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Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

Cited 40 time in Web of Science Cited 52 time in Scopus
Authors
Kim, Woojin; Park, Jaemann; Yoo, Jaehyun; Kim, H. Jin; Park, Chan Gook
Issue Date
2013-08
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
IEEE Transactions on Cybernetics, Vol.43 No.4, pp. 1189-1198
Keywords
공학Ensemble support vector regression (SVR)target localizationwireless sensor networks(WSNs)
Abstract
Target localization, whose goal is to estimate the location of an unknown target, is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployments of large-scale WSNs have become economically feasible. However, there exist issues such as limited communication and the curse of dimensionality in applying machine-learning algorithms such as support vector regression (SVR) on large-scale WSNs. Here, in order to overcome such issues, we propose an ensemble implementation of SVR for the problem of target localization. The convergence property of the localization algorithm using the ensemble SVR is verified, and the robustness of the proposed scheme against measurement noise is analyzed. Furthermore, experimental results confirm that the estimation performance of the proposed method is more accurate and robust to measurement noise than the conventional SVR predictor.
ISSN
2168-2267
Language
English
URI
https://hdl.handle.net/10371/91661
DOI
https://doi.org/10.1109/TSMCB.2012.2226151
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Journal Papers (저널논문_기계항공공학부)
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