Abstract
In any sensor network one of the major challenges is to distinguish between the expected data and unexpected or faulty data. In this paper we have proposed a fault detection technique using DBSCAN and statistical model. DBSCAN is used to cluster the similar data and detect the outliers whereas statistical model is used to build a model to represent the expected behaviour of the sensor nodes. Using the expected behaviour model we have detected the faults in the data. Our experimental results on Intel Berkeley research lab dataset shows that faults have been successfully detected.
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References
Intel Berkeley Research lab dataset, http://db.csail.mit.edu/labdata/labdata.html
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Doreswamy, Narasegouda, S. (2014). Fault Detection in Sensor Network Using DBSCAN and Statistical Models. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_50
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DOI: https://doi.org/10.1007/978-3-319-02931-3_50
Publisher Name: Springer, Cham
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