How to Build a Smart Home Ventilation Controller with Air Quality Monitoring
Create an intelligent home ventilation system that continuously monitors air quality and automatically adjusts airflow. This project combines IoT sensors, machine learning, and smart home integration to maintain healthy indoor air while optimizing energy efficiency.
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Simple Summary
Build a smart home ventilation system that monitors air quality and automatically adjusts airflow for optimal indoor environments.
Product Requirements Document (PRD)
Goals:
- Develop a smart ventilation system that monitors and improves indoor air quality
- Create an intuitive user interface for manual control and system insights
- Integrate with popular smart home platforms for seamless automation
Target Audience:
- Homeowners concerned about indoor air quality
- Smart home enthusiasts
- People with allergies or respiratory sensitivities
Key Features:
- Real-time air quality monitoring (CO2, VOCs, particulate matter)
- Automatic ventilation adjustment based on air quality data
- Integration with smart home systems (e.g., HomeKit, Google Home)
- Mobile app for remote monitoring and control
- Energy efficiency optimization
- Historical data tracking and analysis
User Requirements:
- Easy installation and setup process
- Intuitive mobile app interface
- Customizable alerts and notifications
- Manual override capabilities
- Compatibility with existing HVAC systems
User Flows
-
System Setup:
- User installs air quality sensors and smart vents
- User downloads mobile app and creates account
- App guides user through device pairing and system configuration
-
Daily Operation:
- System continuously monitors air quality
- Ventilation automatically adjusts based on air quality data
- User receives notifications for significant air quality changes
-
Manual Control:
- User opens mobile app to view current air quality status
- User manually adjusts ventilation settings if desired
- System returns to automatic mode after a set time period
Technical Specifications
- Hardware: Custom PCB with ESP32 microcontroller, air quality sensors (MQ-135 for CO2, SGP30 for VOCs, PMS5003 for particulate matter)
- Firmware: C++ with Arduino framework
- Mobile App: React Native for cross-platform development
- Backend: Node.js with Express.js
- Database: MongoDB for data storage
- Cloud Platform: AWS IoT Core for device communication
- Machine Learning: TensorFlow Lite for on-device air quality prediction
- Smart Home Integration: HomeKit and Google Home SDKs
API Endpoints
- /api/auth/register - User registration
- /api/auth/login - User login
- /api/devices - CRUD operations for user's devices
- /api/airquality - Get air quality data
- /api/ventilation - Get/set ventilation settings
- /api/alerts - Manage user alerts and notifications
Database Schema
- Users: id, email, password_hash, name, created_at
- Devices: id, user_id, name, type, location, last_connected
- AirQualityData: id, device_id, timestamp, co2, voc, pm25, temperature, humidity
- VentilationSettings: id, device_id, mode, fan_speed, schedule
- Alerts: id, user_id, type, message, created_at, read_at
File Structure
/
├── firmware/
│ ├── src/
│ │ ├── main.cpp
│ │ ├── sensors.h
│ │ └── wifi_manager.h
│ └── platformio.ini
├── mobile_app/
│ ├── src/
│ │ ├── components/
│ │ ├── screens/
│ │ ├── services/
│ │ └── App.js
│ └── package.json
├── backend/
│ ├── src/
│ │ ├── routes/
│ │ ├── models/
│ │ ├── controllers/
│ │ └── server.js
│ └── package.json
├── ml_models/
│ └── air_quality_predictor.tflite
└── README.md
Implementation Plan
-
Hardware Development (2 weeks)
- Design and prototype custom PCB
- Integrate air quality sensors and microcontroller
- Test hardware functionality
-
Firmware Development (3 weeks)
- Implement sensor data collection and processing
- Develop Wi-Fi connectivity and OTA update system
- Create basic ventilation control algorithms
-
Backend Development (4 weeks)
- Set up Node.js server with Express.js
- Implement user authentication and device management
- Create API endpoints for data storage and retrieval
-
Mobile App Development (5 weeks)
- Design and implement user interface with React Native
- Integrate with backend API
- Develop real-time data visualization and controls
-
Machine Learning Integration (3 weeks)
- Collect and preprocess air quality data
- Train and optimize air quality prediction model
- Integrate TensorFlow Lite model into firmware
-
Smart Home Integration (2 weeks)
- Implement HomeKit and Google Home SDKs
- Test and refine smart home functionality
-
Testing and Refinement (3 weeks)
- Conduct thorough system testing
- Optimize performance and energy efficiency
- Address any bugs or usability issues
-
Documentation and Deployment (2 weeks)
- Create user manuals and setup guides
- Prepare for production deployment
- Plan for ongoing support and updates
Deployment Strategy
- Set up CI/CD pipeline using GitHub Actions for automated testing and builds
- Deploy backend to AWS Elastic Beanstalk for scalability
- Use AWS IoT Core for secure device communication
- Store data in MongoDB Atlas for reliable cloud database
- Distribute mobile app through Apple App Store and Google Play Store
- Implement OTA firmware updates for continuous improvement
- Set up monitoring and alerting using AWS CloudWatch
- Create a phased rollout plan for initial users and gather feedback
Design Rationale
- Custom hardware design allows for precise control and integration of specific sensors
- ESP32 microcontroller chosen for its low power consumption and built-in Wi-Fi capabilities
- React Native enables efficient cross-platform mobile app development
- Node.js backend provides a lightweight and scalable server solution
- MongoDB offers flexibility for storing varied sensor data and user information
- Machine learning integration allows for predictive ventilation control, improving efficiency
- Smart home integration enhances user experience and expands market appeal