Name
Abstract | ||
|---|---|---|
We present a new approach for the detection of complex events in Wireless Sensor Networks. Complex events are sets of data points that correspond to interesting or unusual patterns in the underlying phenomenon that the network monitors. Our approach is inspired from time-series data mining techniques and transforms a stream of real-valued sensor readings into a symbolic representation. Complex event detection is then performed using distance metrics, allowing us to detect events that are difficult or even impossible to describe using traditional declarative SQL-like languages and thresholds. We have tested our approach with four distinct data sets and the experimental results were encouraging in all cases. We have implemented our approach for the TinyOS and Contiki Operating Systems, for the Sky mote platform. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-75696-5_17 | EuroSSC |
Keywords | Field | DocType |
sky mote platform,time-series data mining technique,contiki operating systems,distance metrics,complex event detection,wireless sensor network,data point,distinct data set,complex event,new approach,wireless sensor networks,sensor network,data compression,operating system,distance metric,network monitoring | Data point,Key distribution in wireless sensor networks,Data mining,Data set,Computer science,Visual sensor network,Real-time computing,Mobile wireless sensor network,Network control,Data compression,Wireless sensor network | Conference |
Volume | ISSN | ISBN |
4793 | 0302-9743 | 3-540-75695-7 |
Citations | PageRank | References |
22 | 1.25 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Zoumboulakis | 1 | 39 | 3.34 |
| George Roussos | 2 | 79 | 6.28 |