Real-Time AQI App using MATLAB and Raspberry Pi

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
12/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Tranquilo
Stack tecnológico
matlab, raspberry-pi
Área
cloud, data, embedded-iot

Línea de trabajo

The issue links to the project repository and a presentation, but does not identify files, tests, or a specific change to make. Start by reviewing the linked repository and presentation to understand the project; the issue does not define a completion criterion.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Dear Professors,

I would like to share our project developed using MATLAB, Raspberry Pi, and a Nova PM Sensor as part of our academic and practical learning activities in IoT, environmental monitoring, and real-time data analytics.

GitHub Repository:
https://github.com/vedantjoliya/AQI

Project Overview

AQI-Real-Time-App.pptx

The primary objective of this project was to design and implement an IoT-based environmental monitoring system capable of continuously measuring and analyzing air quality parameters in real time. The system acquires PM2.5 and PM10 particulate matter data using a Nova particulate matter sensor connected to a Raspberry Pi, while MATLAB is used for data acquisition, processing, visualization, and cloud integration.

The project demonstrates the integration of embedded hardware systems with software-based analytical tools to create a real-time AQI monitoring platform.

Implemented Features

Real-time PM2.5 and PM10 monitoring
AQI calculation and visualization
Raspberry Pi serial communication with sensor hardware
MATLAB-based live dashboard and graphical plotting
Automated Google Sheets cloud logging using Google Apps Script APIs
Continuous real-time environmental data acquisition
Error handling for stable monitoring operation

Technologies Used

MATLAB
Raspberry Pi
Nova PM Sensor
IoT Communication
Google Apps Script
Google Sheets API
Serial Communication Protocols
Learning Outcomes

Through this project, we gained practical experience in:

IoT system development
Embedded sensor integration
Real-time data processing
MATLAB programming and visualization
Cloud-based data logging systems
Hardware-software communication
Environmental analytics and monitoring systems

This project also helped us better understand how intelligent monitoring systems can contribute to smart environmental solutions and real-time analytics applications.

Teammates:
TAUSIF AHMED
DHRUV DANGI
PRIYANSHU VISHNOI

We sincerely thank @MarcoRianiUNIPR, @Asadunipr , @GianlucaMorelliUNIPR, and @AldoCorbelliniUNIPR for their valuable guidance, continuous support, and for providing us the opportunity to present and work on this project.

Thank you.

Lenguaje dominante
MATLAB
Estrellas
30
Forks
1
Sin PR fusionados en 30 d

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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