Search Results (396)

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 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

Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent malicious takeovers. This research proposes a novel approach to assessing the impact of cyber-attacks on autonomous vehicles and their surroundings, with a strong focus on prioritizing human safety. The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. It is validated using metrics such as MAE, loss function, and Spearman’s correlation through experiments on a dataset of 5410 samples. Beyond enhancing autonomous vehicle security, this research contributes to real-world attack impact assessment, ensuring human safety remains a priority in the evolving autonomous landscape. 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

Vehicular Ad Hoc Networks (VANETs) serve as critical platforms for inter-vehicle communication within constrained ranges, facilitating information exchange. However, the inherent challenge of dynamic network topology poses persistent disruptions, hindering safety and emergency information exchange. An alternative generalised statistical model of the channel is proposed to capture the varying transmission range of the vehicle node. The generalised model framework uses simple wireless fading channel models (Weibull, Nakagami-m, Rayleigh, and lognormal) and the large vehicle obstructions to model the transmission range. This approach simplifies analysis of connection of vehicular nodes in environments were communication links are very unstable from obstructions from large vehicles and varying speeds. The connectivity probability is computed for two traffic models—free-flow and synchronized Gaussian unitary ensemble (GUE)—to simulate vehicle dynamics within a multi-lane road, enhancing the accuracy of VANET modeling. Results show that indeed the dynamic range distribution is impacted at shorter inter-vehicle distances and vehicle connectivity probability is lower with many obstructing vehicles. These findings offer valuable insights into the overall effects of parameters like path loss exponents and vehicle density on connectivity probability, thus providing knowledge on optimizing VANETs in diverse traffic scenarios. Full article

The mix-zone method is effective in preserving real-time vehicle identity and location privacy in Vehicular Ad Hoc Networks (VANETs). However, it has limitations in low-vehicle-density scenarios, where adversaries can still identify the real trajectories of the victim vehicle. To address this issue, researchers often generate numerous fake beacons to deceive attackers, but this increases transmission overhead significantly. Therefore, we propose the Communication-Efficient Pseudonym-Changing Scheme within the Restricted Online Knowledge Scheme (CPCROK) to protect vehicle privacy without causing significant communication overhead in low-density VANETs by generating highly authentic fake beacons to form a single fabricated trajectory. Specifically, the CPCROK consists of three main modules: firstly, a special Kalman filter module that provides real-time, coarse-grained vehicle trajectory estimates to reduce the need for real-time vehicle state information; secondly, a Recurrent Neural Network (RNN) module that enhances predictions within the mix zone by incorporating offline data engineering and considering online vehicle steering angles; and finally, a trajectory generation module that collaborates with the first two to generate highly convincing fake trajectories outside the mix zone. The experimental results confirm that CPCROK effectively reduces the attack success rate by over 90%, outperforming the plain mix-zone scheme and beating other fake beacon schemes by more than 60%. Additionally, CPCROK effectively minimizes transmission overhead by 67%, all while ensuring a high level of protection. Full article

Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular ad hoc network applications now use convolutional neural networks (CNNs). However, the growing model size and latency make implementing them in real-time systems challenging, and most previous studies focusing on using CNNs still face significant challenges. Some effective models with sustainable performances have recently been proposed. One of the most advanced models among them is EfficientNet. One may consider it a family of network models with significantly fewer parameters and computational costs. This paper proposes EfficientNet-based optimized real-time decision-making in the DT-VANET architecture. This paper investigates the performance of EfficientNet in digital-based vehicular ad hoc networks. Extensive experiments have proved that EfficientNet outperforms CNN models (ResNet50, VGG16) in accuracy, latency, computational efficiency, and convergence time, which proves its effectiveness in real-time applications of DT-VANET. Full article

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%. Full article

Road safety is a critical concern, as accidents happen globally. Despite efforts to enhance roads and enforce stricter driving rules, the number of accidents remains high. These issues arise from distracted driving, speeding, and driving under the influence. In the United States, fatal accidents increased by 16% from 2018 to 2022. The number of deaths rose from 36,835 in 2018 to 42,795 in 2022. This trend reveals a critical need for new solutions to reduce traffic incidents and improve road safety. Machine learning (ML) can help make roads safer and reduce traffic-related deaths. This paper presents an ML-based real-time highway traffic incident forecasting system named “VehiCast”. VehiCast utilizes vehicular ad hoc networks (VANETs) and federated learning (FL) to collect real-time traffic data, such as average delay, average speed, and the total number of vehicles across several highway zones, to enhance traffic incident prediction accuracy in real-time. Our extensive experimental results showcase that VehiCast reaches an impressive prediction accuracy of 91%, highlighting the power of innovation and determination. Full article

