FEATURES OF PREDICTING FAILURES IN A SMART HOME BASED ON MACHINE LEARNING METHODS

Abstract

Modern smart homes based on the Internet of Things (IoT) offer users a wide range of automated conveniences, but at the same time face potential problems and system failures. This article examines the potential for using machine learning techniques to predict and manage these failures in order to ensure greater reliability and efficiency of the smart home. The article discusses current trends in the development of smart homes, as well as practical problems associated with system failures. The authors analyse various aspects of failures, from network incidents to device failures, taking into account the variety of sensors and detectors used in smart systems. The article discusses the main machine learning methods, such as neural networks, decision trees, and classification, and their application to predict failures in smart homes. The advantages and limitations of each method are analysed and their effectiveness in different scenarios is considered. The reference architecture of the IoT platform and the fault prediction platform are presented. The algorithm of the platform is described in detail. The fault prediction platform assumes the existence of an IoT platform. In particular, it assumes the existence of two data sources, one for operational and the other for historical and static data. The process of data processing is described. The authors of the article provide recommendations on the optimal approaches to predicting failures in smart homes and identify possible areas for further research in this area. It is highlighted that accurate failure prediction can significantly increase the reliability and usability of smart home systems, which is a key aspect for their popularity and market acceptance. Effective prediction of failures in a smart home can improve the safety, energy efficiency, and convenience of users' lives.