Search Results (118)

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

The evolution of the internet towards supporting time-critical applications, such as industrial cyber-physical systems (CPSs) and autonomous systems, has created an urgent demand for networks capable of providing deterministic, low-latency communication. Autonomous vehicles represent a particularly challenging use case within this domain, requiring both reliability and determinism for massive data streams—a requirement that traditional Ethernet technologies cannot satisfy. This paper addresses this critical gap by proposing a comprehensive scheduling framework based on Time-Aware Shaping (TAS) within the Time-Sensitive Networking (TSN) standard. The framework features two key contributions: (1) a novel baseline scheduling algorithm that incorporates a sub-flow division mechanism to enhance schedulability for high-bandwidth streams, computing Gate Control Lists (GCLs) via an iterative SMT-based method; (2) a separate heuristic-based computation acceleration algorithm to enable fast, scalable GCL generation for large-scale networks. Through extensive simulations, the proposed baseline algorithm demonstrates a reduction in end-to-end latency of up to 59% compared to standard methods, with jitter controlled at the nanosecond level. The acceleration algorithm is shown to compute schedules for 200 data streams in approximately one second. The framework’s effectiveness is further validated on a real-world TSN hardware testbed, confirming its capability to achieve deterministic transmission with low latency and jitter in a physical environment. This work provides a practical and scalable solution for deploying deterministic communication in complex autonomous and cyber-physical systems. Full article

In recent years, C-ITS services have been extensively specified, tested, and deployed, leading to their first commercial applications. While technical advancements are progressing, the human factor remains crucial for widespread system implementation. The paper presents results of two field studies on user acceptance evaluations focusing on six use cases. Eighteen drivers participated in highway tests, while over 70 individuals responded to an online survey. The empirical results are discussed considering related literature. A structured literature review was conducted, starting with 426 papers, of which 32 were deeply analysed. The key findings of the activities are that the compliance rate is extremely high for safety-related services like hazard warning. However, compliance rates differ depending on the use case. People trust information coming from road operators compared to other sources of traffic information. In-vehicle information does not distract drivers from driving and must be clear and easy to understand. While user acceptance is high, particularly for safety-related services, there remains a need for clearer communication about C-ITS benefits to enhance transparency and trust. Full article

The controller area network (CAN) protocol, widely used for in-vehicle communication, lacks built-in security features and is inherently vulnerable to various attacks. Numerous attack techniques against CAN have been reported, leading to intrusion detection systems (IDSs) tailored for in-vehicle networks. In this study, we propose a novel lightweight unsupervised IDS for CAN networks, designed for real-time, on-device implementation. The proposed autoencoder model was trained exclusively on normal data. A portion of the attack data was utilized to determine the optimal detection threshold using a Gaussian kernel density estimation function, while the frame count was selected based on error rate analysis. Subsequently, the model was evaluated using four types of attack data that were not seen during training. Notably, the model employs a single threshold across all attack types, enabling detection using a single model. Furthermore, the designed software model was optimized for hardware implementation and validated on an FPGA under a real-time CAN communication environment. When evaluated, the proposed system achieved an average accuracy of 99.2%, precision of 99.2%, recall of 99.1%, and F1-score of 99.2%. Furthermore, compared to existing FPGA-based IDS models, our model reduced the usage of LUTs, flip-flops, and power by average factors of 1/5, 1/6, and 1/11. Full article

With the rapid development of smart connected vehicles, vehicle network communications demand high-speed data transmission to support advanced automotive services. Millimeter Wave (mmWave) communication offers fast data rates, strong anti-interference capabilities, high precision localization and low-latency, making it suitable for high-speed in-vehicle communications. However, mmWave communication performance in vehicular networks is hindered by high path loss and frequent beam alignment updates, significantly degrading the coverage and connectivity of vehicle nodes (VNs). In addition, atmospheric propagation attenuation further deteriorates signal quality and limits system performance due to raindrop absorption and scattering. Therefore, the pure mmWave networks cannot meet the high requirements of highway vehicular communications. To address these challenges, this paper proposes a hybrid mmWave and microwave network architecture to improve VNs’ coverage and connectivity performances through the strategic deployment of Roadside Units (RSUs). Using Radio Access Technology (RAT), mmWave and microwave RSUs are symmetrically deployed on both sides of the road to communicate with VNs located at the road center. This symmetric RSUs deployment significantly improves the network reliability. Analytical expressions for coverage and connectivity in the proposed hybrid networks are derived and compared with the pure mmWave networks, accounting for rainfall attenuation. The study results show that the proposed hybrid network shows better performance than the pure mmWave network in both coverage and connectivity. Full article

