (MSS) and Pseudo-code Certificate Management (PCM) to minimise overhead for communication and latency while ensuring entity authenticity. Messages are authenticated by sender, encoded with a vehicular public key distributed by a PCM-MSS and decrypted by the destination, resulting in every transmission including a certification from a reliable authority. During that verification, the transmitter and receiver of message’s authentication and validation is accomplished. Simulation findings show that the proposed approach improves the reliability of identifying hostile nodes and PDR while reducing authentication delays and overhead.

Collaborative Joint Perception and Prediction for Autonomous Driving.

Collaboration among road agents, such as connected autonomous vehicles and roadside units, enhances driving performance by enabling the exchange of valuable information. However, existing collaboration methods predominantly focus on perception tasks and rely on single-frame static information sharing, which limits the effective exchange of temporal data and hinders broader applications of collaboration. To address this challenge, we propose CoPnP, a novel collaborative joint perception and prediction system, whose core innovation is to realize multi-frame spatial-temporal information sharing. To achieve effective and communication-efficient information sharing, two novel designs are proposed: (1) a task-oriented spatial-temporal information-refinement model, which filters redundant and noisy multi-frame features into concise representations; (2) a spatial-temporal importance-aware feature-fusion model, which comprehensively fuses features from various agents. The proposed CoPnP expands the benefits of collaboration among road agents to the joint perception and prediction task. The experimental results demonstrate that CoPnP outperforms existing state-of-the-art collaboration methods, achieving a significant performance-communication trade-off and yielding up to 11.51%/10.34% Intersection over union and 12.31%/10.96% video panoptic quality gains over single-agent PnP on the OPV2V/V2XSet datasets.