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Fog of Things Framework to Handle Data Streaming Heterogeneity on Internet of Things

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Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 (AISI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 152))

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Abstract

Internet of Things (IoT) sensors produce a huge amount of data every second. This unexpected data volume needs significant effort to manage, structure, save, and process to produce effective information and best utilize it. Extracting valuable knowledge from this heterogonous data is a high demanding need. Making accurate decisions based on this data was a dream in the near past. Data management and handling data heterogeneity locally on sensor boards and regionally on local servers using Fog and centrally on Cloud acts as an enabler for giving IoT an excellent value even if it faces many complexities and challenges, as discussed in this paper. Moreover, data management in IoT is a serious issue due to communication among billions of devices, which generate massive datasets. Data analysis on such a big volume of data is a difficult undertaking due to the lack of any standard. A definition of IoT-based data should be established to determine what is available and how it can be used. A study like this also points to the need for innovative approaches to deal with such problems. It's a great difficulty to cope with such a range of data as IoT delivers processing nodes in the form of smart nodes due to the heterogeneity of linked nodes, varied data rates, and formats. The Industrial IoT involves real-time analysis of data from interconnected sensor devices. It provides a solid foundation for big data research. Real-time data streaming adds an extra dimension of complexity and involves some more challenges. we briefly outline current open issues of edge computing and cloud computing platforms based on our literature survey for IoT data streaming heterogeneity. Finally, we propose Fog of Things Framework as a solution for all these challenges.

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Authors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Sherif Zahran, Hatem Elkadi & Waleed Helm

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  1. Sherif Zahran
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  2. Hatem Elkadi
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  3. Waleed Helm
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Correspondence to Sherif Zahran .

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Editors and Affiliations

  1. Faculty of Computers Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Mincong Tang

  4. College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China

    Tien-Wen Sung

  5. Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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Zahran, S., Elkadi, H., Helm, W. (2023). Fog of Things Framework to Handle Data Streaming Heterogeneity on Internet of Things. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_53

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