Oyekan, J., Hutabarat, W. orcid.org/0000-0001-7393-7695, Turner, C. et al. (3 more authors) (2021) A knowledge-based cognitive architecture supported by machine learning algorithms for interpretable monitoring of large-scale satellite networks. Sensors, 21 (13). 4267. ISSN 1424-8220
Abstract
Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | satellite networks; machine learning; IoT |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jul 2021 15:13 |
Last Modified: | 08 Jul 2021 15:13 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/s21134267 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175776 |