1. Target Classification in Wireless Distributed Sensor Networks (WSDN) Using AI Techniques Can Komar komarcan@boun.edu.tr

2. 01/12/2003CMPE 5302 Contents What is WDSN? Surveillance Networks Our Goal Conclusions

3. 01/12/2003CMPE 5303 What is WDSN? Collection of sensor devices distributed (generally random) over an area Sensor devices  Small (a few mm or cm)  Low battery power  Low processing power & memory Used for surveillance, geographical observation etc.

4. 01/12/2003CMPE 5304 Surveillance Networks Security sensitive regions need to be monitored (border zones, war fields etc.) Wireless distributed sensor networks (WSDN) are useful for surveillance Different types available Acoustic Seismic

5. 01/12/2003CMPE 5305 Target Classification Goal  To classify the type of the vehicle detected in the field Data sets  The data collected by the Sensor Networks group in University of Wisconsin in 29 Palms Army Ground Test Area

6. 01/12/2003CMPE 5306 Our Goal To obtain a suitable set of classification methods for this task  Low computation  Unreliable/incomplete data sets  Information fusion Comparison of our results with those obtained in SensIT group

7. 01/12/2003CMPE 5307 Our Goal Feature extraction  Extraction of events from time series data  Filter out the noise in the data Classifiers used  K-Nearest Neighbor  Maximum Likelihood

8. 01/12/2003CMPE 5308 Conclusions K-nearest neighbor seems to have more accurate results but requires high-computation power The noise in the data affects the results drastically

9. Thanks… Any questions?