The number of damaging cyberattacks is increasing exponentially due in part to lack of user awareness of risky online practices, such as visiting unsafe websites, ignoring warning messages, and communicating with unauthenticated entities. Although research has established the role that game-based learning can play in cognitive development and conceptual learning, relatively few serious mobile games have been developed to educate users about different forms of cyberattack and ways of avoiding them. This paper reports the development of an effective augmented reality (AR) game designed to increase cybersecurity awareness and knowledge in an active and entertaining way. The Cybersecurity Awareness using Augmented Reality (CybAR) game is an AR mobile application that teaches not only cybersecurity concepts, but also demonstrates the consequences of actual cybersecurity attacks through feedback. The design and evaluation of the application are described in detail. A survey was conducted to verify the effectiveness of the game received positive responses from 91 participants. The results indicate that CybAR is useful for players to develop an understanding of cybersecurity attacks and vulnerabilities.

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.

Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This article comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the article reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.

The integration of electric and flying vehicle (EnFV) systems has ushered in a new era of transportation, offering promising solutions for reducing carbon emissions and improving urban mobility. However, optimizing routing in these systems poses significant challenges due to their multi-objective nature, including minimizing energy consumption, travel time and ensuring safety. In this study, we explore applying hybrid metaheuristic approaches as a viable solution to tackle the complex routing optimization problem in EnFV systems. By combining multiple metaheuristic algorithms, these hybrid approaches can offer improved performance and efficiency, contributing to the sustainable evolution of transportation systems. We present an in-depth analysis of this domain's state-of-the-art research, methodologies, and promising results. The paper discusses the background and related work in routing optimization for EnFV systems, highlights various metaheuristic algorithms and their limitations, and introduces hybridization techniques to address the challenges of multi-objective optimization. Real-world case studies demonstrate the successful implementation of hybrid metaheuristic approaches in routing optimization. Performance evaluation and analysis of the advantages and trade-offs of hybridization provide valuable insights into the effectiveness of these approaches. Furthermore, the paper identifies challenges specific to EnFV routing and explores potential areas for future research and enhancements to hybrid metaheuristic techniques. In conclusion, the review underscores the significance of routing optimization in EnFV systems and emphasizes the potential of hybrid metaheuristics to shape the future of sustainable and optimized transportation systems.