This project is a Temperature and Humidity Monitoring System designed to collect real-time environmental data using a DHT22 sensor connected to a Raspberry Pi. It visualizes the data on an interactive graph and triggers a notification (via an LED) when the temperature exceeds a predefined threshold.
- Data Collection:
- Real-time temperature (°C) and humidity (%RH) readings from the DHT22 sensor.
- Threshold Notification:
- An LED blinks when the temperature exceeds 20°C.
- Graph Visualization:
- Displays temperature and humidity trends over time.
- Includes threshold lines for better visual clarity.
- Custom UI:
- A professional and interactive graph created using HTML, CSS, and Chart.js.
- Raspberry Pi (any model with GPIO support)
- DHT22 Temperature and Humidity Sensor
- LED and Resistor (330Ω recommended)
- Jumper wires and a breadboard
| Component | Raspberry Pi Pin |
|---|---|
| DHT22 Signal | GPIO4 (Pin 7) |
| LED Positive | GPIO17 (Pin 11) |
| LED Negative | Ground (GND) |
| DHT22 VCC | 3.3V (Pin 1) |
| DHT22 GND | Ground (Pin 6) |
The graph includes:
- Temperature (°C) and Humidity (%RH) trends.
- Threshold lines for both metrics.
- Graphs: Real-time visualization of temperature and humidity trends.
- LED Notifications: Alerts via blinking LED when the temperature exceeds the threshold.
- Real-Time Data Processing: Reduced latency by leveraging fog computing near data sources.
- Comparison of Libraries:
- Implemented the project using Adafruit DHT library for simplicity and reliability.
- Created an alternative solution without external libraries using custom GPIO-based DHT22 data handling for better understanding of low-level data processing.
- Accuracy: Observed improved accuracy and ease of use with Adafruit libraries compared to manual GPIO-based implementations.
- Performance Analysis: Compared the response time and error rates of both implementations under similar conditions. The library-based approach had fewer errors and faster data retrieval.
- Scalability: Tested with multiple sensors connected to the same system, demonstrating stable performance.
- Seamless Interoperability: Bridge communication gaps between heterogeneous IoT devices (e.g., MQTT, CoAP, Zigbee).
- Database Integration: Store sensor readings for long-term analysis and insights.
- Web Dashboard: Enable users to view real-time data through an online interface.
- Mobile Notifications: Push alerts to mobile devices when thresholds are exceeded.
- Blockchain Security: Enhance trust and data integrity with blockchain-based transactions.
- Extended Standards Support: Integrate support for emerging platforms like Matter and LoRaWAN.
- Cloud-Fog Collaboration: Efficiently divide tasks between fog nodes and cloud systems for large-scale analytics.
- Energy Efficiency: Develop low-power fog nodes powered by renewable energy sources.
- Enhanced Security: Process sensitive data locally to ensure privacy and secure encrypted transmission.