This paper proposes a scalable architecture for secure data sharing in cloud environments by integrating encryption techniques with blockchain. This hybrid approach ensures data privacy, integrity, and access control while allowing seamless and secure data sharing across cloud systems and their stakeholders.

Grace Victoria Harris, Thomas Benjamin Scott, Ava Mia Walker, Matthew Samuel Cooper, Ella Amelia Edwards

This research addresses cloud storage allocation challenges in IoT systems by proposing an optimized model for high-volume data processing. The model prioritizes data storage efficiency while ensuring real-time access and scalability, contributing to more efficient management of IoT-generated data across distributed cloud environments.

William Charles Green, Amelia Sophia Johnson, Jack Oliver Martin, Scarlett Emily White, Alexander Lucas Lee

This paper explores advanced edge computing techniques for real-time decision-making in autonomous systems. By leveraging edge processing capabilities, the proposed system reduces latency and improves system response time, enabling autonomous vehicles and robotics to operate more efficiently and reliably in complex environments.

Charlotte Louise Turner, Ethan James Smith, Lily Grace Robinson, Daniel William Baker, Mia Olivia Harris

This paper presents an AI-driven approach for optimizing cloud resource allocation in multi-tenant environments. By leveraging machine learning algorithms, the system dynamically adjusts resources based on real-time demands, improving efficiency and reducing operational costs for cloud service providers and their clients.

James Alexander Mitchell, Sophie Ava Anderson, Liam Noah Collins, Chloe Olivia Moore, Daniel Robert Carter

This paper introduces a blockchain-based access control framework for enhancing security in cloud-based healthcare systems. By combining blockchain’s immutable ledger and smart contracts, the system ensures data integrity, transparency, and secure access control, providing a robust solution for sensitive healthcare data management.

Ella Mia Carter, Oliver Samuel Walker, Emily Charlotte Harris, Lucas Alexander Clark, Mia Grace Edwards

This paper explores the integration of edge AI into IoT healthcare devices for real-time data processing. By enabling local decision-making, edge AI reduces latency, enhances performance, and ensures timely responses in critical healthcare applications, improving patient outcomes and system efficiency.

Emma Olivia Taylor, Jack Noah Walker, Mia Charlotte Harris, Daniel Robert Lee, Sophia Amelia Young

This research focuses on optimizing cloud resource allocation using machine learning techniques to improve service quality. The proposed model analyzes real-time data to predict demand, dynamically allocating resources, and ensuring efficient cloud service delivery while minimizing energy consumption and operational costs.

Liam William Davis, Chloe Elizabeth Thompson, Noah James Robinson, Lucas Matthew Clark, Mia Abigail Harris