The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics

Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, pos...

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Autores principales:	Dina Fawzy, Sherin Moussa, Nagwa Badr
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Internet of Things big data analytics data fusion real-time processing data reduction data aggregation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8ecd0a37dcaa44df852715187a5f0335
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spelling	oai:doaj.org-article:8ecd0a37dcaa44df852715187a5f03352021-11-11T19:04:01ZThe Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics10.3390/s212170351424-8220https://doaj.org/article/8ecd0a37dcaa44df852715187a5f03352021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7035https://doaj.org/toc/1424-8220Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.Dina FawzySherin MoussaNagwa BadrMDPI AGarticleInternet of Thingsbig data analyticsdata fusionreal-time processingdata reductiondata aggregationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7035, p 7035 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Internet of Things big data analytics data fusion real-time processing data reduction data aggregation Chemical technology TP1-1185
spellingShingle	Internet of Things big data analytics data fusion real-time processing data reduction data aggregation Chemical technology TP1-1185 Dina Fawzy Sherin Moussa Nagwa Badr The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
description	Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.
format	article
author	Dina Fawzy Sherin Moussa Nagwa Badr
author_facet	Dina Fawzy Sherin Moussa Nagwa Badr
author_sort	Dina Fawzy
title	The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
title_short	The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
title_full	The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
title_fullStr	The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
title_full_unstemmed	The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics
title_sort	spatiotemporal data fusion (stdf) approach: iot-based data fusion using big data analytics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8ecd0a37dcaa44df852715187a5f0335
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The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics

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