Implementation: IoT-Based Lake Monitoring System
Overview
The implementation of the IoT-Based Lake Monitoring System involves the setup of hardware components, software development, data integration, and deployment. This section outlines the step-by-step process of setting up the system.
1. Hardware Setup
The IoT sensor nodes are deployed at different locations around the lake to collect real-time water quality data.
IoT Devices & Sensors
| Component | Description |
|---|---|
| ESP32 | Low-power microcontroller for sensor data collection |
| Raspberry Pi | Acts as a gateway for processing and transmitting data |
| pH Sensor | Measures water acidity |
| Temperature Sensor | Monitors water temperature |
Connection & Communication
- Sensors are interfaced with ESP32 / Raspberry Pi via GPIO/PWM/I2C.
- Data is transmitted using MQTT (Mosquitto) / HTTP (REST API).
2. Backend Development
The backend is responsible for data processing, storage, and real-time analytics.
Technologies Used
| Component | Technology |
|---|---|
| Backend Framework | FastAPI (Python) |
| Database | PostgreSQL |
| Message Broker | MQTT (IoT Data Communication) |
| Cloud Storage | AWS |
API Endpoints
| Method | Endpoint | Description |
|---|---|---|
POST /api/v1/sensors/readings | Stores data in the database | |
GET /api/v1/sensors/readings/latest | Retrieves data from the nodes | |
GET /api/v1/alerts | Retrieves any alerts from the particular nodes | |
GET /api/v1/sensors | Retrieve all sensor nodes | |
GET /api/v1/dashboard/stats | Fetch dashboard statistics |
Data Ingestion Process
- IoT devices send sensor data via MQTT/HTTP.
- Backend validates & processes data.
- Data is stored in PostgreSQL.
- Anomaly detection triggers alerts for abnormal values.
- Frontend dashboard updates in real time.
3. Frontend Development
The frontend provides an interactive dashboard for visualization.
Technologies Used
| Feature | Technology |
|---|---|
| Framework | React (Vite) |
| Visualization | Recharts, Leaflet.js (Maps) |
| Notifications | WebSockets, Push Notifications |
Dashboard Features
- Real-time Sensor Data Graphs
- Historical Data Trends
- Alerts & Notifications for Anomalies
- Map Integration for Sensor Locations
4. Deployment
The system is deployed on a cloud infrastructure with Docker.
Steps for Deployment
-
Containerization:
- Create Docker containers for backend and database.
- Use Docker Compose for local testing.
-
Cloud Deployment:
- Deploy using AWS.
-
Database Setup:
- Deploy PostgreSQL on AWS.
Performance Optimization
- Optimize database queries using indexing.
- Use load balancing for high availability.