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EnglishThe paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.
DE PAOLA, A., Gaglio, S., LO RE, G., Milazzo, F., & Ortolani, M. (2014). Adaptive Distributed Outlier Detection for WSNs. IEEE TRANSACTIONS ON CYBERNETICS, PP, 1-12.
| Data di pubblicazione: | 2014 | |
| Titolo: | Adaptive Distributed Outlier Detection for WSNs | |
| Autori: | ||
| Citazione: | DE PAOLA, A., Gaglio, S., LO RE, G., Milazzo, F., & Ortolani, M. (2014). Adaptive Distributed Outlier Detection for WSNs. IEEE TRANSACTIONS ON CYBERNETICS, PP, 1-12. | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/TCYB.2014.2338611 | |
| Abstract: | The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy. | |
| Settore Scientifico Disciplinare: | Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/10447/97658
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