Real-Time Air Quality Analysis in UAL High Holborn Lecture Room 308
The current project is centered on the data collected from IoT sensors in Lecture Room 08 at the University of the Arts London, High Holborn. The initial phase, involving comprehensive data visualization, has been successfully completed. The ongoing phase focuses on developing a 3D visualization of the data using advanced 3D Computer Vision techniques, including Gaussian splatting. This process involves generating a 3D model of the lecture room within a few shots and subsequently mapping the visualized data as material onto the 3D model. The aim is to create an immersive and detailed representation of the sensor data, providing valuable insights into the environmental dynamics of the lecture room.
The visualization you see is an innovative representation of real-time air quality data obtained via an IoT sensor device from Lecture Room 308 in the High Holborn Building at the University of the Arts London. This visualization transforms a complex set of values into an intuitive depiction of air quality, focusing on the flow and concentration of particulate matter in a vectorial form. By illustrating the density and movement of air particles of different sizes (0.3 micron, 1 micron, 2.5 micron, and 10 micron), as well as light intensity and the number of devices measuring this data, the visualization provides a dynamic and comprehensive analysis. The choice of using vector fields allows for an effective representation of air movement dynamics, making it possible to observe the intensity and orientation of air particle movement in a spatial domain. Different tones, agents, densities, and velocities are used to discriminate between various particle sizes and concentrations, offering a detailed and comprehensive observation of the ambient air quality in the lecture room.
What’s Utilised:
Coding Languages
HTML
CSS
JavaScript
p5.js
JSON
Services
MQTT Data Server
Webserver.js
Technology
IoT Sensors