An Intelligent Microprocessor Integrating TinyML in Smart Hotels for Rapid Accident Prevention

Abstract

In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be installed in large scale IoT deployments ranging from smart homes to smart cities and smart buildings. The aim of these devices shall be not only data monitoring but at the same time energy saving and overall building management. In the context of this paper, an all-in-one microprocessor is presented, namely ZAC888DP, which can sense data from multivariate sources and perform data analytics on top of the collected data. Moreover, machine learning (ML) models are deployed in the embedded memory of the device and specifically TinyML methods using a tflite file. The aim of the developed ML model is to collect data from four heterogeneous sources (water sensor, light sensor, humidity and temperature) in order to identify and forecast possible lavatory accidents. The experimental results of this work are encouraging as the model managed to achieve 100 percentage accuracy after 256 iterations. Future directions include the integration of the device with a neural network that will be trained on top of the pre-trained model in order to increase the overall precision even further.