Search Results (201)

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

Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

Vehicular Communication Networks (VCNs) are essential to intelligent transportation systems, where real-time data exchange between vehicles and infrastructure supports safety, efficiency, and automation. However, achieving high Quality of Service (QoS)—especially under delay-sensitive conditions—remains a major challenge due to the high mobility and dynamic topology of vehicular environments. While some efforts have explored routing protocol optimization, few have systematically compared multiple optimization approaches tailored to distinct traffic and delay conditions. This study addresses this gap by evaluating and enhancing two widely used routing protocols, QOS-AODV and GPSR, through their improved versions, CM-QOS-AODV and CM-GPSR. Two distinct optimization models are proposed: the Traffic-Oriented Model (TOM), designed to handle variable and high-traffic conditions, and the Delay-Efficient Model (DEM), focused on reducing latency for time-critical scenarios. Performance was evaluated using key QoS metrics: throughput (rate of successful data delivery), packet delivery ratio (PDR) (percentage of successfully delivered packets), and end-to-end delay (latency between sender and receiver). Simulation results reveal that TOM-optimized protocols achieve up to 10% higher PDR, maintain throughput above 0.40 Mbps, and reduce delay to as low as 0.01 s, making them suitable for applications such as collision avoidance and emergency alerts. DEM-based variants offer balanced, moderate improvements, making them better suited for general-purpose VCN applications. These findings underscore the importance of traffic- and delay-aware protocol design in developing robust, QoS-compliant vehicular communication systems. Full article

Background: Trauma-related injuries are among the most common reasons for paediatric hospital presentations and represent a substantial component of orthopaedic care. Their management poses unique challenges due to ongoing skeletal development in children. While most reported fractures occur at home or during sports, prior studies have primarily used data from urban European populations, limiting the relevance of their findings for rural and regional settings. Urban-centred research often informs public healthcare guidelines, treatment algorithms, and infrastructure planning, introducing a bias when findings are generalised outside of metropolitan populations. This study addresses that gap by analysing fracture data from two rural trauma centres in New South Wales, Australia. This study assesses paediatric fractures resulting from severe injury mechanisms in rural areas, identifying common fracture types, underlying mechanisms, and treatment approaches to highlight differences in demographics. These findings aim to cast a light on healthcare challenges that regional areas face and to improve the overall cultural safety of children who live and grow up outside of the metropolitan trauma networks. Methods: We analysed data from two major rural referral hospitals in New South Wales (NSW) for paediatric injuries presenting between 1 January 2018 and 31 December 2022. This study included 150 patients presenting with fractures following severe mechanisms of injury, triaged into Australasian Triage Scale (ATS) categories 1 and 2 upon initial presentation. Results: A total of 150 severe fractures were identified, primarily affecting the upper and lower limbs. Males presented more frequently than females, and children aged 10–14 years old were most commonly affected. High-energy trauma from motorcycle (dirt bike) accidents was the leading mechanism of injury among all patients, and accounted for >50% of injuries among 10–14-year-old patients. The most common fractures sustained in these events were upper limb fractures, notably of the clavicle (n = 26, 17.3%) and combined radius/ulna fractures (n = 26, 17.3%). Conclusions: Paediatric trauma in regional Australia presents a unique and under-reported challenge, with high-energy injuries frequently linked to unregulated underage dirt bike use. Unlike urban centres where low-energy mechanisms dominate, rural areas require targeted prevention strategies. While most cases were appropriately managed locally, some were transferred to tertiary centres. These findings lay the groundwork for multi-centre research, and support the need for region-specific policy reform in the form of improved formal injury surveillance, injury prevention initiatives, and the regulation of under-aged off-road vehicular usage. Full article

Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

This work presents a novel dual-polarized antenna array tailored for Internet of Things (IoT) applications, specifically designed to operate in the millimeter-wave (mm-wave) spectrum within the frequency range of 30–60 GHz. Leveraging printed ridge gap waveguide (PRGW) technology, the antenna ensures robust performance by eliminating parasitic radiation from the feed network, thus significantly enhancing the reliability and efficiency required by IoT communication systems, particularly for smart cities, autonomous vehicles, and high-speed sensor networks. The proposed antenna achieves superior radiation characteristics through a cross-shaped magneto-electric (ME) dipole backed by an artificial magnetic conductor (AMC) cavity and electromagnetic bandgap (EBG) structures. These features suppress surface waves, reduce edge diffraction, and minimize back-lobe emissions, enabling stable, high-quality IoT connectivity. The antenna demonstrates a wide impedance bandwidth of 24% centered at 30 GHz and exceptional isolation exceeding 40 dB, ensuring interference-free dual-polarized operation crucial for densely populated IoT environments. Fabrication and testing validate the design, consistently achieving a gain of approximately 13.88 dBi across the operational bandwidth. The antenna’s performance effectively addresses the critical requirements of emerging IoT systems, including ultra-high data throughput, reduced latency, and robust wireless connectivity, essential for real-time applications such as healthcare monitoring, vehicular communication, and smart infrastructure. Full article

With increasing vehicle density and growing demands on transport infrastructure, there is a need for resilient, low-cost communication systems capable of supporting safety-critical applications, especially in situations where primary channels like Wi-Fi or LTE are unavailable. This paper proposes a novel, real-time vehicular network protocol that functions as an emergency fallback communication layer using long-range LoRa modulation and off-the-shelf hardware. The core contribution is a development of Mobile Cell Broadcast Protocol that is implemented using Long-Range modulation and time-division multiple access (TDMA)-based cell broadcast protocol (LoRA TDMA) capable of supporting up to six dynamic clients to connect and exchange lightweight cooperative awareness messages. The system achieves a sub-100 ms node notification latency, meeting key low-latency requirements for safety use cases. Unlike conventional ITS stacks, the focus here is not on full-featured data exchange but on maintaining essential communication under constrained conditions. Protocol has been tested in laboratory to check its ability to ensure real-time data exchange between dynamic network nodes having 14 bytes of payload per data packet and 100 ms network member notification latency. While focused on vehicular safety, the solution is also applicable to autonomous agents (robots, drones) operating in infrastructure-limited environments. Full article

Vehicular networks utilize wireless communication among vehicles and between vehicles and infrastructures. While vehicular networks offer a wide range of benefits, the security of these networks is critical for ensuring public safety. The transmission of false information by malicious nodes (vehicles) for selfish gain is a security issue in vehicular networks. Mitigating false information is essential to reduce the potential risks posed to public safety. Existing methods for false information detection in vehicular networks utilize various approaches, including machine learning, blockchain, trust scores, and statistical techniques. These methods often rely on past information about vehicles, historical data for training machine learning models, or coordination between vehicles without considering the trustworthiness of the vehicles. To address these limitations, we propose a technique for False Information Mitigation using Pattern-based Anomaly Detection (FIM-PAD). The novelty of FIM-PAD lies in using an unsupervised learning approach to learn the usual patterns between the direction of travel and speed of vehicles, considering the variations in vehicles’ speeds in different directions. FIM-PAD uses only real-time network characteristics to detect the malicious vehicles that do not conform to the identified usual patterns. The objective of FIM-PAD is to accurately detect false information in vehicular networks with minimal processing delays. Our performance evaluations in networks with high proportions of malicious nodes confirm that FIM-PAD on average offers a 38% lower data processing delay and at least 19% lower false positive rate compared to three other existing techniques. Full article

As security issues in vehicular networks continue to intensify, ensuring the trustworthiness of message exchanges among vehicles, infrastructure, and cloud platforms has become increasingly critical. Although trust authentication serves as a fundamental solution to this challenge, existing models fail to effectively address the specific requirements of vehicular networks, particularly in defending against malicious evaluations. This paper proposes a novel multidimensional trust evaluation framework that integrates both static and dynamic metrics. To tackle the issue of malicious ratings in peer assessments, a rating reversal mechanism based on K-means clustering is designed to effectively identify and correct abnormal trust feedback. In addition, the framework incorporates an entropy-based trust weight allocation mechanism and a time decay model to enhance adaptability in dynamic environments. The simulation results demonstrate that, compared with traditional approaches, the proposed scheme improves the average successful information rate by 12% and reduces the false positive rate to 6.1%, confirming its superior performance in securing communications within the vehicular network ecosystem. Full article

