1. Target Learning for Wireless Sensor Networks Prasanth Jeevan

2. Motivation and Problem Formulation  Detection and classification methods application specific  Significant human-in-the-loop component  Learning aspect is done by the user  Requires gathering lots of data for offline processing  Semi-supervised learning at the node-level to learn target signatures for detection and classification  subset of person, person with ferrous object, vehicle  Detection and classification methods application specific  Significant human-in-the-loop component  Learning aspect is done by the user  Requires gathering lots of data for offline processing  Semi-supervised learning at the node-level to learn target signatures for detection and classification  subset of person, person with ferrous object, vehicle

3. Key Ideas  Node-level capabilities are sufficient to detect and classify targets  Multiple sensors on each mote  Aggregation to higher levels will improve confidence  Expectation Maximization algorithm  Each node will develop models of the different targets and “no-target”  Adaptation to changing environmental conditions will pose a significant challenge  Node-level capabilities are sufficient to detect and classify targets  Multiple sensors on each mote  Aggregation to higher levels will improve confidence  Expectation Maximization algorithm  Each node will develop models of the different targets and “no-target”  Adaptation to changing environmental conditions will pose a significant challenge

4. Current Status and Future Plans  Current  Real-world data from XSM/Trio motes  Need to process the data into useful features for classification  Implementation of EM (Matlab)  Must adapt to current problem and make robust to real-world data  Future  Bring learning in-network (on-line), adaptive  Expand from node-level and exploit correlation  Learn other aspects of detection/classification such as how to automatically manipulate data in the most effective way to bring out features for classification  Current  Real-world data from XSM/Trio motes  Need to process the data into useful features for classification  Implementation of EM (Matlab)  Must adapt to current problem and make robust to real-world data  Future  Bring learning in-network (on-line), adaptive  Expand from node-level and exploit correlation  Learn other aspects of detection/classification such as how to automatically manipulate data in the most effective way to bring out features for classification