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International Journal of Distributed Sensor Networks
Volume 2014 (2014), Article ID 265801, 7 pages
Research Article

Semisupervised Location Awareness in Wireless Sensor Networks Using Laplacian Support Vector Regression

School of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-742, Republic of Korea

Received 22 October 2013; Accepted 17 March 2014; Published 15 April 2014

Academic Editor: Hwa-Young Jeong

Copyright © 2014 Jaehyun Yoo and H. Jin Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Supervised machine learning has been widely used in context-aware wireless sensor networks (WSNs) to discover context descriptions from sensor data. However, collecting a lot of labeled training data in order to guarantee good performance requires much cost and time. For this reason, the semisupervised learning has been recently developed due to its superior performance despite using only a small amount of the labeled data. In this paper, we extend the standard support vector regression (SVR) to the semisupervised SVR by employing manifold regularization, which we call Laplacian SVR (LapSVR). The LapSVR is compared with the standard SVR and the semisupervised least square algorithm that is another recently developed semisupervised regression algorithm. The algorithms are evaluated for location awareness of multiple mobile robots in a WSN. The experimental results show that the proposed algorithm yields more accurate location estimates than the other algorithms.