Smart Homes Powered by Machine Learning: A Review

Abstract

The Internet of Things (IoT) has attracted significant attention from researchers and companies as it covers a wide range of applications, including industrial control, healthcare, transportation, smart cities and homes, and agriculture. As the pioneering IoT application, smart homes aim to increase the quality of residents' lives by making home appliances automated by remotely controlling or automatically operating them far from home. However, not only remotely controlling should take into consideration when building a smart home system, but also making the home environment as smart as possible by incorporating the value and power of Machine Learning (ML) techniques to it. Adding such value is crucial to make the system adaptive to users' activities toward predicting user behavior to reduce power consumption further, enhance security levels, and improve usability experiences. This study reviews popular machine learning techniques in smart home applications with their benefits and limitations. The paper also provides a comprehensive comparison among the available systems in the literature which integrate smart homes with the intelligence of the ML algorithms. Finally, after discussing the results, the open problems and suggested directions are presented, and the challenges and perspectives of future development of smart homes systems are presented and discussed.