The AI Air Pollution Forecasting is an AI + IoT project that combines Weather station and Alert system, built with PM2.5 sensor. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on Alert system and PM2.5 sensor, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through Weather station, orchestrated with PM2.5 sensor and Alert system. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
PM2.5 sensor
Raspberry Pi
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
ESP32 / Arduino
MQ gas sensors
Modular IoT pipeline around Weather station and Alert system
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through Weather station; the connectivity and AI layer processes it with PM2.5 sensor and Alert system; and the output layer monitors and presents results via Air sensing array. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with Weather station.
Implement connectivity and AI processing using PM2.5 sensor and Alert system.
Add the dashboard and alert layer via Air sensing array.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply Building weather stations and AI quality forecasting
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The AI Air Pollution Forecasting delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