With the rapid increase in the number of vehicles and the growing demand for low-latency and reliable communication, traditional vehicular network architectures face numerous challenges. Unmanned Aerial Vehicle (UAV)-assisted vehicular networks provide an innovative solution for real-time data transmission and efficient cross-domain communication, significantly enhancing resource allocation efficiency and traffic safety. However, these networks also raise privacy and security concerns. Traditional symmetric key and Public Key Infrastructure (PKI)-based authentication schemes suffer from issues such as key management, certificate verification, and data leakage risks. While blockchain technology has been explored to address these problems, it still suffers from inefficiencies and high computational overhead. This paper proposes a UAV-assisted vehicular network architecture that leverages UAVs as trusted intermediaries for cross-domain authentication, effectively reducing authentication delays and improving scalability. Through ProVerif security proofs and detailed theoretical analysis, the proposed scheme is demonstrated to meet the security requirements of vehicular networks and withstand a broader range of attacks. Performance evaluation results show that the proposed scheme achieves at least a 20% reduction in computational and communication overhead compared to existing schemes, highlighting its significant advantages. Additionally, the average consensus time for the proposed scheme is at least 40% lower than existing schemes. The novelty of the proposed scheme lies in the integration of UAVs as trusted intermediaries with blockchain technology, addressing key management and privacy issues, and providing an efficient and secure solution for high-density vehicular networks. Full article

We conduct a cryptanalysis of the Vehicle-to-Infrastructure (V2I) handover authentication protocol newly developed by Son et al., which incorporates blockchain technology for authentication purposes. Although this approach is notably efficient, our analysis reveals that the protocol is vulnerable to vehicle impersonation attacks, traceability attacks, and trusted authority (TA) circumvention attacks. To address these security vulnerabilities, we propose an enhanced protocol integrating Schnorr signature-based authentication, dynamically refreshed temporary identities, and TA-anchored credential mechanisms. We validate its security through heuristic analysis and formal verification using ProVerif. Furthermore, a comprehensive comparison with various related schemes confirms that the new protocol achieves a higher level of security while simultaneously maintaining satisfactory efficiency in both the computational and communication aspects. Full article

Vehicular Internet of Things (V-IoT) systems will be critical in advancing intelligent transportation networks because of the easy communication they make possible between vehicles, roadside infrastructure, and other network entities. Integrating adaptive IoT-based communication models will increase resource utilization and allow multiple communications in vehicular networks. This work proposes an Adaptive Multi-mode Spectrum Access (AMSA) approach for optimal Quality of Service (QoS) in multi-class V-IoT networks. Unlike traditional static spectrum access methods, AMSA switches between interweave, underlay, and coexistence modes based on network conditions. Our results indicate that AMSA improves spectrum usage by 56% over static spectrum access improves throughput by 110%, and reduces delay for low-priority traffic by up to 47.5%. This new integration offers robust vehicular communication with optimal resource allocation under different network scenarios. Full article

Vehicular Ad Hoc Networks play a crucial role in enabling Smart City applications by facilitating seamless communication between vehicles and infrastructure. This study evaluates the throughput performance of different routing protocols, specifically AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM, under various city and highway scenarios in complex networks. The analysis covers key parameters including traffic generation, packet sizes, mobility speeds, and pause times. Results indicate that TOM and DEM profiles significantly improve throughput compared to traditional AODV and GPSR protocols. GPSR:TOM achieves the highest throughput across most scenarios, making it a promising solution for high-performance data transmission in Smart Cities. For instance, GPSR:TOM achieves an average throughput of 3.2 Mbps in city scenarios compared to 2.8 Mbps for GPSR, while in highway scenarios, the throughput increases to 3.6 Mbps. Additionally, AODV:DEM records a throughput of 3.4 Mbps for high traffic generation, outperforming AODV:TOM at 3.1 Mbps and baseline AODV at 2.7 Mbps. The findings highlight the importance of optimizing data throughput to ensure reliability and efficiency in complex vehicle interconnection systems, which are critical for traffic management, accident prevention, and real-time communication in smart urban environments. Full article