🖥️ Integrated Data Transmission System: STM32 Nucleo with i2c and UART drivers and Raspberry Pi for MQTT Communication with Accelerometer (adxl345) Integration

Overview

This project integrates a Raspberry Pi with an STM32 Nucleo board to collect and transmit data. The Raspberry Pi runs a Python script to gather data from the STM32 via UART and monitor internal metrics. Accelerometer data is transmitted using I2C and sent to the ThingsBoard platform via MQTT for real-time analysis. A JSON configuration file creates a dynamic dashboard on ThingsBoard, while a Plane Data Environment (PDE) file visualizes the plane’s tilt based on accelerometer data. This project showcases effective use of UART, I2C, and MQTT for comprehensive data monitoring and visualization.

Project Structure

• main_py_sendUARTdata.py: 🐍 Python script responsible for reading data from the UART interface and sending it to the MQTT broker. The script also gathers system metrics such as CPU usage, memory usage, and disk space, publishing them to the MQTT broker for real-time monitoring.

• pi4_dashboard_mqtt.json: 📊 JSON configuration file for setting up the ThingsBoard dashboard. This file defines the widgets and data sources used to display real-time data on the dashboard, including CPU usage, RAM usage, accelerometer data, and more.

• acc_ADXL.pde: ✈️ Processing script that visualizes the orientation of a plane in real-time based on accelerometer data. This script communicates with the Raspberry Pi, receiving data via MQTT and updating the plane's orientation accordingly.

• adxl345-driver-stm32: 📂 Contains the STM32 driver code for the ADXL345 accelerometer. This code is responsible for interfacing with the accelerometer and transmitting data via I2C. The driver code is included in the repository to facilitate integration and communication with the STM32 board.

Features

1. Real-Time Monitoring: ⏱️ The system monitors various system metrics (CPU, RAM, disk usage) and accelerometer data, publishing it to the MQTT broker for real-time visualization on the ThingsBoard dashboard.

2. Customizable Dashboard: 🛠️ The pi4_dashboard_mqtt.json file provides a pre-configured dashboard that includes widgets for visualizing system metrics and sensor data. Users can easily customize this dashboard according to their requirements; either by editing the json file, or graphically on theThingsBoard plateform.

3. Remote Procedure Call (RPC): 📡 The project includes an RPC mechanism allowing remote triggering of specific actions on the Raspberry Pi, such as forcing the retrieval of telemetry data.

4. 3D Visualization: 🎨 The acc_ADXL.pde script offers a 3D visualization of the plane’s orientation based on real-time accelerometer data, providing a graphical representation of the sensor's output.

Getting Started

Prerequisites

• Raspberry Pi 4 with Python installed.
• UART-connected device for accelerometer data (or similar).
• Access to a ThingsBoard server with MQTT broker capabilities.
• Processing IDE for running the acc_ADXL.pde script.

  

Setup Instructions

1. Install Required Libraries:

   pip install paho-mqtt

2. Configure UART Interface: Ensure your UART-connected device is properly configured to communicate with the Raspberry Pi.

3. Run the Python Script:

   python3 main_py_sendUARTdata.py

   This script will begin reading data from the UART interface and system metrics, publishing them to the MQTT broker.

4. Deploy the Dashboard:

   • Import the pi4_dashboard_mqtt.json file into your ThingsBoard instance.
   • Assign the correct data sources and configure any additional widgets as needed.

5. Run the Plane Visualizer:

   • Open the acc_ADXL.pde file in the Processing IDE.
   • Run the sketch to visualize the accelerometer data in real-time.

Future Enhancements

• Expand Sensor Integration: 📈 Incorporate additional sensors to monitor environmental factors like temperature, humidity, etc.
• Extended RPC Capabilities: ⚙️ Enhance the RPC functionality to include more complex remote operations, such as system diagnostics or updates.