HydroNeuro: A Data-Efficient IoT Sensing and Edge-AI Framework for Real-Time Hydraulic Anomaly Detection

Area of Science:

• Agricultural Engineering
• Embedded Systems
• Data Science

Background:

• Efficient water management in agriculture relies on reliable hydraulic network monitoring.
• Intelligent, low-latency anomaly detection is crucial for sustainable water distribution systems.

Purpose of the Study:

• To develop HydroNeuro, a domain-aware embedded framework for real-time leak and obstruction detection in hydraulic networks.
• To integrate hydraulic principles with neural inference for physically consistent anomaly detection.

Main Methods:

• Leveraged Bernoulli's equation and Darcy-Weisbach formulation to guide experimental design and data interpretation.
• Employed fractional factorial design (FFD) for efficient dataset acquisition, optimizing sensor configurations and valve activations.
• Deployed a lightweight neural network on an ESP32 microcontroller using TensorFlow Lite for Microcontrollers for edge inference.

Main Results:

• Achieved anomaly detection accuracy exceeding 96% on a laboratory-scale hydraulic testbed.
• Demonstrated strong robustness against sensor noise and hydraulic perturbations.
• Reduced prediction error (RMSE) from 0.58 (baseline) to 0.12 with the proposed neural model.

Conclusions:

• HydroNeuro provides a scalable and deployable solution for autonomous hydraulic monitoring.
• The framework enables energy-efficient, low-latency edge inference for precision irrigation and smart water distribution.
• Coupling physical principles with embedded neural inference enhances the reliability of hydraulic network monitoring.