Dr Taha Mansouri T.Mansouri@salford.ac.uk
Lecturer in AI
Dr Taha Mansouri T.Mansouri@salford.ac.uk
Lecturer in AI
Prof Julian Bass J.Bass@salford.ac.uk
Professor of Software Engineering
Tarek Gaber
Steven Wright
Benedict Scorey
IoT sensors capture different aspects of the environmental data and generate high throughput data streams. To harvest potential values from these sensors, a system fulfilling the big data requirements should be designed. In this work, we reviewed the important nonfunctional requirements, in particular big data-based
ones. Moreover, we dug out a conventional IoT architecture to address these requirements. Finally, we designed a brokering based architecture which is flexible and scalable enough to cover big data
requirements of high throughput data streams resulted from modern sensors. Evaluation results using quantitative comparisons on use case displayed that the proposed new architecture outperformed the
conventional ones. The experiments showed that the proposed architecture can handle 32 times more load than the conventional.
Mansouri, T., Bass, J., Gaber, T., Wright, S., & Scorey, B. (2023). A Data Brokering Architecture to Guarantee Nonfunctional Requirements in IoT Applications. In Big Data Technologies and Applications (75-84). https://doi.org/10.1007/978-3-031-33614-0_6
Acceptance Date | May 9, 2023 |
---|---|
Online Publication Date | May 26, 2023 |
Publication Date | May 26, 2023 |
Deposit Date | May 18, 2023 |
Publisher | Springer |
Volume | 480 |
Pages | 75-84 |
Series Title | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
Book Title | Big Data Technologies and Applications |
ISBN | 9783031336133 |
DOI | https://doi.org/10.1007/978-3-031-33614-0_6 |
Publisher URL | https://link.springer.com/chapter/10.1007/978-3-031-33614-0_6 |
Explainable fault prediction using learning fuzzy cognitive maps
(2023)
Journal Article
Developing an industry 4.0 readiness model using fuzzy cognitive maps approach
(2022)
Journal Article
A deep explainable model for fault prediction using IoT sensors
(2022)
Journal Article
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search