This study explores strategies for optimizing edge computing systems to enhance real-time data processing and AI inference capabilities. The research proposes architectural improvements and workload allocation models, enabling efficient processing at the edge and reducing latency for critical applications in IoT and AI-driven systems.

Hannah Olivia Adams, Thomas William Davies, Sarah Emily Lewis, Christopher Benjamin Mitchell, Madison Grace Walker

This paper introduces a novel deep learning-based approach for adaptive load balancing in cloud-based systems. By predicting resource demands and dynamically adjusting load distribution strategies, the model enhances scalability and reduces operational costs in cloud computing environments, ensuring high availability and performance.

Joshua Alexander Reed, Olivia Claire Richardson, Nathaniel Joseph Adams, Lily Annabelle Hall, Isaac Daniel Lewis

This research proposes a blockchain-based solution for secure data sharing among IoT devices in smart cities. The model leverages decentralized consensus mechanisms to ensure data integrity and security, while enabling seamless and efficient communication between devices, fostering trust and scalability in urban IoT ecosystems.

Jacob Benjamin Foster, William Michael Davis, Victoria Ann Matthews, Ella Victoria Adams, Lucas James Clark

This paper explores the integration of cloud-edge computing and deep reinforcement learning for intelligent traffic management. The proposed framework utilizes real-time traffic data to optimize routing, reduce congestion, and improve urban mobility, enhancing traffic flow and reducing emissions in smart cities.

Henry Christopher Thompson, Abigail Rose Green, Oliver Nicholas Baker, Charlotte Olivia Wilson, James Robert Foster

This research presents a solution for enhancing cybersecurity in cloud platforms using AI-powered intrusion detection systems. The model utilizes machine learning algorithms to detect anomalies and malicious activities, providing real-time threat detection and protection for cloud infrastructures against evolving cyber threats.

Elizabeth Marie Harris, Alexander Thomas Green, Olivia Sarah Turner, George William Johnson, Matthew Daniel Lee

This paper proposes a hybrid cloud-edge architecture for efficient resource allocation in IoT applications. By leveraging cloud computing for heavy processing tasks and edge computing for real-time data analysis, the model optimizes network usage, enhances response times, and improves IoT system performance.

James Anthony Martin, Abigail Rose Turner, Samuel Joseph Harris, Grace Eleanor Scott, Henry Louis Mitchell

This study investigates the use of advanced edge AI for real-time monitoring and decision support in smart healthcare systems. By integrating AI with edge devices, the system can provide timely diagnosis, track patient health data, and recommend treatments, reducing healthcare costs and improving outcomes.

Oliver William Foster, Charlotte Annabelle Green, Nathaniel Robert Johnson, Emily Grace Turner, Liam Alexander Hughes