By Topic

Node location estimation scheme in wireless sensor networks based on support vector regression

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

3 Author(s)
Songbin Zhou ; Sch. of Mech. Eng., South China Univ. of Technol., Guangzhou ; Xiaoping Zhang ; Guixiong Liu

In view of the issue that the accuracy of the node location of wireless sensor networks (WSN) is low by adopting maximum likelihood estimation (MLE) in estimating the measurement information value with big noise, a new node location estimation scheme based on support vector regression (SVR-NLE) is proposed. Through learning the relation between the real value of trilateral and node coordinate, this method utilizes the generalization capability of SVR (support vector regression) to achieve better location on the same noise level. The experiments choose LS-SVR (least squares SVR) and epsiv - SVR ( epsiv -insensitive SVR) to estimate the location of 100 randomly distributed unknown nodes. The result indicates that this new method can improve 15-20% location accuracy than MLE.

Published in:

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Date of Conference:

25-27 June 2008