Learning From Smart Home Data: Methods and Challenges of Data Acquisition and Analysis in Smart Home Solutions

Abstract

As the adoption rate of commercial smart home solutions increases, it drives the development of novel system features, needed to support advanced user scenarios. Being able to remotely control the household is not enough already, and the efforts are made to provide context-aware and intelligent homes, which detect user activities, learn about user habits, adapt to particular users, create intelligent alarms, seamlessly integrate with remote services, etc. On the other hand, significant efforts have already been made by the research community to identify data mining scenarios applicable to smart home solutions, and to propose and improve the algorithms suitable for this purpose. This article summarizes state-of-the-art research in the field of machine learning for smart home solutions.