The Controller Area Network (CAN) protocol is one of the important communication standards in autonomous vehicles, enabling real-time information sharing across in-vehicle (IV) components to realize smooth coordination and dependability in vital activities. Without encryption and authentication, CAN reveals several vulnerabilities related to message attacks within the IV Network (IVN). Traditional centralized Intrusion Detection Systems (IDS) where all the historical data is grouped on one node result in privacy risks and scalability issues, making them unsuitable for real-time intrusion detection. To address these challenges, we propose a Deep Federated Learning (FL) architecture for intrusion detection in IVN. We propose a Bidirectional Long Short Term Memory (BiLSTM) architecture to capture temporal dependencies in the CAN bus and ensure enhanced feature extraction and multi-class classification. By evaluating our framework on three real-world datasets, we show how our proposal outperforms a baseline LSTM model from the state of the art. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems. Full article

The Internet of Vehicles plays a crucial role in advancing intelligent transportation systems, with In-Vehicle Ethernet serving as the fundamental backbone network of the new generation of in-vehicle communication. However, In-Vehicle Ethernet faces various network security threats, including data theft, data tampering, and malicious attacks. This study focuses on network intrusion and security issues in In-Vehicle Ethernet, by analyzing the data characteristics of Audio Video Transport Protocol and potential network attack means. We innovatively propose a network intrusion detection method based on a weighted histogram algorithm. This method aims to enhance the security of In-Vehicle Ethernet. Experimental results show that the anomaly detection rate of the proposed weighted histogram algorithm in this study is 99.7%, which shows an improvement of 15.8% compared with the traditional Bayesian algorithm, and 6.9% higher than the decision tree algorithm. Thus, our approach enhances the stability and anti-attack ability of In-Vehicle Ethernet, providing a solid network security for In-Vehicle Networks. Full article

With the development of intelligent connected vehicles, higher demands are being placed on the capabilities of in-vehicle bus networks. Compared to traditional in-vehicle bus networks like Local Interconnect Network (LIN) and Controller Area Network (CAN), the FlexRay bus offers advantages such as high real-time performance and high transmission rates, making it the core technology of the new generation of in-vehicle bus networks. This study focuses on the phenomenon of bandwidth resource waste in the FlexRay bus and innovatively proposes the FlexRay Static Segment Heterogeneous Scheduling Algorithm (SHSA). The SHSA algorithm optimizes the message transmission performance of the FlexRay bus through heterogeneous allocation of communication channels and message scheduling methods. This study established a simulation experimental platform using the CANoe.FlexRay bus network simulation tool and conducted simulation experiments on the proposed algorithm. Experimental results show that the average bandwidth utilization of the SHSA algorithm is 72.5%, which is 20.91%, 51.14%, and 54% higher than that of the existing Heterogeneous Makespan-minimizing DAG Scheduler (HMDS), Message Packing Scheme, and Jitter-aware Message Scheduling-Simulated Annealing and Greedy Randomized Adaptive Search Procedure (JAMS-SG), respectively. This study provides technical support for message transmission in intelligent connected vehicles and enhances the communication efficiency of the in-vehicle FlexRay bus network. Full article

The progress in automotive technology, communication protocols, and embedded systems has propelled the development of the Internet of Vehicles (IoV). In this system, each vehicle acts as a sophisticated sensing platform that collects environmental and vehicular data. These data assist drivers and infrastructure engineers in improving navigation safety, pollution control, and traffic management. Digital artefacts stored within vehicles can serve as critical evidence in road crime investigations. Given the interconnected and autonomous nature of intelligent vehicles, the effective identification of road crimes and the secure collection and preservation of evidence from these vehicles are essential for the successful implementation of the IoV ecosystem. Traditional digital forensics has primarily focused on in-vehicle investigations. This paper addresses the challenges of extending artefact identification to an IoV framework and introduces the Collaborative Forensic Platform for Electronic Artefacts (CFPEA). The CFPEA framework implements a collaborative forensic-by-design mechanism that is designed to securely collect, store, and share artefacts from the IoV environment. It enables individuals and groups to manage artefacts collected by their intelligent vehicles and store them in a non-proprietary format. This approach allows crime investigators and law enforcement agencies to gain access to real-time and highly relevant road crime artefacts that have been previously unknown to them or out of their reach, while enabling vehicle owners to monetise the use of their sensed artefacts. The CFPEA framework assists in identifying pertinent roadside units and evaluating their datasets, enabling the autonomous extraction of evidence for ongoing investigations. Leveraging CFPEA for artefact collection in road crime cases offers significant benefits for solving crimes and conducting thorough investigations. Full article

