NammaLakes, an IoT-powered distributed system designed to monitor the health and status of urban lakes. As urbanization grows, many freshwater bodies suffer from pollution, mismanagement, and neglect. NammaLakes takes a step toward smarter, data-driven lake conservation and management.

Explore the live system

At its core, this system leverages a network of IoT devices and intelligent software to monitor lake parameters in real-time — from water quality to sensor-driven alerts. This allows stakeholders and authorities to take timely action, backed by accurate data and reliable insights.

The system provides:

• Real-time Monitoring: Continuous collection of water quality parameters
• Intelligent Analytics: ML-powered anomaly detection (Isolation Forest algorithm)
• Interactive Dashboard: User-friendly visualization of complex environmental data
• Automated Alerts: Instant notifications when parameters exceed safe thresholds
• Scalable Architecture: Built to monitor multiple water bodies simultaneously

Traditional approaches to lake monitoring have significant limitations:

• Delayed Response: Manual sampling causes critical delays in detecting contamination events
• Limited Data: Infrequent collection makes it impossible to identify subtle trends
• Low Visibility: Lack of centralized visualization prevents effective decision-making
• No Early Warning: Absence of automated alerts means pollution events often go unnoticed

The NammaLakes system consists of three primary layers:

• Hardware: ESP32 microcontrollers and Raspberry Pi devices
• Sensors: pH, turbidity, temperature, dissolved oxygen, conductivity
• Edge Processing: Initial data filtering and preprocessing
• Communication: MQTT protocol for efficient, lightweight data transmission

• Backend APIs: FastAPI (Python) for robust, high-performance data handling
• Databases:
• PostgreSQL for structured data (sensor metadata, users, alerts)
• SQLite for development/testing
• Message Broker: RabbitMQ for reliable communication between components
• Analytics: Machine learning models for identifying abnormal readings

• Frontend: React-based responsive dashboard
• Data Visualization: Interactive charts for parameter tracking
• Geospatial Interface: Map-based sensor location overview
• Documentation: Comprehensive guides with Docusaurus

This isn’t just a data dashboard. The system also uses trained machine learning models, such as the Isolation Forest, to detect anomalies in lake behavior — spotting irregular patterns in temperature, turbidity, or sensor activity.

Microservices are provided to allow alert messages to be sent via E-Mails, Whatsapp and Telegram to users

At a time when sustainability matters more than ever, NammaLakes aims to bridge the gap between smart city tech and environmental conservation. By creating a scalable, open system for lake monitoring, we hope to empower local communities, researchers, and administrators to keep our lakes healthy and thriving.

This is more than a tech stack — it’s a collective step toward ecological responsibility, one data point at a time.

Whether you’re a developer, environmentalist, or curious citizen, we welcome contributions, feedback, and ideas. Dive into the code, check out the docs, or spin up your own deployment. There’s a lot to build — and a lot more to protect.

Prerequisites

• Python 3.9+
• Node.js 16+
• Docker and Docker Compose
• Poetry (Python dependency management)

To get started locally:

git clone https://github.com/NammaLakes/nammalakes.git
cd nammalakes

Install Python packages:

cd backend
poetry install

Spin up containers (RabbitMQ, DB, API):

sudo docker compose up --build

For the dashboard:

cd dashboard
npm install
npm run dev

And for documentation:

cd docs
npm install
npm run start

The project is organized into the following core folders:

NammaLakes/
├── backend/               # FastAPI backend service
│   ├── data/              # Data storage and logs
│   ├── lakewatch/         # Core application code
│   │   ├── db/            # Database models and DAOs
│   │   ├── services/      # External service integrations
│   │   └── web/           # API endpoints and server config
│   ├── tests/             # Backend test suite
│   └── deploy/            # Deployment configurations
├── dashboard/             # React frontend application
│   ├── src/               # Source code
│   │   ├── components/    # Reusable UI components
│   │   ├── hooks/         # Custom React hooks
│   │   ├── lib/           # Utilities
│   │   └── pages/         # Page components
├── docs/                  # Documentation site
│   └── docs/              # Markdown documentation files
├── node/                  # IoT sensor node service
│   ├── node/              # Node application code
│   │   ├── read/          # GPIO sensor reading functionality
│   │   ├── transmit/      # RabbitMQ transmission logic
│   │   └── logger/        # Logging configuration
│   └── tests/             # Node test suite
└── .github/               # GitHub configuration files
    └── profile/           # Organization profile

We welcome contributions from developers, researchers, and curious minds alike.

To contribute:

1. Fork this repository.
2. Create a new branch:

   git checkout -b your-feature-branch

3. Make your changes and commit:

   git commit -m "Add your message here"

4. Push to your fork and open a Pull Request:

   git push origin your-feature-branch

We review PRs regularly and are happy to collaborate.

For questions, collaboration, or demo access, reach out at:
[email protected]
Or open an issue on this repository.