System Design: IoT-Based Lake Monitoring System
Overview
The IoT-Based Distributed Lake Monitoring System is designed to collect, process, and analyze water quality parameters using IoT sensors, cloud computing, and real-time analytics. This section explains the high-level architecture, hardware components, software stack, and data flow in the system.
1. High-Level Architecture
The system consists of three major layers:
-
Edge Layer (IoT Sensors & Devices)
- Collects real-time water quality data.
- Uses ESP32 and Raspberry Pi for data acquisition.
- Transmits data via MQTT or REST APIs.
-
Cloud & Processing Layer
- Uses FastAPI (Python) / Express (Node.js) for backend processing.
- Stores structured data in PostgreSQL and time-series data in MongoDB.
- Performs anomaly detection and data analytics.
-
Application & Visualization Layer
- Provides a React-based dashboard for real-time monitoring.
- Implements Docusaurus for system documentation.
- Uses Leaflet.js / Google Maps API for geospatial visualization.
2. Hardware Components
IoT Devices
- ESP32 – Low-power microcontroller for lightweight sensor nodes.
- Raspberry Pi – Used for edge computing and AI-based anomaly detection.
Water Quality Sensors
| Sensor | Parameter Measured |
|---|---|
| pH Sensor | Water acidity level |
| Turbidity Sensor | Suspended particles in water |
| Temperature Sensor | Water temperature |
| Dissolved Oxygen Sensor | Oxygen levels in water |
| Conductivity Sensor | Salinity and dissolved minerals |
3. Software Stack
| Component | Technology Used |
|---|---|
| Frontend | React.js + Recharts (Data Visualization) |
| Backend | FastAPI (Python) / Express.js (Node.js) |
| Database | PostgreSQL (Structured Data), MongoDB (Sensor Data) |
| Messaging Protocol | MQTT (IoT Data Transfer) |
| Cloud Deployment | Docker + AWS/GCP |
4. Data Flow & Communication
- Sensor Nodes (ESP32/Raspberry Pi) collect water quality data.
- Data is transmitted via MQTT/REST API to the backend.
- Backend processes and stores data in PostgreSQL / MongoDB.
- Anomaly detection module triggers alerts if abnormal values are detected.
- Frontend Dashboard updates in real-time via WebSockets.
- Alerts & Notifications are sent via Email/SMS if critical thresholds are breached.
5. System Scalability & Security
Scalability Considerations
- Supports multiple sensor nodes for distributed monitoring.
- Uses Kubernetes & Docker for scalable backend deployment.
- Implements load balancing (Nginx, AWS ALB) for handling high data influx.
Security Measures
- TLS Encryption for secure data transmission.
- OAuth2 / JWT Authentication for API access.
- Role-Based Access Control (RBAC) for user permissions.
Conclusion
The IoT-Based Lake Monitoring System integrates IoT, cloud computing, and real-time analytics to provide an efficient and scalable solution for water quality monitoring. This design ensures high accuracy, automation, and ease of access for stakeholders.