With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, their heavy computational and power demands often limit real-world deployment. In this paper, we present an optimised IDS based on a Binarised Neural Network (BNN) that employs network pruning to eliminate redundant parameters, achieving up to a 91.07% reduction with only a 0.1% accuracy loss. The proposed approach incorporates a two-stage Coarse-to-Fine (C2F) framework, efficiently filtering normal traffic in the initial stage to minimise unnecessary processing. To assess its practical feasibility, we implement and compare the pruned IDS across CPU, GPU, and FPGA platforms. The experimental results indicate that, with the same model structure, the FPGA-based solution outperforms GPU and CPU implementations by up to 3.7× and 2.4× in speed, while achieving up to 7.4× and 3.8× greater energy efficiency, respectively. Among cutting-edge BNN-based IDSs, our ultra-lightweight FPGA-based C2F approach achieves the fastest average inference speed, showing a 3.3× to 12× improvement, while also outperforming them in accuracy and average F1 score, highlighting its potential for low-power, high-performance vehicle security. Full article

With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are showing significant limitations in terms of bandwidth and response speed. In-Vehicle Ethernet, with its advantages of high bandwidth, low latency, and high reliability, has become the core technology for next-generation in-vehicle communication networks. This study focuses on bandwidth waste caused by guard bands and the limitations of Frame Pre-Emption in fully utilizing available bandwidth in In-Vehicle Ethernet. It aims to optimize TSN scheduling mechanisms by enhancing scheduling flexibility and bandwidth utilization, rather than modeling system-level vehicle functions. Based on the Time-Sensitive Networking (TSN) protocol, this paper proposes an innovative Adaptive Frame Segmentation (AFS) algorithm. The AFS algorithm enhances the performance of In-Vehicle Ethernet message transmission through flexible frame segmentation and efficient message scheduling. Experimental results indicate that the AFS algorithm achieves an average local bandwidth utilization of 94.16%, improving by 4.35%, 5.65%, and 30.48% over Frame Pre-Emption, Packet-Size Aware Scheduling (PAS), and Improved Qbv algorithms, respectively. The AFS algorithm demonstrates stability and efficiency in complex network traffic scenarios, reducing bandwidth waste and improving In-Vehicle Ethernet’s real-time performance and responsiveness. This study provides critical technical support for efficient communication in intelligent connected vehicles, further advancing the development and application of In-Vehicle Ethernet technology. Full article

Deterministic Ethernet (DetEth) is widely used in real-time distributed systems, such as avionics and in-vehicle control. Clock synchronization protocols (CSPs) establish global time, which is a critical foundation for deterministic communication in DetEth. However, existing protocols often lack flexibility, making customization and adaptation to specific scenarios difficult and time consuming. We propose OpenSync, which is a software-defined clock synchronization architecture that decouples the synchronization control plane from the data plane. OpenSync includes a programmable time data injector and a fine-grained calibrated timer in the data plane, enabling easy implementation with standard DetEth hardware and support for various CSPs. The control plane provides a synchronization library to configure local clocks and retrieve accurate time data for different methods. To validate OpenSync’s generality and efficiency, we develop an FPGA-based prototype and implement three CSPs through software programming. A fully functional testbed demonstrates that these CSPs meet the accuracy and protocol consistency requirements of their respective application scenarios. Full article

Recent advances in autonomous driving have led to an increased use of LiDAR (Light Detection and Ranging) sensors for high-frequency 3D perceptions, resulting in massive data volumes that challenge in-vehicle networks, storage systems, and cloud-edge communications. To address this issue, we propose a bounded-error LiDAR compression framework that enforces a user-defined maximum coordinate deviation (e.g., 2 cm) in the real-world space. Our method combines multiple compression strategies in both axis-wise metric Axis or Euclidean metric L2 (namely, Error-Bounded Huffman Coding (EB-HC), Error-Bounded 3D Compression (EB-3D), and the extended Error-Bounded Huffman Coding with 3D Integration (EB-HC-3D)) with a lossless Huffman coding baseline. By quantizing and grouping point coordinates based on a strict threshold (either axis-wise or Euclidean), our method significantly reduces data size while preserving the geometric fidelity. Experiments on the KITTI dataset demonstrate that, under a 2 cm bounded-error, our single-bin compression reduces the data to 25–35% of their original size, while multi-bin processing can further compress the data to 15–25% of their original volume. An analysis of compression ratios, error metrics, and encoding/decoding speeds shows that our method achieves a substantial data reduction while keeping reconstruction errors within the specified limit. Moreover, runtime profiling indicates that our method is well-suited for deployment on in-vehicle edge devices, thereby enabling scalable cloud-edge cooperation. Full article