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MQTT-Based Architecture for Real-Time Data Collection and Anomaly Detection in Smart Livestock Housing.

Kyeong Il Ko1, Meong Hun Lee1

  • 1Department of Smart Agriculture Program, Sunchon National University, Suncheon-si 31031, Republic of Korea.

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A new MQTT communication framework provides stable, low-latency environmental monitoring for smart livestock housing. This system integrates sensor data acquisition and anomaly detection with high accuracy and reliability.

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Area of Science:

  • Agricultural Engineering
  • Internet of Things (IoT)
  • Environmental Monitoring

Background:

  • Smart livestock housing requires reliable, low-latency environmental data acquisition for effective management and anomaly detection.
  • Existing communication frameworks may struggle with stability, latency, and scalability in real-time agricultural applications.

Purpose of the Study:

  • To design and validate a Message Queuing Telemetry Transport (MQTT)-based communication framework for smart livestock housing.
  • To achieve stable, low-latency (soft real-time) data acquisition and enable anomaly detection in livestock environments.
  • To evaluate the performance of different Quality of Service (QoS) levels and system scalability.

Main Methods:

  • Developed an MQTT-based framework comprising environmental sensor nodes, a Mosquitto MQTT broker, and a Gated Recurrent Unit (GRU)-based anomaly detection model.
  • Utilized WiFi for data transmission and compared various MQTT Quality of Service (QoS) levels for optimal performance.
  • Conducted sensor node expansion experiments and integrated system testing to assess latency, data rates, and uptime.

Main Results:

  • MQTT QoS 1 configuration achieved the most stable performance with ~150 ms average latency, ≥99% data collection rate, and ≤0.5% packet loss.
  • The system maintained responsiveness (≤200 ms) for up to 10-15 sensor nodes, with the GRU model achieving 97.5% accuracy and 18.5 ms/sample inference latency.
  • The integrated system demonstrated an average end-to-end latency of 185.4 ms, 98.9% data retention, and 99.6% system uptime.

Conclusions:

  • The proposed MQTT framework with QoS 1 and GRU-based anomaly detection ensures stable, low-latency soft real-time monitoring in livestock housing.
  • The system effectively acquires environmental data and detects anomalies, offering a scalable solution for smart agriculture applications.
  • This approach provides a robust foundation for enhancing animal welfare and farm management through intelligent environmental control.