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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7035 |
| _version_ | 1797511793066639360 |
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| author | Dina Fawzy Sherin Moussa Nagwa Badr |
| author_facet | Dina Fawzy Sherin Moussa Nagwa Badr |
| author_sort | Dina Fawzy |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:53:03Z |
| format | Article |
| id | doaj.art-8ecd0a37dcaa44df852715187a5f0335 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:03Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ecd0a37dcaa44df852715187a5f03352023-11-22T21:35:37ZengMDPI AGSensors1424-82202021-10-012121703510.3390/s21217035The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data AnalyticsDina Fawzy0Sherin Moussa1Nagwa Badr2Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptInformation Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptInformation Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptEnormous 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.https://www.mdpi.com/1424-8220/21/21/7035Internet of Thingsbig data analyticsdata fusionreal-time processingdata reductiondata aggregation |
| spellingShingle | Dina Fawzy Sherin Moussa Nagwa Badr The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics Sensors Internet of Things big data analytics data fusion real-time processing data reduction data aggregation |
| title | 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_short | 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 |
| topic | Internet of Things big data analytics data fusion real-time processing data reduction data aggregation |
| url | https://www.mdpi.com/1424-8220/21/21/7035 |
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