This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg-Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura-Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feed-forward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg-Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained.

Over the past decade, the groundbreaking technological advancements in the Internet of Vehicles (IoV) coupled with the notion of trust have attracted increasing attention from researchers and experts in intelligent transportation systems (ITS), wherein vehicles establish a belief towards their peers in the pursuit of ensuring safe and efficacious traffic flows. Diverse domains have been taking advantage of trust management models in the quest of alleviating diverse insider attacks, wherein messages generated by legitimate users are altered or counterfeited by malicious entities, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. In the course of vehicles forming perceptions towards other participating vehicles, a range of contributing parameters regarding the interactions among these vehicles are accumulated to establish a final opinion towards a target vehicle. The significance of these contributing parameters is typically represented by associating a weighting factor to each contributing attribute. The values assigned to these weighting factors are often set manually, i.e., these values are predefined and do not take into consideration any affecting parameters. Furthermore, a threshold is specified manually that classifies the vehicles into honest and dishonest vehicles relying on the computed trust. Moreover, adversary models as an extension to trust management models in order to tackle the variants of insider attacks are being extensively emphasized in the literature. This paper, therefore, reviews the state of the art in the vehicular trust management focusing on the aforementioned factors such as quantification of weights, quantification of threshold, misbehavior detection, etc. Moreover, an overarching IoV architecture, constituents within the notion of trust, and attacks relating to the IoV have also been presented in addition to open research challenges in the subject domain.

Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data.

Trust evaluation and management schemes have been extensively employed in a bid to alleviate diverse insider attacks. These trust values are often ascertained by taking into consideration trust parameters to determine the honesty and reliability of a vehicle and evaluating a weighted sum of the said parameters. To achieve precision and to reflect a reasonable impact of these parameters, rational weight values are imperative. Accordingly, this research primarily emphasizes on the quantification of weights associated with the contributing trust attributes by proposing a novel trust management mechanism that utilizes contextual information in addition to employing relevant impacting quantities as weights to formulate trust evaluations. Moreover, the envisaged trust management model incorporates 1) attack resilience while constituting certain parameters and 2) an adaptive and flexible threshold to mitigate malevolent behaviors. The simulation results depict that the devised parameters and the formulated trust aggregation cater to the dynamic nature of vehicular networks demonstrating the rationality of the weights’ quantification, and the introduction of the adaptive threshold for misbehavior detection aligns well with the requirements of the ever-changing vehicular networks.

The vision for 6G aims to enhance network capabilities, supporting an intelligent digital ecosystem where artificial intelligence (AI) is a key. However, the expansion of 6G raises critical security and privacy concerns due to the increased integration of IoT devices, edge computing, and AI. This survey provides a comprehensive overview of 6G protocols with a focus on security and privacy, identifying risks that have not been experienced in preceding 5G systems, and presenting mitigation strategies. While many vulnerabilities from earlier generations persist, the introduction of AI/ML introduces novel risks like model inversion and malicious manipulation of AI. Vulnerabilities in emerging personal IoT networks and autonomous vehicles are also underscored, where falsified command signaling or privacy leakage can pose safety and ethical concerns. The survey also discusses the transition toward lattice-based, post-quantum encryption standards, and identifies limitations in current security frameworks and calls for new, dynamic approaches tailored to 6G’s complexity. Close collaboration among stakeholders, including governments, industry, and researchers, is indispensable to developing robust standards, secure architectures, and risk assessment frameworks that address AI, quantum threats, and privacy at scale.