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Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression
Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector reg...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507659/ https://www.ncbi.nlm.nih.gov/pubmed/26024420 http://dx.doi.org/10.3390/s150612539 |
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author | Yoo, Jaehyun Kim, H. Jin |
author_facet | Yoo, Jaehyun Kim, H. Jin |
author_sort | Yoo, Jaehyun |
collection | PubMed |
description | Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods. |
format | Online Article Text |
id | pubmed-4507659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-45076592015-07-22 Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression Yoo, Jaehyun Kim, H. Jin Sensors (Basel) Article Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods. MDPI 2015-05-27 /pmc/articles/PMC4507659/ /pubmed/26024420 http://dx.doi.org/10.3390/s150612539 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yoo, Jaehyun Kim, H. Jin Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title | Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title_full | Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title_fullStr | Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title_full_unstemmed | Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title_short | Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression |
title_sort | target localization in wireless sensor networks using online semi-supervised support vector regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507659/ https://www.ncbi.nlm.nih.gov/pubmed/26024420 http://dx.doi.org/10.3390/s150612539 |
work_keys_str_mv | AT yoojaehyun targetlocalizationinwirelesssensornetworksusingonlinesemisupervisedsupportvectorregression AT kimhjin targetlocalizationinwirelesssensornetworksusingonlinesemisupervisedsupportvectorregression |