A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks
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, proble...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/13/4267 |
| _version_ | 1827689049099337728 |
|---|---|
| author | John Oyekan Windo Hutabarat Christopher Turner Ashutosh Tiwari Hongmei He Raymon Gompelman |
| author_facet | John Oyekan Windo Hutabarat Christopher Turner Ashutosh Tiwari Hongmei He Raymon Gompelman |
| author_sort | John Oyekan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:10:22Z |
| format | Article |
| id | doaj.art-14b1caf97bcb4a62ba8518b02eb87955 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:10:22Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-14b1caf97bcb4a62ba8518b02eb879552023-11-22T01:10:54ZengMDPI AGSensors1424-82202021-06-012113426710.3390/s21134267A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite NetworksJohn Oyekan0Windo Hutabarat1Christopher Turner2Ashutosh Tiwari3Hongmei He4Raymon Gompelman5Department of Automatic Control and Systems Engineering, University of Sheffield, Portobello Street, Sheffield S1 3JD, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Portobello Street, Sheffield S1 3JD, UKSurrey Business School, University of Surrey, Guildford, Surrey GU2 7XH, UKDepartment of Automatic Control and Systems Engineering, University of Sheffield, Portobello Street, Sheffield S1 3JD, UKSchool of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UKIDirect UK Ltd., Derwent House, University Way, Cranfield, Bedfordshire MK43 0AZ, UKCyber–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.https://www.mdpi.com/1424-8220/21/13/4267satellite networksmachine learningIoT |
| spellingShingle | John Oyekan Windo Hutabarat Christopher Turner Ashutosh Tiwari Hongmei He Raymon Gompelman A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks Sensors satellite networks machine learning IoT |
| title | A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks |
| title_full | A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks |
| title_fullStr | A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks |
| title_full_unstemmed | A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks |
| title_short | A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks |
| title_sort | knowledge based cognitive architecture supported by machine learning algorithms for interpretable monitoring of large scale satellite networks |
| topic | satellite networks machine learning IoT |
| url | https://www.mdpi.com/1424-8220/21/13/4267 |
| work_keys_str_mv | AT johnoyekan aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT windohutabarat aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT christopherturner aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT ashutoshtiwari aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT hongmeihe aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT raymongompelman aknowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT johnoyekan knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT windohutabarat knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT christopherturner knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT ashutoshtiwari knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT hongmeihe knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks AT raymongompelman knowledgebasedcognitivearchitecturesupportedbymachinelearningalgorithmsforinterpretablemonitoringoflargescalesatellitenetworks |