A Low-Cost Smart Home Automation to Enhance Decision-Making based on Fog Computing and Computational Intelligence

Filho, Geraldo P. Rocha; Mano, Leandro Yukio; Valejo, Alan; Villas, Leandro A.; Ueyama, Jó

doi:10.1109/tla.2018.8291472

Cited by 28 publications

(15 citation statements)

References 14 publications

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“…They also evaluated machine learning methods that address the problems derived from the management of IoT data by considering smart cities as the case of primary use. Filho et al [11] proposed a solution that combines computational intelligence and fog computing called STORm (Smart Solution for Decision Making in a Residential Environment). The STORm system, to improve the decision-making process, was able to detect and control the information generated by sensors installed in the residential scenario.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%

“…As shown in this section, works related to this project's context were identified in the literature. However, of these works, only those in [11,12] are focused on improving the automatic configuration schemes. None considers the analysis or discovery of the residents' behavior patterns to improve the automatic control of domotic devices.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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“…The first group of papers includes Filho, et al [63], Chiu and Ko [64], Chatterjee, et al [65], Sivanathan, et al [66], and Kim [67], in which their core discussion is around IoT and users since they have received high references. As shown previously, these papers have utilized IoT for decision purposes for daily activities and users' satisfaction (refer to Figure 7).…”

Section: Group 1: Iot and Usersmentioning

confidence: 99%

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

et al. 2020

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This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

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“…Com o advento de novas tecnologias, surge o problema do aumento do consumo energético, e o setor residencial demanda parcela significativa da energia elétrica produzida (Rocha Filho et al, 2018). Somado a isso, o envelhecimento da população exige residências mais adaptadasà nova realidade.…”

Section: Introductionunclassified

Sistema para Controle e Monitoramento de Ambientes Utilizando Internet das Coisas

Amorim

Oliveira

Machado-Coelho

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The increase in demand for convenience and security, together with the focus on energy efficiency, has made residential automation become more relevant. One way to expand the use this technology is by applying the Internet of Things (IoT) concept, that connects things and people through wireless networks. The objective of this work is to develop a system that monitors and controls residential environments by using the IoT concept. The developed devices are independent and used in a cloud system in order to perform their tasks, which can be done remotely. Services that allow integration with external systems are available through the rest API. Resumo: Com o aumento da demanda por comodidade, segurança e o foco em eficiência energética, a automação residencial vem ganhado espaço. Uma aposta para expandir o uso desta tecnologia e melhorar a questão energéticaé a aplicação do conceito de Internet das Coisas (IoT), que interliga coisas e pessoas através de redes sem fio. O objetivo deste trabalhoé desenvolver um sistema para controle e monitoramento de ambientes utilizando o conceito de IoT. Os dispositivos construídos são independentes e estão integrados a um sistema nas nuvens para realização do procedimento, que pode ser feito de modo remoto. São disponibilizados serviços que permitem a integração de sistemas externos através de um web server criado com API rest.

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A Low-Cost Smart Home Automation to Enhance Decision-Making based on Fog Computing and Computational Intelligence

Cited by 28 publications

References 14 publications

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

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

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

Sistema para Controle e Monitoramento de Ambientes Utilizando Internet das Coisas

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