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http://dx.doi.org/10.7471/ikeee.2020.24.2.475

Anomaly Data Detection Using Machine Learning in Crowdsensing System  

Kim, Mihui (School of Comp. Eng. & Applied Math., Computer System Institute, Hankyong National University)
Lee, Gihun (School of Comp. Eng. & Applied Math., Computer System Institute, Hankyong National University)
Publication Information
Journal of IKEEE / v.24, no.2, 2020 , pp. 475-485 More about this Journal
Abstract
Recently, a crowdsensing system that provides a new sensing service with real-time sensing data provided from a user's device including a sensor without installing a separate sensor has attracted attention. In the crowdsensing system, meaningless data may be provided due to a user's operation error or communication problem, or false data may be provided to obtain compensation. Therefore, the detection and removal of the abnormal data determines the quality of the crowdsensing service. The proposed methods in the past to detect these anomalies are not efficient for the fast-changing environment of crowdsensing. This paper proposes an anomaly data detection method by extracting the characteristics of continuously and rapidly changing sensing data environment by using machine learning technology and modeling it with an appropriate algorithm. We show the performance and feasibility of the proposed system using deep learning binary classification model of supervised learning and autoencoder model of unsupervised learning.
Keywords
Crowdsensing; Machine Learning; AutoML; Autoencoder; Anomaly Data Detection;
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Times Cited By KSCI : 2  (Citation Analysis)
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