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

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  • Josimar Reyes-Campos

    (Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9,852, Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Giner Alor-Hernández

    (Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9,852, Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Isaac Machorro-Cano

    (Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9,852, Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • José Oscar Olmedo-Aguirre

    (Department of Electrical Engineering, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2,508, Col. San Pedro Zacatenco, Delegación Gustavo A. Madero, Mexico City 07360, Mexico)

  • José Luis Sánchez-Cervantes

    (CONACYT-Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9,852, Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

  • Lisbeth Rodríguez-Mazahua

    (Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9,852, Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico)

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.

Suggested Citation

  • Josimar Reyes-Campos & Giner Alor-Hernández & Isaac Machorro-Cano & José Oscar Olmedo-Aguirre & José Luis Sánchez-Cervantes & Lisbeth Rodríguez-Mazahua, 2021. "Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm," Mathematics, MDPI, vol. 9(3), pages 1-25, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:219-:d:485180
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    References listed on IDEAS

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    1. Wadim Strielkowski & Olga Kovaleva & Tatiana Efimtseva, 2022. "Impacts of Digital Technologies for the Provision of Energy Market Services on the Safety of Residents and Consumers," Sustainability, MDPI, vol. 14(5), pages 1-18, March.

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