The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV). To achieve the goal of automatic driving and efficient mobility, IoV is now combined with modern communication technologies (such as 5G) to achieve intelligent connected vehicles (ICVs). However, IoV is challenged with security risks in the following five (5) domains: ICV security, intelligent device security, service platform security, V2X communication security, and data security. Numerous AI models have been developed to mitigate the impact of intrusion threats on ICVs. On the other hand, the rise in explainable AI (XAI) results from the requirement to inject confidence, transparency, and repeatability into the development of AI for the security of ICV and to provide a safe ITS. As a result, the scope of this review covered the XAI models used in ICV intrusion detection systems (IDSs), their taxonomies, and outstanding research problems. The results of the study show that XAI though in its infancy of application to ICV, is a promising research direction in the quest for improving the network efficiency of ICVs. The paper further reveals that XAI increased transparency will foster its acceptability in the automobile industry.

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust automatic modulation recognition for cognitive radio services of modern communication systems. Our model employs pooling layers, small filter sizes, Gaussian dropout layers, and skip connections which leads to an increase in network capacity, a reinforced process of feature extraction, and prevents the vanishing gradient problem. From our experiments, CGDNet incurs a low computational complexity and reaches the overall n-modulation recognition accuracy of 93.5% and 90.38% on two widely used Deep-Sig datasets.

The increasing complexity of battery management systems (BMS) has led to challenges processing the vast amounts of data required for accurate real-time monitoring and control. Existing BMS frameworks, which rely heavily on artificial intelligence (AI), often struggle with data limitations that impact the precision of state estimates, ultimately affecting battery performance and safety. The integration of digital twin (DT) technology has been proposed to address these challenges. DTs create virtual representations of physical battery systems, enabling enhanced monitoring, predictive maintenance, and optimized performance through advanced AI algorithms. This study presents a comprehensive exploration of DT technology for BMS. First, we review the fundamental concepts, including DTs’ definitions, roles, and high-level architecture in battery management. Second, we examine research and industry-based case studies to identify the necessary technologies and tools for developing robust battery DTs. We propose a detailed framework for integrating DTs with existing BMS infrastructure, focusing on scalability, cost-effectiveness, and practical implementation strategies. Finally, we discuss the open research challenges and future opportunities in the field, emphasizing the potential impact of DTs on the evolution of BMSs.

Advancements in battery management systems involve using digital twins to optimize battery performance in electric vehicles. The state of charge and state of health estimations is essential for battery efficiency and longevity. Digital twins allow for precise predictions of the state of charge and state of health by simulating battery behavior under different conditions. Using artificial intelligence in digital twins, as demonstrated through studies employing deep neural networks and long short-term memory networks, improves predictive capabilities. However, incorporating artificial intelligence presents challenges due to the opaque nature of the models, necessitating the need for explainable artificial intelligence and trustworthy digital twin models. This study pioneered explainable artificial intelligence methods such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and linear regression-based surrogate models to explain the predictions of deep neural networks and long short-term memory networks in digital twin-supported battery management systems. The results reveal that the long short-term memory digital twin model is more reliable due to a higher R ² score and lower mean residuals.

Integrating Explainable Artificial Intelligence (XAI) into marine cyberdefense systems can address the lack of trustworthiness and low interpretability inherent in complex black-box Network Intrusion Detection Systems (NIDS) models. XAI has emerged as a pivotal focus in achieving a zero-trust cybersecurity strategy within marine communication networks. This article presents the development of a zero-trust NIDS framework designed to detect contemporary marine cyberattacks, utilizing two modern datasets (2023 Edge-IIoTset and 2023 CICIoT). The zero-trust NIDS model achieves an optimal Matthews Correlation Coefficient (MCC) score of 97.33% and an F1-score of 99% in a multi-class experiment. The XAI approach leverages visual and quantitative XAI methods, specifically SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-agnostic Explanations (LIME) algorithms, to enhance explainability and interpretability. The research results indicate that current black-box NIDS models deployed for marine cyberdefense can be made more reliable and interpretable, thereby improving the overall cybersecurity posture of marine organizations.