Real-Time Data Processing Architecture for a Smart Cities 


Vol. 46,  No. 2, pp. 401-409, Feb.  2021
10.7840/kics.2021.46.2.401


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  Abstract

Smart City uses IoT-based ICT to solve various urban problems such as traffic, environment, and facilities, so that citizens can enjoy a convenient and comfortable life. The IoT-based smart city service is based on artificial intelligence platform technology that collects, stores and processes vast amounts of data generated in a smart city, and makes predictions and inferences by machine learning. In this paper, we briefly review technologies for real-time big data analysis platform development for application to a smart city and propose an architecture for real-time big data processing. In order to verify the big data processing architecture for a smart city, a TensorFlow-based real-time data analysis module and a data visualization module were added to construct the overall data flow. The platform proposed in this paper enables real-time data processing for vast amounts of data generated in a smart city, and the smart city manager can apply the desired machine learning model and check the results of prediction and inference through the dashboard. It is estimated that the proposed real-time big data analysis platform will contribute to solving urban problems.

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  Cite this article

[IEEE Style]

M. Lee, M. Kang, H. Kim, J. Kim, "Real-Time Data Processing Architecture for a Smart Cities," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 2, pp. 401-409, 2021. DOI: 10.7840/kics.2021.46.2.401.

[ACM Style]

Mo-se Lee, Min-su Kang, Hong-joon Kim, and Jae-hun Kim. 2021. Real-Time Data Processing Architecture for a Smart Cities. The Journal of Korean Institute of Communications and Information Sciences, 46, 2, (2021), 401-409. DOI: 10.7840/kics.2021.46.2.401.

[KICS Style]

Mo-se Lee, Min-su Kang, Hong-joon Kim, Jae-hun Kim, "Real-Time Data Processing Architecture for a Smart Cities," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 2, pp. 401-409, 2. 2021. (https://doi.org/10.7840/kics.2021.46.2.401)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880