TY - GEN
T1 - Support vector classification strategies for localization in sensor networks
AU - Tran, Duc A.
AU - Nguyen, Thinh
PY - 2006
Y1 - 2006
N2 - 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 multi-class SVM strategy. They are shown to perform well in various aspects such as localization error, processing efficiency, and effectiveness in addressing the border issue.
AB - 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 multi-class SVM strategy. They are shown to perform well in various aspects such as localization error, processing efficiency, and effectiveness in addressing the border issue.
UR - https://www.scopus.com/pages/publications/46249127226
UR - https://www.scopus.com/pages/publications/46249127226#tab=citedBy
U2 - 10.1109/CCE.2006.350857
DO - 10.1109/CCE.2006.350857
M3 - Conference contribution
AN - SCOPUS:46249127226
SN - 1424405688
SN - 9781424405688
T3 - HUT-ICCE 2006 First International Conference on Communications and Electronics, Proceedings
SP - 29
EP - 34
BT - HUT-ICCE 2006 1st International Conference on Communications and Electronics, Proceedings
T2 - HUT-ICCE 2006 1st International Conference on Communications and Electronics
Y2 - 10 October 2006 through 11 October 2006
ER -