This paper explores the current challenges in data privacy and security within cloud computing environments. It presents a survey of recent trends and solutions aimed at securing sensitive data, focusing on encryption techniques, access control mechanisms, and privacy-preserving technologies in cloud infrastructures.

Samuel David Roberts, Grace Emily Johnson, William Alexander Wright, Olivia Sophia Lewis, Benjamin Joseph White

This paper introduces AI-based dynamic scheduling algorithms for optimizing cloud resource allocation. By using machine learning and AI techniques, the proposed approach maximizes resource efficiency and reduces operational costs while meeting service level agreements (SLAs) for diverse cloud applications.

Liam Oliver Anderson, Emma Ava Peterson, Jack Henry Foster, Sophia Grace Harris, Thomas Isaac Mitchell

This paper investigates the potential of blockchain technology to secure cloud data sharing and access control. By implementing decentralized blockchain-based systems, the research aims to improve data security, enhance transparency, and prevent unauthorized access in multi-tenant cloud environments.

James Alexander Cooper, Amelia Rose Hughes, George William Taylor, Isabella Emily Hall, Noah Charles James

This paper proposes an edge computing-based framework for real-time video analytics in smart surveillance systems. By processing video data at the edge, the proposed system reduces latency, minimizes bandwidth usage, and enhances real-time decision-making for video surveillance applications.

Charlotte Lily Walker, Edward Thomas Bennett, Alice Marie King, Daniel Michael Campbell, Rachel Laura Mitchell

This paper explores the use of artificial intelligence (AI) in predictive maintenance for industrial IoT systems. By analyzing sensor data and machine performance, AI algorithms predict equipment failures, reducing downtime, improving operational efficiency, and minimizing maintenance costs in industrial environments.

Matthew Luke Collins, Lily Jane Scott, Benjamin Thomas Rogers, Charlotte Olivia Green, William Samuel Adams

This paper provides a comprehensive survey on cloud-based healthcare data management and analytics in the context of IoT devices. It covers various data collection, processing, and storage models, as well as techniques for real-time monitoring and predictive analytics in health-related applications.

Lucas Evan Carter, Abigail Grace Thompson, Sebastian Miles Harris, Lily Ava Powell, Benjamin Jack Walker

This paper investigates the use of deep learning for image-based threat detection in smart cities' surveillance systems. By leveraging convolutional neural networks (CNNs), the system can effectively detect security threats in real-time, enhancing public safety and supporting efficient law enforcement operations.

Noah Lucas Bennett, Emma Claire Davis, James Riley Robinson, Grace Caroline Clark, Daniel Samuel Lee