This paper explores the optimization of edge computing architectures for IoT applications in smart cities. By distributing computing tasks closer to IoT devices, it enhances the responsiveness and scalability of city-wide smart systems such as traffic management, waste monitoring, and energy efficiency applications.

Jacob Matthew Stone, Elizabeth Sarah Cooper, James Patrick Turner, Olivia Natalie White, Daniel Thomas Harris

This research examines the integration of blockchain technology with cloud computing to enhance data privacy in healthcare systems. By implementing decentralized and secure storage mechanisms, the model aims to prevent unauthorized data access and ensure the privacy and integrity of sensitive medical information.

Adam Lucas Campbell, Olivia Maria Hughes, George Thomas Turner, Sarah Victoria Wright, Michael Anthony Davis

This paper proposes an AI-powered predictive maintenance system for cloud infrastructure using IoT sensors. By leveraging machine learning algorithms, the system can predict hardware failures in advance, enabling proactive maintenance and minimizing downtime in large-scale cloud data centers.

Matthew Samuel Brooks, Daniel Christopher Lewis, Alice Margaret Thompson, Ethan Andrew Wright, Sophia Claire Evans

This paper investigates the use of deep reinforcement learning for efficient resource allocation in hybrid cloud environments. By utilizing AI-based algorithms to optimize resource scheduling and task distribution, the system aims to improve cost-efficiency and performance in mixed cloud-edge architectures.

Lucas Benjamin Harris, William John Carter, Emily Rose Parker, Thomas Ryan Mitchell, Olivia Jane Foster

This paper explores the use of AI-driven demand forecasting to optimize cloud service pricing models. By accurately predicting customer demand, cloud service providers can adjust prices dynamically, improving their competitive edge and maximizing revenue while ensuring cost-effective resource allocation for users.

John Daniel Morgan, Sarah Katherine Clark, Matthew Alexander Stewart, Olivia Maria Young, Christopher James Lee

This paper explores the integration of Internet of Things (IoT) devices and cloud computing technologies for efficient management of smart grids in urban environments. The proposed system optimizes energy distribution, reduces outages, and enhances grid performance through real-time data collection and predictive analytics.

Lucas James Anderson, Amelia Claire Moore, Joshua Benjamin King, Sophia Grace Thompson, Michael David Robinson

This research presents the latest advancements in deep learning algorithms for real-time object detection in autonomous vehicles. The paper discusses the optimization of neural networks for enhanced accuracy, faster processing times, and improved safety in dynamic driving environments, making self-driving cars more reliable and effective.

Henry Charles Carter, Olivia Marie Evans, Samuel William Harris, Isabella Grace Wilson, Thomas Andrew Johnson