STM32-Based IoT Framework for Real-Time Environmental Monitoring and Wireless Node Synchronization

TL;DR

This paper presents a real-time environmental monitoring system using STM32 microcontrollers with Bluetooth communication, demonstrating advantages over Arduino in processing power and energy efficiency, and discusses future enhancements like Wi-Fi integration.

Contribution

The work designs and implements a dual-node IoT environmental monitoring system with real-time data display and alerts, showcasing STM32's benefits over Arduino for such applications.

Findings

STM32-based system provides reliable real-time environmental data.

The system consumes less power and offers better peripherals than Arduino.

Bluetooth communication effectively transmits sensor data between nodes.

Abstract

The fast pace of technological growth has created a heightened need for intelligent, autonomous monitoring systems in a variety of fields, especially in environmental applications. This project shows the design process and implementation of a proper dual node (master-slave) IoT-based monitoring system using STM32F103C8T6 microcontrollers. The structure of the wireless monitoring system studies the environmental conditions in real-time and can measure parameters like temperature, humidity, soil moisture, raindrop detection and obstacle distance. The relay of information occurs between the primary master node (designated as the Green House) to the slave node (the Red House) employing the HC-05 Bluetooth module for information transmission. Each node displays the sensor data on OLED screens and a visual or auditory alert is triggered based on predetermined thresholds. A comparative analysis of STM32 (ARM Cortex-M3) and Arduino (AVR) is presented to justify the STM32 used in this work for greater processing power, less energy use, and better peripherals. Practical challenges in this project arise from power distribution and Bluetooth configuration limits. Future work will explore the transition of a Wi-Fi communication protocol and develop a mobile monitoring robot to enhance scalability of the system. Finally, this research shows that ARM based embedded systems can provide real-time environmental monitoring systems that are reliable and consume low power.