This paper presents an AI-driven framework for dynamic resource allocation in cloud computing platforms. The model optimizes the processing of real-time sensor data streams, enhancing cloud efficiency while maintaining low latency, ensuring scalability for data-intensive applications like IoT and smart cities.

John Matthew Roberts, Olivia Sophia Martin, Benjamin Christopher Davies, Emma Isabelle Clark, Alexander James Wright

This research introduces a hybrid model for cloud storage optimization in sensor network applications. By combining traditional storage systems with distributed techniques, the approach improves data retrieval times and optimizes storage costs, providing scalable solutions for large-scale IoT deployments and environmental monitoring systems.

James Henry Williams, Emily Katherine Scott, Daniel Michael Cooper, Hannah Rachel Adams, Luke Oliver Harris

This study explores cloud-based sensor fusion techniques aimed at real-time environmental monitoring. By integrating data from various sensor types, the system provides accurate predictive analytics for environmental changes, improving resource management in areas like agriculture, urban planning, and climate monitoring.

George William Johnson, Victoria Catherine Lee, Samuel Thomas Anderson, Patricia Louise Green, Michael John Harris

This paper presents an optimization strategy for cloud-enabled smart sensor networks used in energy-efficient IoT applications. By leveraging edge computing and advanced machine learning algorithms, the model reduces power consumption while maintaining high-quality data processing for real-time decision-making in smart cities and smart homes.

William John Taylor, Emily Jane Parker, Richard Adam Walker, Oliver Mark Thompson, Grace Claire Mitchell

This research proposes an innovative cloud computing model for smart grid data analytics. By leveraging big data and machine learning algorithms, the system provides optimal energy distribution and load balancing, ensuring reliability, cost-efficiency, and sustainability in modern energy grids.

Samuel Andrew Collins, Mary Louise Miller, Alexander Christopher Nelson, Patricia Anne Baker, Daniel John Smith