EnvMon is an IoT platform that combines a Raspberry Pi-based acquisition device with a web Ground Station for collecting, organizing, visualizing, and analyzing environmental data.
The system supports mobile and static monitoring missions through four operating profiles: Drone, Car, Bicycle, and Static Station.
Watch the full project demonstration
EnvMon covers the complete monitoring workflow, from acquiring measurements in the field to exploring them in a web application. Environmental readings, GPS coordinates, images, and mission metadata are recorded by the acquisition device and organized into separate monitoring missions.
After a mission is completed, its data can be imported into the Ground Station, stored in a local database, displayed on interactive maps, and analyzed through heatmaps, charts, and AI-assisted reports.
- Automatic discovery and management of EnvMon devices in the local network
- Environmental telemetry acquisition using a BME680 sensor
- GPS positioning and optional image capture
- Drone, Car, Bicycle, and Static Station operating profiles
- Mission configuration, execution, import, and local storage
- Live mission tracking and telemetry visualization
- Interactive routes, measurement points, and environmental heatmaps
- Single-mission and multi-mission graphical analysis
- AI-assisted mission reports
- Simulation scenarios for testing without a physical deployment
The Raspberry Pi device collects environmental measurements, GPS data, and optional images. A local Flask REST API allows the Ground Station to check the device status, control missions, and import completed datasets.
The Ground Station backend manages synchronization, mission processing, and database access, while the React frontend provides the user interface for mission control, mapping, and analysis.
| Profile | Monitoring scenario | GPS usage | Image capture |
|---|---|---|---|
| Drone | Mobile aerial monitoring | Continuous | Optional |
| Car | Mobile road monitoring | Continuous | Usually disabled |
| Bicycle | Low-speed mobile monitoring | Continuous | Usually disabled |
| Static Station | Monitoring a fixed location | Initial position | Usually disabled |
Mission Control displays the active device, its current position, the recorded route, and live environmental telemetry during a monitoring mission.
Imported missions can be explored through GPS routes, measurement points, environmental heatmaps, and images captured during data collection. Heatmaps can represent local variations in temperature, humidity, atmospheric pressure, and gas resistance.
The Analytics module provides interactive charts for inspecting measurements over time and comparing data collected during different missions.
EnvMon can generate structured mission summaries that highlight relevant environmental variations, GPS data quality, and possible observations. These reports complement the raw measurements and graphical analysis and are not intended to provide certified environmental conclusions.
Hardware prototype of the EnvMon acquisition device
The acquisition device is built around a Raspberry Pi Zero 2 W and integrates:
- BME680 environmental sensor for temperature, humidity, atmospheric pressure, and gas resistance
- NEO-6M GPS module for associating measurements with geographic positions
- Raspberry Pi Camera for optional image capture
- Local storage for telemetry, images, and mission metadata
The current hardware is an experimental prototype intended for development, testing, and demonstration.
| Component | Technologies |
|---|---|
| Acquisition Device | Raspberry Pi Zero 2 W, Python, Flask, BME680, NEO-6M GPS, Raspberry Pi Camera |
| Ground Station Backend | Python, Flask, Flask-CORS, SQLAlchemy, SQLite, Google Gemini API |
| Web Interface | React, Vite, MapTiler SDK, Recharts, Tailwind CSS, DaisyUI, Axios |
| Communication and Storage | REST API, HTTP, I2C, UART, CSI, CSV, JSON, SQLite |
EnvMon is divided into two main repositories:
- envmon-app — Ground Station frontend, backend, database, maps, analytics, and AI-assisted reports
- envmon-device — Raspberry Pi sensor acquisition, local mission recording, REST API, and simulation tools
View installation instructions
- Python 3
- Node.js and npm
- A MapTiler API key
- An optional Google Gemini API key for AI-assisted reports
- An EnvMon acquisition device or a compatible simulation instance
Navigate to the backend directory:
cd server
python -m venv .venv
mkdir dataActivate the virtual environment.
Windows PowerShell:
.venv\Scripts\Activate.ps1Linux or macOS:
source .venv/bin/activateInstall the dependencies:
pip install -r requirements.txtCreate a file named server/.env:
DEFAULT_DEVICE_URL=http://DEVICE_IP:8000
DATABASE_URL=sqlite:///data/envmon.sqlite
GEMINI_API_KEY=your_gemini_api_key
GEMINI_MODEL=gemini-2.5-flash
AI_ANALYSIS_ENABLED=trueSet AI_ANALYSIS_ENABLED=false if AI-assisted reports are not required.
Start the backend:
python run.pyThe backend will be available at:
http://127.0.0.1:5000
Open a second terminal and navigate to the frontend directory:
cd client
npm installCreate a file named client/.env:
VITE_API_BASE_URL=http://127.0.0.1:5000/api
VITE_MAPTILER_KEY=your_maptiler_api_keyStart the development server:
npm run devDevice installation and simulation details are available in the envmon-device repository.
- Communication is currently designed primarily for a local network
- The prototype does not include a weather-resistant enclosure or autonomous field power system
- BME680 gas resistance is a relative indicator rather than a certified measurement of individual pollutants
- GPS availability and accuracy depend on signal and operating conditions
- SQLite is appropriate for the prototype but not for a large multi-device deployment
- AI-generated observations require interpretation alongside raw data and charts
- 4G, LoRaWAN, or cloud-based communication
- Simultaneous monitoring of multiple acquisition devices
- PostgreSQL migration for larger datasets
- Integration of additional sensors such as PM2.5, PM10, CO₂, soil moisture, light, and noise sensors
- Automated anomaly detection and cross-mission analysis
- Automatic PDF mission reports
EnvMon was developed as my bachelor's degree project in Computer Engineering. It brings together embedded systems, sensor integration, REST APIs, databases, web development, geospatial visualization, and AI-assisted data analysis in a complete functional prototype.