This paper explores the integration of edge computing and cloud computing for real-time data processing in IoT systems. By processing data closer to the source and offloading intensive tasks to the cloud, the system reduces latency and improves performance in applications like smart homes and industrial monitoring.

David Alexander Carter, Olivia Grace Morgan, Michael James Robinson, Emma Charlotte Harris, John William Young

This paper investigates the use of cloud services in autonomous vehicle systems, focusing on the challenges and opportunities associated with real-time data processing, vehicle-to-cloud communication, and network latency. It highlights potential solutions for improving the efficiency, safety, and scalability of autonomous driving systems.

William Charles Anderson, Emily Louise Brown, Alexander Thomas Taylor, Mia Catherine Mitchell, John David Harris

This paper examines the integration of blockchain and cloud computing technologies to enhance healthcare data security. By using blockchain's decentralized ledger and the cloud's scalability, the solution ensures secure data storage, authentication, and access for sensitive healthcare information across various platforms.

Daniel Joseph Scott, Emma Lily Young, Anthony Brian Collins, Olivia Maria Brown, George James Taylor

This research investigates the use of distributed cloud storage for high-performance computing in scientific simulations. By leveraging the elasticity of cloud storage, the system enables efficient data management, high-throughput computing, and collaboration across geographically distributed research teams, particularly in fields like physics and climate science.

William John Martin, Alice Marie Edwards, Charles David Thompson, Lucas Jonathan White, Sarah Emily Roberts

This paper presents an AI-driven cloud automation framework designed to optimize resource allocation in data centers. The model uses machine learning algorithms to predict workload demands and dynamically allocate resources, improving the efficiency and cost-effectiveness of cloud infrastructure management for large-scale services and applications.

Michael Christopher Clark, Anna Patricia Green, Samuel Robert Lee, Rachel Evelyn Hall, Benjamin Andrew Harris

This research focuses on optimizing machine learning models for real-time sensor data analysis in smart agriculture. By integrating advanced prediction models, the system enhances crop monitoring, pest detection, and soil health analysis, leading to improved decision-making, resource efficiency, and sustainable farming practices.

Olivia Grace Walker, Ethan Jack Thompson, Daniel Edward Williams, Emily Rose Harris, Robert James Carter

This paper presents a cloud-based framework for enhancing the security and privacy of data in remote healthcare monitoring systems. By incorporating encryption, access controls, and advanced authentication mechanisms, the framework ensures secure transmission and storage of sensitive health information across distributed platforms.

William Alexander Foster, John Michael Davis, Rebecca Claire Allen, George Thomas Mitchell, Charlotte Olivia Brown