Title :
Support Vector Classification Strategies for Localization in Sensor Networks
Author :
Tran, Duc A. ; Nguyen, Thinh
Author_Institution :
Dept. of Comput. Sci., Dayton Univ., OH, USA
Abstract :
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.
Keywords :
support vector machines; wireless sensor networks; geographic locations; localization error; processing efficiency; support vector classification; support vector machines; wireless sensor network; Computer networks; Computer science; Convergence; Event detection; Kernel; State estimation; Support vector machine classification; Support vector machines; Training data; Wireless sensor networks;
Conference_Titel :
Communications and Electronics, 2006. ICCE '06. First International Conference on
Conference_Location :
Hanoi
Print_ISBN :
1-4244-0568-8
Electronic_ISBN :
1-4244-0569-6
DOI :
10.1109/CCE.2006.350857
