Search Results (266)

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 rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. 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

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

The Internet of Vehicles (IoV) presents a vast opportunity for optimised traffic flow, road safety, and enhanced usage experience with the influence of Federated Learning (FL). However, the distributed nature of IoV networks creates certain inherent problems regarding data privacy, security from adversarial attacks, and the handling of available resources. This paper introduces Fed-DTB, a new dynamic trust-based framework for FL that aims to overcome these challenges in the context of IoV. Fed-DTB integrates the adaptive trust evaluation that is capable of quickly identifying and excluding malicious clients to maintain the authenticity of the learning process. A performance comparison with previous approaches is shown, where the Fed-DTB method improves accuracy in the first two training rounds and decreases the per-round training time. The Fed-DTB is robust to non-IID data distributions and outperforms all other state-of-the-art approaches regarding the final accuracy (87–88%), convergence rate, and adversary detection (99.86% accuracy). The key contributions include (1) a multi-factor trust evaluation mechanism with seven contextual factors, (2) correlation-based adaptive weighting that dynamically prioritises trust factors based on vehicular conditions, and (3) an optimisation-based client selection strategy that maximises collaborative reliability. This work opens up opportunities for more accurate, secure, and private collaborative learning in future intelligent transportation systems with the help of federated learning while overcoming the conventional trade-off of security vs. efficiency. Full article

The emergence of the Internet of Vehicles (IoV) has revolutionized intelligent transportation and communication systems. However, IoV presents many complex and ever-changing security challenges and thus requires robust cybersecurity protocols. This paper comprehensively describes and evaluates ensemble learning approaches for multi-class intrusion detection systems in the IoV environment. The study evaluates several approaches, such as stacking, voting, boosting, and bagging. A comprehensive review of the literature spanning 2020 to 2025 reveals important trends and topics that require further investigation and the relative merits of different ensemble approaches. The NSL-KDD, CICIDS2017, and UNSW-NB15 datasets are widely used to evaluate the performance of Ensemble Learning-Based Intrusion Detection Systems (ELIDS). ELIDS evaluation is usually carried out using some popular performance metrics, including Precision, Accuracy, Recall, F1-score, and Area Under Receiver Operating Characteristic Curve (AUC-ROC), which were used to evaluate and measure the effectiveness of different ensemble learning methods. Given the increasing complexity and frequency of cyber threats in IoV environments, ensemble learning methods such as bagging, boosting, and stacking enhance adaptability and robustness. These methods aggregate multiple learners to improve detection rates, reduce false positives, and ensure more resilient intrusion detection models that can evolve alongside emerging attack patterns. Full article

Vehicular visible light communication (VVLC) with ultralow electromagnetic interference has great potential to propel the growth of the Internet of Vehicles (IoV). However, ensuring quick response times and minimal delays in VVLC is a significant challenge brought on by fast-moving vehicles. In response to this problem, we propose a heuristic optical flow scheduling algorithm. First, the optical flow scheduling problem of VVLC is built as a multi-objective optimization model considering the makespan, delay, schedulable ratio, and bandwidth utilization with non-conflict constraints. Second, an improved artificial plant community (APC) algorithm with enhanced global and local search capabilities is proposed to achieve low-delay communication for time-sensitive optical flows. Finally, a series of benchmark experiments are conducted to show that the proposed algorithm can efficiently schedule optical flows with minimal delay. The cost of this algorithm is very low, and it is suitable for deployment on edge computing platforms such as vehicles. Full article

The Internet of Things (IoT) has enabled the development of various applications for energy, exploiting unprecedented data collection, multi-stage data processing, enhanced awareness, and control of the physical environment. In this context, the availability of tools for efficient development is paramount. This paper explores and validates the use of a generic, flexible, open-source measurement-oriented data collection framework for the energy field, namely Measurify, in the Internet of Things (IoT) context. Based on a literature analysis, we have spotted three domains (namely, vehicular batteries, low voltage (LV) test feeder, and home energy-management system) and defined for each one of them an application (namely: range prediction, power flow analysis, and appliance scheduling), to verify the impact given by the use of the proposed IoT framework. We modeled each one of them with Measurify, mapping the energy field items into the abstract resources provided by the framework. From our experience in the three applications, we highlight the generality of Measurify, with straightforward modeling capabilities and rapid deployment time. We thus argue for the importance for practitioners of using powerful big data management development tools to improve efficiency and effectiveness in the life-cycle of IoT applications, also in the energy domain. Full article

