Smart IoT Home Data Analysis and Device Control Algorithm Using Deep Learning

KIPS Transactions on Computer and Communication Systems, Vol. 7, No.4, pp.103-110, April 2018
10.3745/KTCCS.2018.7.4.103, Full Text

Abstract

Services that enhance user convenience by using various IoT devices are increasing with the development of Internet of Things(IoT) technology. Also, since the price of IoT sensors has become cheaper, companies providing services by collecting and utilizing data from various sensors are increasing. The smart IoT home system is a representative use case that improves the user convenience by using IoT devices. To improve user convenience of Smart IoT home system, this paper proposes a method for the control of related devices based on data analysis. Internal environment measurement data collected from IoT sensors, device control data collected from device control actuators, and user judgment data are learned to predict the current home state and control devices. Especially, differently from previous approaches, it uses deep neural network to analyze the data to determine the inner state of the home and provide information for maintaining the optimal inner environment. In the experiment, we compared the results of the long-term measured data with the inferred data and analyzed the discrimination performance of the proposed method.


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Cite this paper

[KIPS Transactions Style]
S. Lee and H. Lee, "Smart IoT Home Data Analysis and Device Control Algorithm Using Deep Learning," KIPS Transactions on Computer and Communication Systems, Vol.7, No.4, pp.103-110, 2018, DOI: 10.3745/KTCCS.2018.7.4.103.

[IEEE Style]
Sang-Hyeong Lee and Hae-Yeoun Lee, "Smart IoT Home Data Analysis and Device Control Algorithm Using Deep Learning," KIPS Transactions on Computer and Communication Systems, vol. 7, no. 4, pp. 103-110, 2018. DOI: 10.3745/KTCCS.2018.7.4.103.

[ACM Style]
Lee, S. and Lee, H. 2018. Smart IoT Home Data Analysis and Device Control Algorithm Using Deep Learning. KIPS Transactions on Computer and Communication Systems, 7, 4, (2018), 103-110. DOI: 10.3745/KTCCS.2018.7.4.103.