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Open AccessArticle

Data-Filtering System to Avoid Total Data Distortion in IoT Networking

Department of Software Engineering, Changshin University, Changwon 51352, Korea
Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea
Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
Author to whom correspondence should be addressed.
Academic Editor: Young-Sik Jeong
Symmetry 2017, 9(1), 16;
Received: 30 September 2016 / Revised: 25 December 2016 / Accepted: 16 January 2017 / Published: 20 January 2017
(This article belongs to the Special Issue Symmetry in Secure Cyber World)
In the Internet of Things (IoT) networking, numerous objects are connected to a network. They sense events and deliver the sensed information to the cloud. A lot of data is generated in the IoT network, and servers in the cloud gather the sensed data from the objects. Then, the servers analyze the collected data and provide proper intelligent services to users through the results of the analysis. When the server analyzes the collected data, if there exists malfunctioning data, distortional results of the analysis will be generated. The distortional results lead to misdirection of the intelligent services, leading to poor user experience. In the analysis for intelligent services in IoT, malfunctioning data should be avoided because integrity of the collected data is crucial. Therefore, this paper proposes a data-filtering system for the server in the cloud. The proposed data-filtering system is placed in front of the server and firstly receives the sensed data from the objects. It employs the naïve Bayesian classifier and, by learning, classifies the malfunctioning data from among the collected data. Data with integrity is delivered to the server for analysis. Because the proposed system filters the malfunctioning data, the server can obtain accurate analysis results and reduce computing load. The performance of the proposed data-filtering system is evaluated through computer simulation. Through the simulation results, the efficiency of the proposed data-filtering system is shown. View Full-Text
Keywords: data-filtering system; data distortion; naïve Bayesian classifier; Internet of Things (IoT) data-filtering system; data distortion; naïve Bayesian classifier; Internet of Things (IoT)
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MDPI and ACS Style

Kim, D.-Y.; Jeong, Y.-S.; Kim, S. Data-Filtering System to Avoid Total Data Distortion in IoT Networking. Symmetry 2017, 9, 16.

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