AirWatch Pro – Air Quality Intelligence Dashboard

Abierto
  • ¿Algo no está bien?
#205 0 comentarios 3 reacciones 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
15/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Tranquilo
Stack tecnológico
matlab, raspberry-pi, react, typescript
Área
data-visualization, embedded-iot

Línea de trabajo

The issue describes an AirWatch Pro project and links to its repository and slides, but does not specify a change to make in DSwithMATLAB. First check whether the project belongs in this repository and whether a maintainer wants it added; without a defined change or completion criteria, there is no implementation task to start.

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

Descripción

Dear Professors,

I would like to share our project AirWatch Pro, developed using MATLAB, Raspberry Pi, and a Nova SDS011 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/akashlanke/AirWatch-Pro-Air-Quality-Intelligence-Dashboard.git

Projects Slides

airpro project-compressed.pdf

Project Overview

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 SDS011 sensor connected to a Raspberry Pi, while MATLAB is used for data acquisition, processing, visualization, and ML model training. Additionally, a React-based web dashboard provides a cross-platform interface for real-time monitoring and AI insights. 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 with global compliance standards (WHO, US EPA, EU, India NAAQS)
Raspberry Pi UART serial communication with SDS011 sensor (baud rate: 9600)
MATLAB-based live dashboard with graphical plotting and statistical analysis (mean, median, variance)
Client-side AI/ML pipeline:

K-Means Clustering (k=3) for automatic classification into Clean, Moderate, and Polluted zones
Linear Regression for pollution forecasting (predicts next 12 readings)
Z-Score Anomaly Detection for sudden pollution spike alert model for PM2.5 prediction
React + TypeScript web dashboard with Recharts for interactive data visualization
Automated CSV/XLSX data import and live simulation mode
Smart alert system with browser notifications and severity debouncing
AI-powered chatbot for answering plain-language air quality questions

Technologies Used

MATLAB (Data acquisition, , visualization, statistical modeling)
Raspberry Pi 4 (Quad-core ARM Cortex-A72, 4GB RAM, GPIO/UART communication)
Nova SDS011 PM Sensor IoT Communication (UART serial protocol at 9600 baud)
React + TypeScript + Vite (Client-side AI/ML and web dashboard)
Recharts, SheetJS (XLSX) (Data parsing and visualization)
Google Apps Script / Sheets API (Cloud logging – optional extension)
Serial Communication Protocols (Raspberry Pi ↔ SDS011)
Learning Outcomes

Through this project, we gained practical experience in:

IoT system development with Raspberry Pi and sensor integration
Real-time data acquisition and processing via UART serial communication
MATLAB programming for statistical analysis and machine learning
Implementation of client-side AI/ML models (K-Means, Linear Regression,KNN, Z-Score)
Frontend development with React and TypeScript for real-time dashboards
Comparative analysis of global air quality standards (WHO 2021, US EPA, EU, India NAAQS)
Environmental analytics and anomaly detection in time-series data
This project also helped us understand how intelligent monitoring systems can contribute to smart environmental solutions and real-time analytics applications.

Teammates:
AKASH LANKE (@akashlanke),
MUHAMMAD QASEEM (@qaseem97),
ANMOL SUNDRIYAL(@Anmol-dstd)
SNEGA AMBIGAPATHY(@snegaambigapathy)

We sincerely thank @AldoCorbelliniUNIPR , @MarcoRianiUNIPR , @Asadunipr 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.

Más de UniprJRC/DSwithMATLAB

Todos los issues de UniprJRC/DSwithMATLAB

Issues similares

Más issues de MATLAB

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.