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1. Distributed signal classification for wireless sensor networks |
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Processing sensor data locally requires considerably less energy than communicating it to a distant node, yielding an interesting communication / computation trade off. To reduce global communication requirements, one needs to perform signal processing to extract key information in a distributed fashion and without losing fidelity. We have considered the problem of distributed classification in a WSN by means of support vector machines (SVM). Taking advantage of the sparse representation that SVMs provide for the decision boundaries, we designed classes of
distributed consensus algorithms for training the classifier. |
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ASPIRE members involved: Flouri, Kornaropoulos, Beferull-Lozano, Stathopoulos, Tsakalides. |
Collaborative Signal Processing for Efficient Wireless Sensor Networks
ACHIEVEMENTS