By Topic

Support Vector Classification Strategies for Localization in Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tran, D.A. ; Dept. of Comput. Sci., Dayton Univ., OH, USA ; 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:

Communications and Electronics, 2006. ICCE '06. First International Conference on

Date of Conference:

10-11 Oct. 2006