Big Data Analytics in Industrial IoT Using a Concentric Computing Model
The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, cl...
| Main Authors: | , , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2018
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| _version_ | 1825721587282739200 |
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| author | Rehman, Muhammad Habib ur Ahmed, Ejaz Yaqoob, Ibrar Hashem, Ibrahim Abaker Targio Imran, Muhammad Ahmad, Shafiq |
| author_facet | Rehman, Muhammad Habib ur Ahmed, Ejaz Yaqoob, Ibrar Hashem, Ibrahim Abaker Targio Imran, Muhammad Ahmad, Shafiq |
| author_sort | Rehman, Muhammad Habib ur |
| collection | UM |
| description | The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-Time data analytics, data integration, transmission of meaningful data, edge analytics, real-Time fusion of streaming data, and security and privacy). |
| first_indexed | 2024-03-06T05:52:37Z |
| format | Article |
| id | um.eprints-20898 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:52:37Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | um.eprints-208982019-04-15T07:48:20Z http://eprints.um.edu.my/20898/ Big Data Analytics in Industrial IoT Using a Concentric Computing Model Rehman, Muhammad Habib ur Ahmed, Ejaz Yaqoob, Ibrar Hashem, Ibrahim Abaker Targio Imran, Muhammad Ahmad, Shafiq QA75 Electronic computers. Computer science The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-Time data analytics, data integration, transmission of meaningful data, edge analytics, real-Time fusion of streaming data, and security and privacy). Institute of Electrical and Electronics Engineers (IEEE) 2018 Article PeerReviewed Rehman, Muhammad Habib ur and Ahmed, Ejaz and Yaqoob, Ibrar and Hashem, Ibrahim Abaker Targio and Imran, Muhammad and Ahmad, Shafiq (2018) Big Data Analytics in Industrial IoT Using a Concentric Computing Model. IEEE Communications Magazine, 56 (2). pp. 37-43. ISSN 0163-6804, DOI https://doi.org/10.1109/MCOM.2018.1700632 <https://doi.org/10.1109/MCOM.2018.1700632>. https://doi.org/10.1109/MCOM.2018.1700632 doi:10.1109/MCOM.2018.1700632 |
| spellingShingle | QA75 Electronic computers. Computer science Rehman, Muhammad Habib ur Ahmed, Ejaz Yaqoob, Ibrar Hashem, Ibrahim Abaker Targio Imran, Muhammad Ahmad, Shafiq Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title | Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title_full | Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title_fullStr | Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title_full_unstemmed | Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title_short | Big Data Analytics in Industrial IoT Using a Concentric Computing Model |
| title_sort | big data analytics in industrial iot using a concentric computing model |
| topic | QA75 Electronic computers. Computer science |
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