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In-Network Sensor Data Modelling Methods for Fault Detection

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Evolving Ambient Intelligence (AmI 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 413))

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Abstract

Wireless sensor networks are attracting increasing interest but suffer from severe challenges such as low data reliability. To improve the data reliability, many sensor fault detection techniques have been proposed. Behind these methods, mathematical models are usually employed to serve as comparing metric to find faulty data in the absence of ground truth. In this paper, we firstly discuss sensor data features and their relevance to fault detection. Criteria that should be met to become a competent data model for the purpose of fault detection is summarised. Some existing sensor data modelling methods for fault detection are presented and qualitatively compared.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of St Andrews, UK

    Lei Fang & Simon Dobson

Authors
  1. Lei Fang
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  2. Simon Dobson
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Editor information

Editors and Affiliations

  1. Adaptive Information Cluster, School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael J. O’Grady

  2. University of Birjand, Iran

    Hamed Vahdat-Nejad

  3. Peter L. Reichertz Institute for Medical Informatics, Braunschweig, Germany

    Klaus-Hendrik Wolf

  4. Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

    Mauro Dragone

  5. University of St Andrews, UK

    Juan Ye

  6. RWTH Aachen University, Germany

    Carsten Röcker

  7. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Gregory O’Hare

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Fang, L., Dobson, S. (2013). In-Network Sensor Data Modelling Methods for Fault Detection. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-04406-4_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04405-7

  • Online ISBN: 978-3-319-04406-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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