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

fYear

2006

fDate

10-11 Oct. 2006

Firstpage

29

Lastpage

34

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CCE.2006.350857

Filename

4156508