This paper presents a novel cloud-based framework for real-time traffic management using IoT devices. The system collects real-time traffic data from sensors, analyzes it in the cloud, and optimizes traffic flow to reduce congestion, enhance safety, and improve overall urban mobility in smart cities.

William James Walker, Emma Grace Lewis, Samuel Alexander Clark, Grace Olivia Harris, John Michael Wright

This paper explores the integration of cloud-enabled smart grids for optimizing energy consumption and performing predictive maintenance. The system uses real-time data from smart meters, cloud analytics, and AI algorithms to forecast demand, improve grid reliability, and reduce energy wastage in urban areas.

Charlotte Isabelle Brooks, Nathaniel Luke Green, Sophia Elizabeth Thomas, Ethan Alexander Carter, Rachel Clara Mitchell

This study discusses the optimization of cloud-based health data storage and processing systems by leveraging machine learning algorithms. The framework enables efficient data management, real-time analytics, and improves decision-making in healthcare settings, facilitating faster diagnosis, personalized treatments, and reducing operational costs in hospitals.

Joshua Daniel White, Abigail Maria Brown, David Christopher Miller, Olivia Jane Thompson, Nathan Andrew Scott

This paper presents a cloud-based IoT solution for precision farming systems. The system integrates IoT sensors with cloud computing to monitor soil moisture, temperature, and other environmental factors. The framework helps farmers optimize irrigation, reduce water usage, and improve crop yield predictions through data-driven insights.

Hannah Elizabeth Lee, Charles Benjamin Cooper, Zoe Margaret Brown, Oliver Theodore Evans, Emma Catherine Harris

This research explores the integration of machine learning-driven cloud computing for intelligent traffic flow prediction in smart cities. By analyzing historical and real-time traffic data, the system predicts traffic patterns, optimizes routing decisions, and reduces congestion, leading to smoother traffic flow and enhanced urban mobility.

Alice Claire Johnson, Benjamin Lucas King, Jessica Anne Wright, Daniel Richard Miller, Christopher Samuel Taylor

This paper explores cloud-based predictive analytics to detect cybersecurity threats in real-time across smart cities. By analyzing large datasets using machine learning algorithms, the system predicts potential security breaches, helping municipalities take proactive measures to secure critical infrastructure and ensure public safety in urban environments.

Daniel Joseph Harris, Victoria Ellen Robinson, Lucas Alexander Shaw, Elizabeth Nicole Evans, Michael James Clark

This study presents a framework for leveraging cloud-based smart grids to integrate renewable energy sources into urban infrastructure. By using cloud computing, the system optimizes energy distribution, forecasts energy demand, and manages storage, reducing dependence on non-renewable resources and promoting sustainable urban development.

Samuel Edward Carter, Amelia Grace Taylor, Nicholas Charles Parker, Margaret Anne Lewis, Oliver Henry Green