This paper discusses the optimization of cloud computing resources for smart cities by leveraging AI and big data analytics. It explores techniques for managing computational resources effectively, enhancing the performance and scalability of cloud infrastructures while meeting the demands of urban applications.

Benjamin William Scott, Grace Olivia Lewis, Noah Samuel Walker, Isabella Natalie Moore, Alexander James Young

This research explores the integration of edge computing and AI in cloud infrastructure to optimize energy consumption in smart cities. The proposed solution improves the efficiency of cloud services, enabling scalable and cost-effective management of urban energy resources while minimizing carbon footprints.

David Christopher Turner, Grace Julia Evans, Lucas Nathaniel Wright, Sophie Margaret Carter, Matthew Charles Walker

This paper examines the implementation of blockchain technology for secure data sharing among IoT devices in smart cities. By employing decentralized ledger systems, it enhances data privacy, reduces cybersecurity risks, and ensures real-time data validation for urban infrastructure management.

Liam Henry Clark, Olivia Isabella Martinez, Ethan Michael Wright, Ava Charlotte Johnson, Lucas Dylan Parker

This research focuses on the application of AI-driven predictive analytics in managing smart city traffic systems. The proposed model predicts traffic congestion and optimizes signal control, reducing travel time and energy consumption while improving overall urban mobility and safety.

Noah Harrison White, Ella Abigail Thompson, Benjamin David Brown, Alexander Jacob Martin, William Sophia Lee

This paper proposes a cloud-based management system for smart grids in sustainable urban environments. The system leverages real-time data analysis, predictive algorithms, and cloud computing to optimize energy distribution, reduce waste, and improve grid stability in smart cities.

Chloe Madison Harris, Olivia Isabella White, Benjamin Matthew Clark, Liam Daniel Lewis, Alexander James Harris

This study introduces an AI-driven framework for resource allocation and management in scalable cloud data centers. By using machine learning algorithms, the framework dynamically adjusts resource distribution, optimizes performance, and ensures efficient use of energy, reducing operational costs and environmental impact.

Lucas Christopher Miller, Emma Claire Walker, Nathaniel Ryan Davis, Amelia Kate Scott, Oliver Daniel Turner

This paper examines the use of blockchain technology to secure decentralized cloud storage systems. By leveraging decentralized ledger technology, the system ensures data integrity, prevents unauthorized access, and improves privacy for users, making cloud storage more secure for sensitive data in distributed networks.

James Oliver Clark, Emma Grace Walker, Lucas Benjamin Lewis, Olivia Anna James, Sophia Lydia Morgan