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors. Full article

Intelligent transport systems are essential for urban residents in large cities, facilitating not only vehicular mobility but also the movement of residents. Urban mobility is a significant concern, particularly in the context of the Internet of Things, where vehicles evolve into intelligent nodes within sensor networks. This convergence of the mobile Internet and the Internet of Vehicles (IoV) redefines urban mobility. In the context of smart cities, it examines the evolving IoV and communication models, unveiling both current and emerging trends. This research paper offers insights into global market trends and conducts bibliographic data analysis to illuminate the present and future potential of the IoV. It highlights IoV applications, the layered architecture, and connected and autonomous vehicle levels (Level 0 to Level 5). The communication model is explained, along with addressing research challenges and future directions. The conclusion summarizes the key findings and emphasizes the main points addressed in the study. 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

Vehicular Internet of Things (V-IoT) networks, sustained by a high-density deployment of roadside units and sensor-equipped vehicles, are currently at the edge of next-generation intelligent transportation system evolution. However, offering stable, low-latency, and energy-efficient communication in such heterogeneous and delay-prone environments is challenging due to limited spectral resources and diverse quality of service (QoS) requirements. This paper presents a Priority-Aware Spectrum Management (PASM) scheme for IoT-based vehicular networks. This dynamic spectrum access scheme integrates interweave, underlay, and coexistence modes to optimize spectrum utilization, energy efficiency, and throughput while minimizing blocking and interruption probabilities. The algorithm manages resources efficiently and gives proper attention to each device based on its priority, so all IoT devices, from high to low priority, receive continuous and reliable service. A Continuous-Time Markov Chain (CTMC) model is derived to analyze the proposed algorithm for various network loads. Simulation results indicate improved spectral efficiency, throughput, delay, and overall QoS compliance over conventional access methods. These findings establish that the proposed algorithm is a scalable solution for dynamic V-IoT environments. Full article

In order to enhance the precision of network traffic prediction for multi-area vehicle networks, this paper proposes a two-tier distributed Software-Defined Vehicular Network (SDVN) architecture equipped with multiple controllers, which is subsequently deployed to manage traffic across regions, thus minimizing communication costs and enabling seamless vehicle movement. We firstly build on control plane placement (CPP) research, focusing on deployment strategies that impact scalability, performance, and fault tolerance. It highlights the importance of hierarchical decision-making with multiple controllers handling varying traffic demands. A comprehensive comparison of various machine learning and deep learning algorithms is then conducted to evaluate their efficacy in forecasting SDVN traffic patterns, which is crucial for system efficiency. Experiments show the proposed architecture’s effectiveness in traffic prediction and management in Shenzhen’s Longhua New District. The study confirms that the SDVN system enhances traffic prediction and management, improving urban mobility and Internet of Vehicles. The paper offers a framework using advanced technologies to address challenges in traffic prediction and management in modern vehicular networks. Full article

With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However, traditional centralized model training faces challenges such as high network load due to massive data transmission, energy management difficulties for mobile devices like UAVs, and privacy risks associated with non-anonymized road operation data. Therefore, this paper proposes an air–ground collaborative federated learning framework that integrates Bayesian prediction and an incentive mechanism to achieve privacy protection and communication optimization through localized model training and differentiated incentive strategies. Simulation experiments demonstrate that, compared to the Equal Contribution Algorithm (ECA) and the Importance Contribution Algorithm (ICA), the proposed method improves model convergence speed while reducing incentive costs, providing theoretical support for the reliable operation of large-scale intelligent traffic monitoring systems. 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