This research explores a cloud-based data analytics platform designed for predictive maintenance in industrial IoT systems. The platform utilizes machine learning algorithms to predict equipment failures, thereby reducing downtime, optimizing performance, and improving operational efficiency in industrial environments.

Samuel David Thompson, Olivia Grace Peterson, John Alexander Carter, Margaret Diane Wood, Robert James Evans

This paper presents an edge computing-based framework for real-time security threat detection in smart city networks. By processing data at the edge, the system can detect and mitigate cyber threats with minimal latency, enhancing the overall security and resilience of urban infrastructures.

Michael Andrew Wilson, Sarah Elizabeth Taylor, Thomas Robert Harris, Caroline Jane Davis, Richard George Miller

This paper discusses the development of a secure cloud-based platform for smart healthcare applications. By integrating blockchain technology, the platform ensures the integrity and privacy of sensitive patient data while enabling seamless sharing across healthcare providers, improving treatment outcomes and operational efficiency.

Emma Victoria Cooper, Daniel James Thompson, Grace Margaret Clark, William Jonathan Lewis, Lucy Anne Walker

This paper explores the use of deep learning algorithms for predictive analytics in smart grids and energy systems. The model enhances load forecasting, fault detection, and energy optimization, contributing to more efficient, reliable, and sustainable energy management in modern smart grids.

Jason Thomas Robinson, Isabella Lily Moore, Benjamin Charles Young, Amelia Sophie Mitchell, Patrick Alan Evans

This research presents a cloud-based security framework for protecting industrial IoT systems in smart cities. The framework utilizes machine learning and encryption techniques to secure the data flow between IoT devices and cloud platforms, ensuring the confidentiality and integrity of critical infrastructure data.

Lucas John Anderson, Olivia Grace Johnson, Michael Peter Carter, Elizabeth Anna Clark, Anthony Edward James

This paper introduces an edge computing framework for real-time analytics in industrial IoT systems. The framework uses predictive maintenance models to monitor and analyze sensor data, allowing for early detection of faults and improving system reliability while minimizing downtime.

Christopher Michael Reed, Rebecca Sarah Johnson, David Andrew Phillips, Emily Jane White, Daniel George Morgan

This paper focuses on the application of AI-driven cybersecurity solutions to protect smart city infrastructures. By leveraging machine learning models, the system can detect, respond to, and mitigate potential cyber threats in real-time, enhancing the resilience of IoT-enabled city systems.

Joshua Thomas Baker, Alexandra Marie Parker, Liam Matthew Cooper, Sophie Anna Harris, George Edward Clark