Name
Abstract | ||
|---|---|---|
We present an application of statistical signal processing techniques to the problem of event detection in wireless sensor networks used for environmental monitoring. The proposed approach uses the well-established Principal Component Analysis (PCA) technique to build a compact model of the observed phenomena that cap- tures daily and seasonal trends in the collected measurements. We subsequently use the divergence between actual measurements and model predictions to detect the existence of discrete event s within the collected data streams. Our preliminary results show th at this event detection mechanism is sensitive enough to detect the onset of rain events using the temperature modality of a wireless s ensor network. |
Year | Venue | Keywords |
|---|---|---|
2009 | Clinical Orthopaedics and Related Research | principal component analysis,environmental monitoring,pattern recognition,seasonality,wireless sensor network,statistical signal processing |
Field | DocType | Volume |
Data mining,Data stream mining,Computer science,Real-time computing,Statistical signal processing,Wireless sensor network,Environmental monitoring,Principal component analysis | Journal | abs/0901.3 |
ISSN | Citations | PageRank |
Workshop for Data Sharing and Interoperability on the World Wide
Web (DSI 2007). April 2007, In Proceedings | 12 | 1.17 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayant Gupchup | 1 | 48 | 6.00 |
| Andreas Terzis | 2 | 2449 | 169.59 |
| Randal Burns | 3 | 1955 | 115.15 |
| Alexander S. Szalay | 4 | 959 | 105.36 |