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http://dx.doi.org/10.6109/jkiice.2017.21.11.2167

Design of Efficient Edge Computing based on Learning Factors Sharing with Cloud in a Smart Factory Domain  

Hwang, Zi-on (Department of Smart Systems Software Engineering, Hyupsung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.11, 2017 , pp. 2167-2175 More about this Journal
Abstract
In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.
Keywords
Edge Computing; Learning Factor Sharing; Instant decision-making; Smart Factory;
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Times Cited By KSCI : 1  (Citation Analysis)
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