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Open AccessArticle

Sensor-Based Optimization Model for Air Quality Improvement in Home IoT

by Jonghyuk Kim 1 and Hyunwoo Hwangbo 1,2,*
1
Big Data Analytics Team, Kolon Benit Co., Ltd., 11 Kolon-ro, Gwacheon-si 13837, Gyeonggi-do, Korea
2
Department of Data & Knowledge Service Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si 16890, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 959; https://doi.org/10.3390/s18040959
Received: 31 January 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market. View Full-Text
Keywords: sensor-based home Internet of Things (IoT); indoor air quality; random data generation; spectrum/density analysis; natural cubic spline; user behavioral value sensor-based home Internet of Things (IoT); indoor air quality; random data generation; spectrum/density analysis; natural cubic spline; user behavioral value
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Kim, J.; Hwangbo, H. Sensor-Based Optimization Model for Air Quality Improvement in Home IoT. Sensors 2018, 18, 959.

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