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Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification

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Abstract

Wireless Sensor Network is composed of small, low cost, low energy, and multifunctional sensors. In addition, this network could have scalability, topology, synchronization, radio-coverage, safety and security constraints . Therefore, our challenge is to classify data into normal and abnormal measurements using outlier detection methods. This paper explore the density-based method Ordering Points to Identify the Clustering Structure. Proposed detector applies an auto- configuration of parameters without previous known environmental conditions. It also extracts hierarchical clusters that serve in a post-processing treatment for classification of data into errors and events. Performance is examined within a real and synthetic databases from Intel Berkeley Research lab. Results demonstrate that our proposed process analyzes data of this network with an average equal to 81% of outlier detection rate, 74% of precision rate and only 2% of false alarms rate that it is very low compared to other methods.

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Authors and Affiliations

  1. CES-Lab, ENIS, University of Sfax, Sfax, Tunisia

    Aymen Abid

  2. LETI-Lab, ENIS, University of Sfax, Sfax, Tunisia

    Atef Masmoudi & Abdennaceur Kachouri

  3. CCIT, Taif University, Taif, Saudi Arabia

    Adel Mahfoudhi

Authors
  1. Aymen Abid
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  2. Atef Masmoudi
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  3. Abdennaceur Kachouri
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  4. Adel Mahfoudhi
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Correspondence to Aymen Abid.

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Abid, A., Masmoudi, A., Kachouri, A. et al. Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification. Wireless Pers Commun 97, 1503–1515 (2017). https://doi.org/10.1007/s11277-017-4583-7

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  • DOI: https://doi.org/10.1007/s11277-017-4583-7

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