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Smart Home Control and Management Based on Big Data Analysis

In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware,...

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Detalles Bibliográficos
Autores principales: Chi, Hao, Chi, Yuyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853757/
https://www.ncbi.nlm.nih.gov/pubmed/35186060
http://dx.doi.org/10.1155/2022/3784756
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author Chi, Hao
Chi, Yuyan
author_facet Chi, Hao
Chi, Yuyan
author_sort Chi, Hao
collection PubMed
description In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value.
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spelling pubmed-88537572022-02-18 Smart Home Control and Management Based on Big Data Analysis Chi, Hao Chi, Yuyan Comput Intell Neurosci Research Article In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value. Hindawi 2022-02-10 /pmc/articles/PMC8853757/ /pubmed/35186060 http://dx.doi.org/10.1155/2022/3784756 Text en Copyright © 2022 Hao Chi and Yuyan Chi. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chi, Hao
Chi, Yuyan
Smart Home Control and Management Based on Big Data Analysis
title Smart Home Control and Management Based on Big Data Analysis
title_full Smart Home Control and Management Based on Big Data Analysis
title_fullStr Smart Home Control and Management Based on Big Data Analysis
title_full_unstemmed Smart Home Control and Management Based on Big Data Analysis
title_short Smart Home Control and Management Based on Big Data Analysis
title_sort smart home control and management based on big data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8853757/
https://www.ncbi.nlm.nih.gov/pubmed/35186060
http://dx.doi.org/10.1155/2022/3784756
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