AirSense IoT Platform
Comprehensive air quality monitoring and prediction system
completed6 months
#iot#ml#mobile
Overview
An advanced IoT platform that captures real-time environmental data using ESP32 and multiple sensors. It uploads data at 30-second intervals to Firebase Realtime Database and utilizes Python ML pipelines (ARIMA/LSTM) to predict future air quality indices.
System Architecture
ESP32 with sensors
Firebase Realtime DB
Android App (Kotlin) | Python ML Pipeline
ARIMA/LSTM models
TFLite conversion
Android integration
Tech Stack
ES
ESP32HardwareFi
Firebase RTDBBackendKo
KotlinMobilePy
PythonData ScienceTe
TensorFlowMachine LearningKey Features
- Real-time data upload (30s interval)
- 24h + hourly structured storage
- 13-parameter prediction (next 5 values)
- User authentication
- Dashboard with live charts
- AQI calculation algorithm
Project Gallery
Project Highlights
5+
Sensors
13
Parameters
5 future values
Predictions
30s
Interval
Concept Execution
~/airsense-iot/execute.sh
❯initializing system instance: AirSense IoT Platform...
❯loading dependencies from core storage...
❯connecting parameters: [iot, ml, mobile]
❯system online. executing main loop.
❯▋
