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A Survey of Data Semantization in Internet of Things

With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by mul...

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Detalles Bibliográficos
Autores principales: Shi, Feifei, Li, Qingjuan, Zhu, Tao, Ning, Huansheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795333/
https://www.ncbi.nlm.nih.gov/pubmed/29361772
http://dx.doi.org/10.3390/s18010313
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author Shi, Feifei
Li, Qingjuan
Zhu, Tao
Ning, Huansheng
author_facet Shi, Feifei
Li, Qingjuan
Zhu, Tao
Ning, Huansheng
author_sort Shi, Feifei
collection PubMed
description With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study.
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spelling pubmed-57953332018-02-13 A Survey of Data Semantization in Internet of Things Shi, Feifei Li, Qingjuan Zhu, Tao Ning, Huansheng Sensors (Basel) Article With the development of Internet of Things (IoT), more and more sensors, actuators and mobile devices have been deployed into our daily lives. The result is that tremendous data are produced and it is urgent to dig out hidden information behind these volumous data. However, IoT data generated by multi-modal sensors or devices show great differences in formats, domains and types, which poses challenges for machines to process and understand. Therefore, adding semantics to Internet of Things becomes an overwhelming tendency. This paper provides a systematic review of data semantization in IoT, including its backgrounds, processing flows, prevalent techniques, applications, existing challenges and open issues. It surveys development status of adding semantics to IoT data, mainly referring to sensor data and points out current issues and challenges that are worth further study. MDPI 2018-01-22 /pmc/articles/PMC5795333/ /pubmed/29361772 http://dx.doi.org/10.3390/s18010313 Text en © 2018 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shi, Feifei
Li, Qingjuan
Zhu, Tao
Ning, Huansheng
A Survey of Data Semantization in Internet of Things
title A Survey of Data Semantization in Internet of Things
title_full A Survey of Data Semantization in Internet of Things
title_fullStr A Survey of Data Semantization in Internet of Things
title_full_unstemmed A Survey of Data Semantization in Internet of Things
title_short A Survey of Data Semantization in Internet of Things
title_sort survey of data semantization in internet of things
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795333/
https://www.ncbi.nlm.nih.gov/pubmed/29361772
http://dx.doi.org/10.3390/s18010313
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