This paper introduces a smart contract-based access control model for cloud data storage in healthcare systems. The approach leverages blockchain technology to automate access permissions, ensuring secure and auditable data sharing in healthcare, thereby enhancing patient privacy and compliance with regulations.

Matthew Christopher Robinson, Emma Isabella Hayes, Lucas Benjamin Scott, Grace Olivia Foster, James Alexander Wallace

This research investigates various blockchain consensus mechanisms for optimizing energy efficiency in cloud computing environments. By analyzing the energy consumption of Proof-of-Work and Proof-of-Stake models, the study identifies the most efficient solution for sustainable cloud computing and low-cost energy usage in distributed systems.

Daniel Arthur Cooper, Sophia Grace Brooks, Jackson Michael Parker, Olivia Claire Bennett, Benjamin David Hayes

This paper presents a secure and scalable cloud architecture designed for training machine learning models. The architecture integrates blockchain technology to enhance data integrity, privacy, and traceability, ensuring reliable and transparent training processes while maintaining the confidentiality of sensitive training data.

Alexander Ethan Walker, Mia Charlotte Moore, William Jacob Brown, Isabella Grace Thomas, Noah Samuel Clark

This research explores the integration of artificial intelligence with predictive maintenance for industrial IoT systems deployed in cloud environments. By leveraging real-time sensor data and machine learning algorithms, the system predicts equipment failures, enabling proactive maintenance and reducing downtime in industrial operations.

Charlotte Emily Carter, William Henry Davis, Lily Mae Harris, Oliver Thomas Taylor, Jack George Martin

This study proposes cloud-based encryption and authentication techniques for securing Internet of Things (IoT) data in smart cities. The solutions address the challenges of securing real-time data flows and ensuring the privacy of sensitive information across various interconnected IoT devices and cloud systems.

Benjamin Daniel Roberts, Olivia Grace Mitchell, Nathaniel James Walker, Sophia Lily Carter, Mason William Baker

This paper introduces a machine learning-driven edge computing framework for autonomous systems within smart grids. The framework integrates distributed intelligence at the edge, optimizing energy distribution, predicting grid failures, and enhancing system reliability in real-time operations without relying heavily on centralized cloud systems.

Lucas Aiden Ford, Emma Olivia Clark, Mason Lucas Anderson, James Michael Edwards, Isabella Sophia Brooks