Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)

  • 2020.05a
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  • Pages.69-71
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  • 2020
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  • 2005-0011(pISSN)
  • /
  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
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Joint PCA and Adaptive Threshold for Fault Detection in Wireless Sensor Networks

무선 센서 네트워크에서 장애 검출을 위한 결합 주성분분석과 적응형 임계값

  • Dang, Thien-Binh (Dept. of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Vo, Vi Van (Dept. of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Le, Duc-Tai (Dept. of Electrical and Computer Engineering, Sungkyunkwan University) ;
  • Kim, Moonseong (Dept. of Liberal Arts, Seoul Theological University) ;
  • Choo, Hyunseung (Dept. of Electrical and Computer Engineering, Sungkyunkwan University)
  • Published : 2020.05.29
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Abstract

Principal Component Analysis (PCA) is an effective data analysis technique which is commonly used for fault detection on collected data of Wireless Sensor Networks (WSN), However, applying PCA on the whole data make the detection performance low. In this paper, we propose Joint PCA and Adaptive Threshold for Fault Detection (JPATAD). Experimental results on a real dataset show a remarkably higher performance of JPATAD comparing to conventional PCA model in detection of noise which is a popular fault in collected data of sensors.

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