ETL automation and mixed reality live data visualization in an IoT system for smart buildings
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Data extraction and processing is an integral part of a complex IoT system. The data visualization interprets huge quantities of data into a graphical 2D and 3D visualization to make it easy to understand the data. The automation of data extraction and processing as well as the automation-driven visualization for continuous data analysis and analytics are the research focus of this thesis. The purpose of this work is to provide real-time IoT sensor data visualization for a digital twin framework aimed at reducing greenhouse gas emissions through low-latency communication between real-time building use and optimized building operation. Here we proposed and implemented a framework that independently computes over the cloud without human intervention, retrieves the sensor endpoint data through API calls at regular time intervals, extracts, processes, and transforms the required parameters from the raw data, and loads the data in scalable docker container local storage. Furthermore, it retrieves the processed data from the docker container local storage through API calls and visualizes the data in the front-end application using various visualization techniques including 2D and 3D bar, line, pie, and heat map graphical representations. In addition, the APIs are created for the processed data to be passed as live data for the augmented reality application and visualized the data in 2D and 3D. The AR application is equipped with a QR reading feature for visualizing device-specific analytics. The visualized 3D analytics in Augmented Reality enables a holistic view of the data and makes cognitive data processing more effective for the user. The automated framework developed for IoT systems and 5G networks in this project has the potential to be used for building energy-efficient and net-zero smart cities in the future.
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M.S.