Smart Buildings: Water Leakage Detection Using TinyML
Keywords:
EfficientNet, TinyML, accelerometer, acoustic data, scalogramAbstract
Rising global water demand and urban water shortages underscore the need for effective water management. This study explores leveraging TinyML in smart buildings to enhance water management by integrating sensors and embedded Machine Learning models. TinyML enables real-time data collection, analysis, and precise decision-making for optimal water utilization, reducing reliance on centralized entities. The proposed solution adapts to real-world scenarios, detecting leaks with minimal human intervention. Following a machine learning lifecycle, the study utilizes an acoustic dataset, applying transfer learning to five Convolutional Neural Network (CNN) variants. The EfficientNet model achieves notable results, with a maximum testing accuracy, recall, precision, and F1 score of 97.45%, 98.57%, 96.70%, and 97.63%, respectively. For deployment on the Arduino Nano 33 BLE edge device, the EfficientNet model undergoes quantization, ensuring low inference time (1932 ms), peak RAM usage (255.3 KB), and flash usage (48.7 KB).
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