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Support Vector Classification Strategies for Localization in Sensor Networks

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2 Author(s)
Duc A. Tran ; Department of Computer Science, University of Dayton, Dayton, OH 45469. Email: ; Thinh Nguyen

We consider the problem of estimating the geographic locations of nodes in a wireless sensor network where most sensors are without an effective self-positioning functionality. A solution to this localization problem is proposed, which uses support vector machines (SVM) and mere connectivity information only. We investigate two versions of this solution, each employing a different multiclass SVM strategy. They are shown to perform well in various aspects such as localization error, processing efficiency, and effectiveness in addressing the border issue.

Published in:

2006 First International Conference on Communications and Electronics

Date of Conference:

10-11 Oct. 2006