Evaluation of Machine Learning Models in a Smart Water Metering System

The integration of AI with IoT heralds the era of AIoT (Artificial Intelligence of Things). It represents a transformative approach in technology and opens up a new opportunity for deploying machine learning models in embed-ded devices that face resource constraints and operate on the edge of networks. Central to this study is the implementation of computational vision techniques for digit recognition, evaluating various machine learning models, particularly in the context of smart metering. The selected models were converted from GPU-equipped workstations to ESP32-S3 microcontroller-based low-end devices. Through a series of experiments using ESP32-S3 development kits, the MNIST database, and TensorFlow Lite, we explore the effectiveness of these models in smart metering applications, focusing on accuracy, inference times, and the challenges in model conversion. The findings demonstrate the feasibility of executing machine learning inferences on low-end devices with high accuracy in smart meter contexts. However, challenges such as model size limitations, processing speed, conversion difficulties, and potential accuracy loss were noted. Not all models were viable for conversion to TensorFlow Lite. Simpler models like LeNet5 emerged as effective solutions for smart metering applications, balancing size, accuracy, and latency. This work offers practical insights for researchers and engineers looking to implement machine learning in AIoT and smart metering environments, highlighting the trade-offs and considerations for effective deployment.