In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an analysis of the sensor readings, thus avoiding additional control overhead. In particular, abnormal behavior of a sensor node will be inferred by analyzing the spatiotemporal correlation of its data with respect to its neighborhood. The algorithm is tested on a public dataset, over which different classes of faults were artificially superimposed.

Farruggia, A., Lo Re, G., Ortolani, M. (2011). Detecting Faulty Wireless Sensor Nodes through Stochastic Classification. In 7th IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (pp.92-97) [10.1109/PERCOMW.2011.5766858].

Detecting Faulty Wireless Sensor Nodes through Stochastic Classification

FARRUGGIA, Alfonso;LO RE, Giuseppe;ORTOLANI, Marco
2011-01-01

Abstract

In many distributed systems, the possibility to adapt the behavior of the involved resources in response to unforeseen failures is an important requirement in order to significantly reduce the costs of management. Autonomous detection of faulty entities, however, is often a challenging task, especially when no direct human intervention is possible, as is the case for many scenarios involving Wireless Sensor Networks (WSNs), which usually operate in inaccessible and hostile environments. This paper presents an unsupervised approach for identifying faulty sensor nodes within a WSN. The proposed algorithm uses a probabilistic approach based on Markov Random Fields, requiring exclusively an analysis of the sensor readings, thus avoiding additional control overhead. In particular, abnormal behavior of a sensor node will be inferred by analyzing the spatiotemporal correlation of its data with respect to its neighborhood. The algorithm is tested on a public dataset, over which different classes of faults were artificially superimposed.
2011
PerSens2011 - 7th IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing
Seattle, USA
2011
6
Farruggia, A., Lo Re, G., Ortolani, M. (2011). Detecting Faulty Wireless Sensor Nodes through Stochastic Classification. In 7th IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (pp.92-97) [10.1109/PERCOMW.2011.5766858].
Proceedings (atti dei congressi)
Farruggia, A; Lo Re, G; Ortolani, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/61795
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