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FreeRTOS Multi-Gas Detector Design Using STM32 and WiFi

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FreeRTOS STM32 IoT Embedded Systems Gas Detection

FreeRTOS Multi-Gas Detector Design Using STM32 and WiFi

Modern mining environments demand accurate, real-time detection of hazardous gases to ensure worker safety and operational reliability. This article presents a wireless portable multi-gas detector built on FreeRTOS, combining embedded processing, sensor fusion, and IoT connectivity.

The system detects methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S) with high accuracy while enabling remote monitoring via WiFi and cloud platforms.

🧭 Introduction
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Toxic gases in underground mining pose serious risks. Traditional detectors often suffer from:

  • Slow response times
  • Limited storage capacity
  • Poor wireless communication
  • Bulky and inefficient designs

To address these challenges, this design integrates:

  • STM32 microcontroller for processing
  • FreeRTOS for multitasking
  • WiFi (ESP8266) for cloud connectivity
  • Kalman filtering for noise reduction

🏗️ System Architecture
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The system is divided into three functional layers:

Layer Description
Data Acquisition Sensors collect gas concentration data
Data Processing STM32 processes and filters data
Monitoring Cloud platform visualizes and stores data

Data Flow
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$$ Sensors → STM32 (ADC + Processing) → WiFi Module → Cloud Platform $$

This architecture enables real-time monitoring and remote analytics.

🔧 Hardware Design
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The system uses a modular hardware approach.

Key Modules
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  • STM32 microcontroller
  • Gas sensors (MQ-4, MQ-7, MQ-136)
  • ESP8266 WiFi module
  • SD card storage
  • TFT LCD display
  • Alarm module (buzzer/LED)
  • RTC clock and input keys

Gas Detection Modules
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Methane (CH₄) – MQ-4 Sensor
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  • Detection range: 0.03%–1.00%
  • Outputs analog and digital signals
$$ C = ((ADC_val × V / n) - b) / a × c $$

Carbon Monoxide (CO) – MQ-7 Sensor
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  • Detection range: 0–500 ppm
  • Uses temperature cycling for sensing
$$ C = 98.322 × (Rs / R0)^(-1.458) $$

Hydrogen Sulfide (H₂S) – MQ-136 Sensor
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  • Detection range: 1–200 ppm
$$ RFL / RL = (VC - VFL) / R0 C = (0.9754 ± 574.889 × VFL) / (33 × 5 - 33 × VFL) $$

Storage Module
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  • SD card (SPI/SDIO interface)
  • Provides non-volatile, high-capacity logging
  • Ensures data persistence after power loss

Wireless Communication Module
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  • ESP8266-01 WiFi module
  • Uses MQTT protocol

Data Transmission Flow
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$$ Read SD → Format JSON → Send via MQTT → Cloud Storage $$

Human-Machine Interface
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  • 2.0-inch TFT LCD
  • Multiple control keys

Supports:

  • Data viewing
  • Parameter configuration
  • Network synchronization

🧠 Software Design
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FreeRTOS Integration
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FreeRTOS enables concurrent task execution with:

  • Task scheduling
  • Priority management
  • Efficient CPU utilization

Task Distribution
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Task Function
Data Acquisition Sensor sampling
Data Processing Filtering and calculation
Storage Save to SD card
Communication WiFi transmission
UI Task Display and input handling

Main Program Flow
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$$ Hardware Init → FreeRTOS Init → Task Creation → Scheduler Execution $$

📊 Kalman Filtering for Noise Reduction
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ADC readings often contain noise. The Kalman filter improves accuracy by:

  • Estimating true signal values
  • Reducing random noise
  • Preserving signal trends

Benefits
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  • Higher measurement precision
  • Stable readings in noisy environments

📡 Wireless Communication Performance
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Packet Loss Test Results
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Distance (m) Packets Sent Packets Received Loss Rate (%)
20–80 100 100 0
100 100 99 1
120 100 95 5
140 100 94 6
160 100 90 10
200 100 85 15

Insight: Reliable communication is maintained within 100 meters.

🧪 Experimental Validation
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Standard Gas Testing
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Gas Standard Value Measured Results
CO 1.00 ppm 1.00–1.02
CH₄ 150 ppm 149–152
H₂S 10 ppm 10.0–10.3
  • Average error < 2%
  • Higher accuracy than traditional detectors

🚀 Key Advantages
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  • High Accuracy

    • Kalman filtering reduces noise

  • Real-Time Monitoring

    • WiFi + MQTT cloud integration

  • Reliability

    • FreeRTOS multitasking reduces data loss

  • Portability

    • Compact embedded design

  • Scalability

    • Modular hardware and software

📌 Best Practices
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  • Calibrate sensors regularly
  • Use watchdog timers for system stability
  • Optimize FreeRTOS task priorities
  • Secure MQTT communication (TLS if possible)
  • Validate sensor data with reference instruments

✅ Conclusion
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The FreeRTOS-based wireless multi-gas detector provides a robust, accurate, and scalable solution for hazardous gas monitoring in mining environments.

By integrating:

  • STM32 embedded processing
  • FreeRTOS multitasking
  • Kalman filtering algorithms
  • WiFi cloud connectivity

the system achieves:

  • Reliable real-time detection
  • Low data loss
  • High measurement accuracy

This design represents a significant advancement in industrial safety monitoring, with strong potential for broader IoT and environmental applications.

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