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Efficient Noise Reduction System in Industrial IoT Data Streams

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021 (AISI 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 100))

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Abstract

In the Industrial Internet of Things (IIoT) applications, sensor data quality has an essential role as they become useless if the quality of data is poor. When the data collected from IIoT sensors is poor quality data, it will lead to distorted analysis results that yields to misdirection of smart services. Therefore, noise reduction in IIoT data stream is one of the main challenges. In this paper, a framework that combines K-Nearest Neighbors (KNN) algorithm and Kalman Filter (KF) algorithm is proposed. The KNN algorithm is implemented to identify the error in data and output the transition matrix that is used as the input to KF which predicts the final prediction value. Various tests are carried out to evaluate the proposed KNN-KF algorithm and compare it with state-of-the-art systems. The simulated results prove that the proposed KNN-KF algorithm improves accuracy with a percentage that exceeds 30% at most cases.

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Authors and Affiliations

  1. Electrical Engineering Department, Port Said University, Port Said, Egypt

    Rabab Farouk Abdel-Kader, Noha Emad El-Sayad & Rawya Yehia Rizk

Authors
  1. Rabab Farouk Abdel-Kader
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  2. Noha Emad El-Sayad
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  3. Rawya Yehia Rizk
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Corresponding author

Correspondence to Noha Emad El-Sayad .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Artificial Intelligence, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VÅ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. Fujian University of Technology, Shulin District, New Taipei, Taiwan

    Kuo-Chi Chang

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. University of Salford, Manchester, UK

    Tarek Gaber

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Abdel-Kader, R.F., El-Sayad, N.E., Rizk, R.Y. (2022). Efficient Noise Reduction System in Industrial IoT Data Streams. In: Hassanien, A.E., Snášel, V., Chang, KC., Darwish, A., Gaber, T. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2021. AISI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-030-89701-7_20

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