Cloud-Enabled Mobile App with Machine Learning for Smart IoT Application
Disciplines
Computer Engineering | Digital Communications and Networking | Systems and Communications
Abstract (300 words maximum)
Smart IoT applications such as smart meters, smart street lights, smart bins and asset tracking all require sensors to gather data for real-time data analysis. We propose to develop a cloud-based mobile application, where sensors transmit real-time data to a cloud-based platform through a central gateway. The data is stored in a dynamic, real-time database and accessible through a dedicated mobile application. Furthermore, our system integrates cutting-edge machine-learning algorithms that analyze the collected data to predict accurate results. By leveraging historical data and real-time measurements, our system offers proactive insights about the data collected. This application can be made available to the users for ease of use.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Ahyoung Lee
Cloud-Enabled Mobile App with Machine Learning for Smart IoT Application
Smart IoT applications such as smart meters, smart street lights, smart bins and asset tracking all require sensors to gather data for real-time data analysis. We propose to develop a cloud-based mobile application, where sensors transmit real-time data to a cloud-based platform through a central gateway. The data is stored in a dynamic, real-time database and accessible through a dedicated mobile application. Furthermore, our system integrates cutting-edge machine-learning algorithms that analyze the collected data to predict accurate results. By leveraging historical data and real-time measurements, our system offers proactive insights about the data collected. This application can be made available to the users for ease of use.