Breathe Clean: Building a Smarter Indoor Air Quality Management System
Breathe Clean is a smart indoor air-quality management project designed to help people make better-informed decisions about ventilation and air purification by considering indoor conditions and outdoor pollution. This article explores the problem, our solution, backend architecture, decision engine, and future integration with AWS IoT Core.
Breathe Clean: Smarter Decisions for
Healthier Indoor Spaces
The Problem
Air pollution is a major concern in cities such as Delhi. People working or studying indoors may face a difficult decision: should they open a window for fresh air or keep it closed to avoid outdoor pollution?
Indoor air conditions also matter. Carbon dioxide (CO₂) can build up in occupied rooms with insufficient ventilation, while outdoor pollution can make opening windows undesirable.
Breathe Clean aims to help people make better-informed decisions by considering indoor air conditions alongside outdoor air quality.
Our Solution
Breathe Clean is a web-based indoor air-quality management application. It combines sensor data, decision-making logic, and a user interface to provide actionable recommendations.
The project's goal is to move beyond simply displaying readings and help users understand what actions may improve their indoor environment.
Key Features
1. Air-Quality Monitoring
The backend supports sensor-data APIs for readings such as indoor AQI, outdoor AQI, CO₂, temperature, humidity, and occupancy.
These measurements can provide context for understanding indoor conditions. Live sensor integration depends on the available hardware and data sources.
2. Intelligent Decision Engine
A rule-based decision engine evaluates environmental readings and recommends suitable actions.
For example:
- When outdoor pollution is high, the system can recommend keeping windows closed and using air purification.
- When CO₂ is elevated and outdoor air quality is acceptable, the system can recommend increasing fresh-air ventilation.
- Under normal conditions, the system can recommend normal ventilation.
These recommendations must consider both indoor and outdoor conditions. Air purifiers that remove particles generally do not remove CO₂, so ventilation may still be necessary when outdoor air is suitable.
3. Device Management
The backend provides APIs for retrieving device state and working with device-related decisions. The system models states such as window position, purifier status, ventilation level, and operating mode.
Actual physical device control requires compatible hardware and a verified connection; simulated or reported device states alone do not establish that physical devices have been controlled.
4. Simulation and Testing
The project includes simulated scenarios for clean air, outdoor pollution, elevated CO₂, and poor air quality.
These scenarios help test how the decision engine responds to different environmental conditions before connecting real sensors and devices.
5. Backend Architecture
The backend uses Node.js and Express to provide API endpoints for sensor data, device state, decisions, and simulations.
SQLite is used for data persistence. Backend testing and debugging help validate the API behavior, decision rules, and reliability of the application.
Technical Approach
The backend is organized into routes, controllers, services, and data modules.
- Routes expose the application's API endpoints.
- Controllers handle requests and responses.
- Decision services evaluate environmental readings.
- Safety validation checks recommended device states.
- SQLite persistence supports storing application data.
This modular structure makes the project easier to test, maintain, and extend.
My Contribution
My main contribution has been backend development. I worked on the Node.js and Express server, sensor and device APIs, the rule-based decision engine, safety validation, simulation scenarios, and automated testing.
I also investigated SQLite persistence and device-state restoration to improve consistency between stored data and the application's API responses.
My focus has been building the logic that turns environmental readings into meaningful recommendations.
Future Scope: AWS IoT Core
A future development goal is to integrate AWS IoT Core to securely connect compatible sensor devices and receive environmental readings in real time.
This could help Breathe Clean move from simulated scenarios toward a connected IoT system. The integration will need to be implemented and tested before it can be considered operational.
Additional future improvements could include more sensor integrations, historical air-quality analysis, improved recommendations, and verified physical device control.
Conclusion
Breathe Clean explores how web development, environmental data, and decision-making logic can work together to support healthier indoor spaces.
By considering indoor CO₂ levels alongside outdoor air pollution, the project aims to help people make more informed decisions about ventilation and air purification.
Breathe Clean — Smarter decisions. Healthier indoor spaces.
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