According to research, the vast majority of road accidents (90%) are the result of human error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart vehicles have gained significant attention as potential solutions to address such issues. In the future of transportation, travel comfort and road safety will be ensured while also offering several value-added services. The automotive industry has undergone a significant transformation through the use of emerging technologies and wireless communication channels, resulting in vehicles becoming more interconnected, intelligent, and safe. However, these technologies and communication systems are susceptible to numerous security attacks. The objective of this paper is to present a comprehensive overview of the smart vehicle’s architecture, encompassing emerging technologies and security challenges and solutions associated with smart vehicles. There has been a significant surge in the utilization of machine learning techniques in smart vehicles. We categorically discuss common security measures, including machine learning and deep learning based solutions that have been mentioned in the literature and implemented against security threats on smart vehicles. This paper has also been titled a tutorial due to its layout, which begins with covering preliminary knowledge, terminologies, and encompassing technologies required to comprehend smart vehicles. Following this, the paper addresses the overall challenges associated with smart vehicles and then focuses on security issues. In terms of solutions, the paper discusses overall solutions to security issues in smart vehicles before delving into a specific solution based on machine learning and deep learning.

Unmanned aerial vehicles (UAVs) offer advantages in serving as aerial small cells (ASCs) to support public safety terrestrial cells (PSTCs) while providing pervasive coverage during disasters. To ensure reliable communications for long-term evolution-based public safety (PS-LTE) users, it is crucial to obtain an accurate understanding of network performance for practical cell association design and network stability. This comprehension is vital for the practical design of cell associations and for maintaining network stability in next-generation consumer wireless networks. For this purpose, we first employ a flexible biased cell association (FBCA) policy that optimally selects the bias factor where a PS-LTE user (PUE) connects to the eNodeB (eNB) giving the maximum power for the received signal. Then, we present a resource allocation and subframe–type selection by formulating stochastic optimization programming to resolve system stability issues in the coexisting PS-LTE and LTE-based high-speed railway (LTE-R) networks and PS-LTE and UAV networks. In addition to this, we employ the Lyapunov optimization technique to seek an optimal almost blank subframe (ABS) algorithm with dynamic delay-aware resource allocation (ADDRA) to resolve the problem of network stability. Using ADDRA, the PS-LTE eNodeB (PSeNB), the aerial eNodeBs (AeNBs), and the LTE-R eNodeBs (ReNBs) obtain up-to-date queues of attached users and accordingly compute a matrix for scheduling resources based on channel state information (CSI) feedback. The simulation results of the UAV-assisted networks using FBCA and ADDRA in coexisting PS-LTE/LTE-R and PS-LTE/UAV networks demonstrate a significant improvement when compared with other state-of-the-art techniques.

To ensure extensive connectivity for a large number of Internet of Things (IoT) devices, there is a critical need for effective random access (RA). However, the substantial number of IoT users accessing the network leads to severe collisions, which cause delays in establishing successful RA connections and ultimately degrade system performance. While recent research has focused on binary preamble detection and the management of random access channel (RACH) overload, it has not specifically addressed RA overload for massive IoT users. This paper proposes a joint solution for preamble collision detection and timing advance (TA) prediction, referred to as PC-TA, inspired by generative adversarial networks (GANs). We utilize an actor-critic-based deep deterministic policy gradient (AC-DDPG) framework as one of the neural networks within the GAN, specifically designed to tackle preamble collision detection and resolution for numerous IoT users. In addition, we implement a fully connected graph neural network (GNN) as the second neural network in the GAN to predict timing advance (TA), which improves the average packet success rate and reduces overall latency. Simulation results validate the effectiveness of the proposed PC-TA, showing an approximate 85% gain over state-of-the-art methods in the existing literature. Consequently, this approach significantly enhances the chances of rapid RA success, allowing each IoT device to achieve successful RA with fewer trials.

The rapid advancement of consumer connected and autonomous vehicle (CAV) technologies offers significant improvements in transportation efficiency, safety, and user convenience. However, these benefits come with substantial cybersecurity risks, as in-vehicle networks and cloud connectivity expose CAVs to increasingly sophisticated cyberattacks. Conventional intrusion detection systems (IDS) often fall short in this domain, as they are not adaptive and struggle to handle the dynamic and stealthy nature of modern attacks. To address these limitations, we propose a novel IDS framework based on a stacking ensemble architecture that integrates multiple machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), as base learners. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) serves as the meta-learner to capture temporal dependencies and sequential patterns in network traffic. To enhance the model’s generalization capability, we incorporate a model-agnostic meta-learning (MAML) approach into the LSTM-RNN meta-learner. The MAML-enhanced set of capabilities enables more effective detection of evolving and previously unseen attack scenarios. Simulation results demonstrate that the proposed framework consistently outperforms standalone LSTM-RNN models, traditional ensemble methods, and individual base learners in detecting complex cyberattack patterns in consumer CAV environments. These findings highlight the potential of meta-learning-driven ensemble IDS frameworks for securing next-generation intelligent transportation systems.