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Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks

Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning,...

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Detalles Bibliográficos
Autores principales: Yoo, Jaehyun, Kim, Hyoun Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299091/
https://www.ncbi.nlm.nih.gov/pubmed/25615729
http://dx.doi.org/10.3390/s141223871
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author Yoo, Jaehyun
Kim, Hyoun Jin
author_facet Yoo, Jaehyun
Kim, Hyoun Jin
author_sort Yoo, Jaehyun
collection PubMed
description Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data.
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spelling pubmed-42990912015-01-26 Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks Yoo, Jaehyun Kim, Hyoun Jin Sensors (Basel) Article Tracking the locations and identities of moving targets in the surveillance area of wireless sensor networks is studied. In order to not rely on high-cost sensors that have been used in previous researches, we propose the integrated localization and classification based on semi-supervised learning, which uses both labeled and unlabeled data obtained from low-cost distributed sensor network. In our setting, labeled data are obtained by seismic and PIR sensors that contain information about the types of the targets. Unlabeled data are generated from the RF signal strength by applying Gaussian process, which represents the probability of predicted target locations. Finally, by using classified unlabeled data produced by semi-supervised learning, identities and locations of multiple targets are estimated. In addition, we consider a case when the labeled data are absent, which can happen due to fault or lack of the deployed sensor nodes and communication failure. We overcome this situation by defining artificial labeled data utilizing characteristics of support vector machine, which provides information on the importance of each training data point. Experimental results demonstrate the accuracy of the proposed tracking algorithm and its robustness to the absence of the labeled data thanks to the artificial labeled data. MDPI 2014-12-11 /pmc/articles/PMC4299091/ /pubmed/25615729 http://dx.doi.org/10.3390/s141223871 Text en © 2014 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, Hyoun Jin
Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title_full Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title_fullStr Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title_full_unstemmed Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title_short Target Tracking and Classification from Labeled and Unlabeled Data in Wireless Sensor Networks
title_sort target tracking and classification from labeled and unlabeled data in wireless sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299091/
https://www.ncbi.nlm.nih.gov/pubmed/25615729
http://dx.doi.org/10.3390/s141223871
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