Vehicle Position and Operational Data Logger

This repository contains a complete solution for recording, storing, and visualizing the position and operational data of vehicles such as cars, trams, or trains. The system consists of an embedded ESP32-based device, a backend server stack, and a web interface.

🚀 Overview

The system includes:

• ESP32 device
  • ESP32-based firmware that gathers data from:
    • UM7 orientation sensor (accelerometer, gyroscope, GPS)
    • Vehicle CAN bus via OBD-II
  • Local storage of data (offline mode) and real-time streaming (online mode)
• Server backend using InfluxDB and FastAPI
  • Visualization dashboards using Grafana

🧱 Project Structure

.
├── README.md                  # Project overview and usage instructions
├── dp.drawio.png              # System architecture diagram
├── vpp_project.vpp            # UML diagrams and system design (Visual Paradigm)

├── esp32fw/                   # Embedded firmware for ESP32 (PlatformIO)
│   ├── src/                   # Main source code (sensor handling, CAN, logging)
│   ├── lib/                   # Firmware libraries (UM7, CAN, config parser)
│   ├── test/                  # Unit tests and integration tests
│   ├── docs/                  # Auto-generated documentation (Doxygen)
│   ├── config.txt             # Runtime configuration file (e.g. sampling rate, server)
│   ├── platformio.ini         # Build system configuration (PlatformIO)
│   └── Doxyfile               # Documentation build config

├── iotplatform/              # Backend services (data collection, visualization)
│   ├── fastapi_app/           # REST API for data ingestion and processing
│   ├── codec8e-parser-tcp-server/  # Raw TCP server for decoding telemetry
│   ├── configurations/        # App and Grafana configuration files
│   ├── influx_data/           # Persistent storage for InfluxDB
│   ├── grafana_data/          # Grafana dashboards and user settings
│   ├── fastapi_data/          # App-level runtime data (e.g., logs, uploads)
│   ├── docker-compose.yml     # Multi-container setup for the full backend stack
│   └── venv/                  # Python virtual environment (optional, for local dev)

🔧 Getting Started

Firmware (ESP32)

Built using PlatformIO in Visual Studio Code.

1. Connect your ESP32 board via USB.

2. Navigate to esp32fw folder.

3. Build and upload the firmware:

   pio run --target upload

4. Optional: Monitor serial output

   pio device monitor

Configuration

Edit esp32fw/config.txt to customize:

• Server URL
• Logging interval
• Enabled sensors (GPS, CAN, etc.)

Backend & Web App

Docker is required.

1. Navigate to the iotplatform folder:

   cd iotplatform

2. Launch the backend stack:

   docker-compose up --build

3. Access services:

   • FastAPI: http://localhost:8000
   • Grafana: http://localhost:3000

📈 Data Flow

1. ESP32 gathers data from sensors.
2. Data is stored locally and/or streamed to the backend.
3. FastAPI receives and stores data in InfluxDB.
4. Grafana visualizes data in dashboards.

🛠 Technologies Used

• ESP32
• UM7 Sensor
• CAN Bus / OBD-II
• FastAPI
• InfluxDB
• Grafana
• Docker & Docker Compose
• PlatformIO

📚 Documentation

• Sensor protocol specs: esp32fw/docs/
• Configuration sample: esp32fw/config.txt
• UML & architecture: vpp_project.vpp, dp.drawio.png

🔮 Possible Future Improvements

• Integration of GSM/LTE Module: Adding mobile network support would eliminate the dependency on external Wi-Fi and enable remote data transmission from virtually anywhere.

• Real-Time Clock (RTC) Module: Including a battery-backed RTC would ensure accurate timestamps, especially when GPS is unavailable, improving reliability in offline mode.

• Advanced Power Management: Implementing sleep modes (e.g., ESP32 Deep Sleep) and ignition status detection would help reduce energy consumption when the vehicle is not active.

• On-Device Data Processing: Running data filtering and event detection (e.g., hard braking or acceleration) directly on the ESP32 would reduce data transfer volume and improve responsiveness.

• Direct OBD-II PID Decoding: Adding native support for decoding standard and proprietary OBD-II PIDs would provide immediate access to readable values like speed, RPM, and temperature.

• Bluetooth Communication: Bluetooth could be used for convenient local configuration and log retrieval via a mobile app or PC without removing the SD card.

These improvements aim to increase the system's autonomy, efficiency, and usability, bringing it closer to commercial-grade telematics solutions​:contentReference[oaicite:0]{index=0}.

👤 Author

Created by Vojtěch Giesl as part of his 2025 Master's Thesis at VŠB – Technical University of Ostrava.

📄 License

MIT License – see LICENSE file.

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⚠️ This README was generated with the assistance of AI (ChatGPT) based on the contents of the accompanying master's thesis.