The exponential growth of wireless traffic has imposed new technical challenges on the Internet and defined new approaches to dealing with its intensive use. Caching, especially cooperative caching, has become a revolutionary paradigm shift to advance environments based on wireless technologies to enable efficient data distribution and support the mobility, scalability, and manageability of wireless networks. Mobile ad hoc networks (MANETs), wireless mesh networks (WMNs), Wireless Sensor Networks (WSNs), and Vehicular ad hoc Networks (VANETs) have adopted caching practices to overcome these hurdles progressively. In this paper, we discuss the problems and issues in the current wireless ad hoc paradigms as well as spotlight versatile cooperative caching as the potential solution to the increasing complications in ad hoc networks. We classify and discuss multiple cooperative caching schemes in distinct wireless communication contexts and highlight the advantages of applicability. Moreover, we identify research directions to further study and enhance caching mechanisms concerning new challenges in wireless networks. This extensive review offers useful findings on the design of sound caching strategies in the pursuit of enhancing next-generation wireless networks. Full article

In vehicular ad hoc networks (VANETs), the presence of untrustworthy nodes poses a significant threat, impacting the network’s reliability. This has led to the emergence of node-centric misbehavior detection as a crucial aspect of VANET security, focusing on the behavior of vehicles rather than the content of their interactions. While the trust model is a popular approach, the computational complexity of trust computations and management in VANETs is attributed to the intricate relationships among vehicles and the dynamic autonomous movement of nodes. To tackle these challenges, we developed a hybrid trust model scheme for node-centric misbehavior detection. Our method represents complex vehicular relationships using a hyper-graph within a dynamic behavior-homogeneous cluster. The model incorporates direct and indirect trust in a multi-layered hybrid trust framework, enabling accurate computation of the aggregate trust level for each cluster member vehicle. Experimental results demonstrate the effectiveness of our scheme, particularly in high-density vehicle cooperation scenarios, highlighting its promising ability to detect misbehaving nodes. Full article

Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML. Full article

Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic topology changes and frequent disruptions of links can lead to large end-to-end delays. To address this issue, we propose the social-based minimum end-to-end delay routing (SMED) algorithm, which leverages the social attributes of both primary and secondary users to reduce end-to-end delay and packet loss. We analyze the influencing factors of vehicle communication in urban road segments and at intersections, formulate the end-to-end delay minimization problem as a nonlinear integer programming problem, and utilize two sub-algorithms to solve this problem. Simulation results show that, compared to the intersection delay-aware routing algorithm (IDRA) and the expected path duration maximization routing algorithm (EPDMR), our method demonstrates significant improvements in both end-to-end delay and packet loss rate. Specifically, the SMED routing algorithm achieved an average reduction of 11.7% in end-to-end delay compared to EPDMR and 25.0% compared to IDRA. Additionally, it lowered the packet loss rate by 24.9% on average compared to EPDMR and 32.5% compared to IDRA. Full article

Traffic congestion imposes burdens on society and individuals. In 2022, the average congestion cost per auto commuter in the USA was USD1259. New possibilities to increase traffic efficiency are now available as vehicles can interact using Vehicular Ad Hoc Network (VANET) systems, a subset of the Internet of Vehicles (IoV). The traffic assignment problem deals with road network traffic optimization. It is a complex and challenging problem. A few solutions incorporating VANET technology have been presented; most are centralized or depend on infrastructure. In previous work, we introduced Road-ACO, an ant colony optimization (ACO), single-hop, decentralized, infrastructure-less, VANET solution. In this paper, we propose a new multi-hop-enabled, decentralized, ant-colony-inspired algorithm for dynamic highway traffic assignment. The algorithm works for large road networks and requires no infrastructure. We develop Veins framework-based simulations to evaluate the algorithm’s performance. The results indicate that the proposed algorithm consistently outperforms Road-ACO and performs optimally on road segments up to 4000 m long, with improvements of up to 40% on average travel time. Full article

Recently, the application of fog-computing technology to vehicular ad hoc networks (VANETs) has rapidly advanced. Despite these advancements, challenges remain in ensuring efficient communication and security. Specifically, there are issues such as the high communication and computation load of authentications and insecure communication over public channels between fog nodes and vehicles. To address these problems, a lightweight and secure authenticated key agreement protocol for confidential communication is proposed. However, we found that the protocol does not offer perfect forward secrecy and is vulnerable to several attacks, such as privileged insider, ephemeral secret leakage, and stolen smart card attacks. Furthermore, their protocol excessively uses elliptic curve cryptography (ECC), resulting in delays in VANET environments where authentication occurs frequently. Therefore, this paper proposes a novel authentication protocol that outperforms other related protocols regarding security and performance. The proposed protocol reduced the usage frequency of ECC primarily using hash and exclusive OR operations. We analyzed the proposed protocol using informal and formal methods, including the real-or-random (RoR) model, Burrows–Abadi–Nikoogadam (BAN) logic, and automated validation of internet security protocols and applications (AVISPA) simulation to show that the proposed protocol is correct and secure against various attacks. Moreover, We compared the computational cost, communication cost, and security features of the proposed protocol with other related protocols and show that the proposed methods have better performance and security than other schemes. As a result, the proposed scheme is more secure and efficient for fog-based VANETs. Full article