Title :
Node location estimation scheme in wireless sensor networks based on support vector regression
Author :
Zhou, Songbin ; Zhang, Xiaoping ; Liu, Guixiong
Author_Institution :
Sch. of Mech. Eng., South China Univ. of Technol., Guangzhou
Abstract :
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.
Keywords :
least mean squares methods; regression analysis; support vector machines; wireless sensor networks; epsiv -SVR; least squares SVR; node location estimation; support vector regression; wireless sensor network; Automation; Equations; Intelligent control; Maximum likelihood estimation; Monitoring; Noise measurement; Nonlinear systems; Position measurement; Time measurement; Wireless sensor networks; Support Vector Regression; Wireless Sensor Networks; location estimation;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593943