Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

Abstract

In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.