This research investigates the potential of 5G-enabled IoT to revolutionize smart transportation and autonomous vehicle systems. By enhancing connectivity and real-time data exchange, the system supports efficient navigation, safety, and scalability of autonomous transportation networks.

Charlotte Amelia Baker, Robert Daniel Moore, Liam James Wright, Elizabeth Grace Davis, Thomas John Walker

This paper explores the use of advanced cyber-physical systems to manage smart city infrastructure through IoT and big data. The system provides real-time monitoring, predictive analytics, and optimized decision-making for efficient urban resource management.

Daniel Matthew Jenkins, Alice Claire Walker, Mark Benjamin Thomas, Lucy Catherine Harris, David Samuel Collins

This paper discusses a distributed machine learning model designed for real-time data analytics in edge computing networks. The model ensures efficient processing and decision-making in resource-constrained environments, providing a scalable solution for IoT applications in smart cities and industrial automation.

John Robert King, Emily Mae Parker, George Michael Thompson, Olivia Sophia Harris, Jacob Andrew Miller

This research focuses on leveraging AI and blockchain technologies to enhance data security in cloud environments. The proposed solution utilizes blockchain's immutability and AI's predictive capabilities to detect and mitigate cyber threats, ensuring secure and resilient cloud storage and data sharing.

Christopher James Wright, Amelia Rose Davies, Benjamin Luke Foster, Eleanor Grace Davis, Alexander Ryan Miller

This paper presents a 5G-enabled IoT solution for smart healthcare systems, focusing on remote patient monitoring. By integrating real-time data collection with advanced analytics, the system enhances patient care by providing timely insights and improving healthcare delivery in remote and underserved areas.

Emily Sarah Richardson, James Thomas Brooks, Oliver William Gray, Isabella Charlotte Martin, Matthew Daniel Clarke

This paper explores cloud-based big data analytics for improving real-time traffic management in smart cities. By analyzing traffic patterns and optimizing traffic flow through predictive models, the proposed system contributes to reducing congestion, enhancing mobility, and improving urban transportation infrastructure.

Olivia Sophia Turner, Noah Liam Adams, Amelia Charlotte Evans, Henry Thomas Johnson, Sophia Lily Walker

This paper presents a robust intrusion detection system (IDS) for cloud networks, utilizing machine learning algorithms to detect and mitigate malicious activities. The system dynamically learns from evolving network patterns, providing real-time security and preventing potential data breaches in cloud-based infrastructures.

Jacob Matthew Parker, Ella Sophie Cooper, Thomas Benjamin Hill, Charlotte Emily Green, Henry Samuel Hall