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A probabilistic multivariate Copula-based technique for faulty node diagnosis in Wireless Sensor Networks

Sanaa Ghalem 1 Bouabdellah Kechar 1 Ahcène Bounceur 2 Reinhardt Euler 3
2 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_UBO_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Wireless sensor networks (WSNs) find extensive applications in various sensitive domains such as tracking, monitoring, environmental data collection and border surveillance. In these cases, the collected data are considered as a critical resource and used to detect any anomalies or abnormal behavior, providing information about an occurring event or a node failure. An outlier detection process must be set up to ensure the proper functioning of the monitoring system. The existing approaches are limited by assumptions on a specific distribution or a predefined data range of the collected data. Often these assumptions do not hold in practice, the data distribution is not known or determining reliable upper and lower bounds for the set of data is not possible. To overcome this, we propose a new copula-based probabilistic multivariate outlier detection method for faulty node detection in wireless sensor networks (WSNs). The joint probability density function of the copula is constructed considering dependency among the captured n−sensed measures without making any assumptions on the distribution of the collected data. The samples having probabilities violating a predetermined control limit are classified to be faulty. The performance of the proposed technique is observed to be better than the existing statistical methods.
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https://hal.univ-brest.fr/hal-02501726
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Sanaa Ghalem, Bouabdellah Kechar, Ahcène Bounceur, Reinhardt Euler. A probabilistic multivariate Copula-based technique for faulty node diagnosis in Wireless Sensor Networks. Journal of Network and Computer Applications, Elsevier, 2019, 127 (1), pp.9-25. ⟨10.1016/j.jnca.2018.11.009⟩. ⟨hal-02501726⟩

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