IoT-Based Air Quality Monitoring and AQI Measurement System

Description

This project focuses on creating an IoT-powered system for monitoring air quality using a combination of advanced sensors and machine learning models. It collects real-time environmental data, transmits it via the HC-05 Bluetooth module, and predicts air quality indices using a custom-built mobile app. The solution is designed for urban and industrial settings to help track air quality and ensure healthier living conditions.

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Features

• Real-time Data Collection: Monitors environmental parameters such as temperature, humidity, and pollutant levels using:
  • BME280: Measures temperature, humidity, and pressure.
  • MQ7: Detects carbon monoxide (CO).
  • MQ2: Detects flammable gases like propane and methane.
  • MQ135: Measures air quality by detecting harmful gases.
  • PMS5003: Measures particulate matter (PM2.5 and PM10).
• Data Transmission: Uses the HC-05 Bluetooth module for seamless serial communication.
• Machine Learning Predictions: Leverages ML models in a mobile app for predicting air quality indices.
• User-Friendly Mobile App: Displays real-time sensor data and provides predictions with actionable insights.

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Hardware Requirements

• BME280 sensor
• MQ7 sensor
• MQ2 sensor
• MQ135 sensor
• PMS5003 sensor
• HC-05 Bluetooth module
• Arduino/ESP32/Raspberry Pi (for microcontroller setup)
• Power source

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Software Requirements

• Arduino IDE or relevant microcontroller programming software
• Python (for ML model training and testing)
• Mobile App (developed using Flutter/React Native/other frameworks)
• Libraries:
  • sklearn
  • numpy
  • matplotlib
  • serial

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Installation and Usage

1. Hardware Setup

• Connect the sensors (BME280, MQ7, MQ2, MQ135, PMS5003) to the microcontroller.
• Attach the HC-05 module for data transfer.
• Power up the microcontroller.

2. Software Configuration

• Clone this repository:

  git clone https://github.com/Awais-Asghar/IoT-Based-Air-Quality-Monitoring-System-with-Machine-Learning.git
  cd IoT-Based-Air-Quality-Monitoring-System-with-Machine-Learning

• Program the microcontroller using the Arduino IDE with the provided code in the /microcontroller folder.

3. Data Processing and Prediction

• Run the ML scripts in the /ml-model folder to preprocess and predict air quality indices.
• Install the mobile app from the /app directory to visualize the data.

4. Testing

• Verify sensor readings on a serial monitor.
• Confirm data transmission to the mobile app.

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Project Structure

iot-air-quality-monitoring/
├── app/                  # Mobile app source code
├── microcontroller/      # Code for sensor and Bluetooth setup
├── ml-model/             # ML scripts for predictions
├── data/                 # Collected datasets and preprocessing scripts
├── docs/                 # Documentation and guides
└── README.md             # Project overview