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http://dx.doi.org/10.14400/JDC.2020.18.3.189

Probability-based Deep Learning Clustering Model for the Collection of IoT Information  

Jeong, Yoon-Su (Dept. of Information Communication & Engineeringe, Mokwon University)
Publication Information
Journal of Digital Convergence / v.18, no.3, 2020 , pp. 189-194 More about this Journal
Abstract
Recently, various clustering techniques have been studied to efficiently handle data generated by heterogeneous IoT devices. However, existing clustering techniques are not suitable for mobile IoT devices because they focus on statically dividing networks. This paper proposes a probabilistic deep learning-based dynamic clustering model for collecting and analyzing information on IoT devices using edge networks. The proposed model establishes a subnet by applying the frequency of the attribute values collected probabilistically to deep learning. The established subnets are used to group information extracted from seeds into hierarchical structures and improve the speed and accuracy of dynamic clustering for IoT devices. The performance evaluation results showed that the proposed model had an average 13.8 percent improvement in data processing time compared to the existing model, and the server's overhead was 10.5 percent lower on average than the existing model. The accuracy of extracting IoT information from servers has improved by 8.7% on average from previous models.
Keywords
Internet of Things; information collection and extraction; probability-based; deep learning; clustering;
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