This paper presents a hybrid cloud-edge computing approach to optimize smart grid management. The system integrates real-time data analytics from edge devices and cloud platforms to improve grid efficiency, predict energy consumption patterns, and enhance overall energy optimization processes in urban environments.

Michael Robert Clark, Isabella Grace Lewis, Benjamin Charles Walker, Olivia Amelia Harris, Lucas Ethan Moore

This research introduces an AI-driven cloud platform for predictive maintenance in manufacturing plants. Using real-time data analytics and machine learning models, the system detects potential faults in equipment, enabling preemptive maintenance actions and significantly reducing downtime while improving operational efficiency in industrial environments.

Henry Samuel Scott, Grace Emily Johnson, Nathan Alexander Walker, Lucy Sophia Miller, Jack William Carter

This paper explores a dynamic resource allocation model for hybrid cloud architectures. The system leverages cloud and on-premise resources to optimize scalability and efficiency, providing real-time adjustments based on demand, minimizing resource wastage, and improving overall cloud infrastructure performance across various industries.

Aidan Thomas Harris, Sophia Charlotte Allen, David Gabriel King, Olivia Amelia Roberts, James Ethan Walker

This research investigates the role of edge cloud computing in IoT systems for real-time data processing. By processing data closer to the source, the proposed architecture reduces latency, improves efficiency, and provides faster decision-making capabilities, optimizing IoT applications in smart cities and connected environments.

Liam Nathaniel Cooper, Sophia Ava Walker, Jacob Elias Gray, Emma Charlotte Davis, Lucas Samuel Morgan

This study investigates the use of machine learning algorithms to optimize energy consumption in cloud data centers. By leveraging predictive models, the system dynamically adjusts power usage based on workload forecasts, ensuring energy efficiency while maintaining performance levels for cloud computing tasks.

John Alexander Ford, William Thomas Evans, Grace Isabella Mitchell, Samuel David Bennett, Emily Rose Clark

This paper proposes an advanced blockchain-based data security mechanism for cloud computing environments. It focuses on decentralized storage and data verification techniques to ensure data integrity and confidentiality. The system improves trust among users and mitigates risks such as data breaches in the cloud.

Michael Richard Lewis, Olivia Kate Wilson, Daniel Joseph Smith, Alexander Peter Harris, Grace Evelyn Lee