dc.contributor.author |
Jurado-Lasso, FF
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dc.contributor.author |
Marchegiani, L
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|
dc.contributor.author |
Jurado, JF
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.contributor.author |
Fafoutis, X
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dc.date.accessioned |
2022-11-06T19:21:17Z |
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dc.date.available |
2022-11-06T19:21:17Z |
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dc.date.issued |
2022-02 |
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dc.identifier.citation |
Jurado-Lasso, F., Marchegiani, L., Jurado, J., Abu Mahfouz, A.M. & Fafoutis, X. 2022. A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. <i>IEEE Access, 10.</i> http://hdl.handle.net/10204/12510 |
en_ZA |
dc.identifier.issn |
ML-SDWSNs |
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dc.identifier.uri |
DOI: 10.1109/ACCESS.2022.3153521
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|
dc.identifier.uri |
http://hdl.handle.net/10204/12510
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|
dc.description.abstract |
Wireless Sensor Network (WSN), which are enablers of the Internet of Things (IoT) technology, are typically used en-masse in widely physically distributed applications to monitor the dynamic conditions of the environment. They collect raw sensor data that is processed centralised. With the current traditional techniques of state-of-art WSN programmed for specific tasks, it is hard to react to any dynamic change in the conditions of the environment beyond the scope of the intended task. To solve this problem, a synergy between Software-Defined Networking (SDN) and WSN has been proposed. This paper aims to present the current status of Software-Defined Wireless Sensor Network (SDWSN) proposals and introduce the readers to the emerging research topic that combines Machine Learning (ML) and SDWSN concepts, also called ML-SDWSNs. ML-SDWSN grants an intelligent, centralised and resource-aware architecture to achieve improved network performance and solve the challenges currently found in the practical implementation of SDWSNs. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSN, mainly the current state-of-art, ML techniques, and open issues. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/9718334 |
en_US |
dc.source |
IEEE Access, 10 |
en_US |
dc.subject |
Wireless Sensor Networks |
en_US |
dc.subject |
WSNs |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Software-Defined Wireless Sensor Networks |
en_US |
dc.subject |
SDWSNs |
en_US |
dc.subject |
Machine Learning Software-Defined Wireless Sensor Networks |
en_US |
dc.subject |
ML-SDWSNs |
en_US |
dc.title |
A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
23560-23592 |
en_US |
dc.description.note |
This work is licensed under a Creative Commons Attribution 4.0 License. |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Jurado-Lasso, F., Marchegiani, L., Jurado, J., Abu Mahfouz, A. M., & Fafoutis, X. (2022). A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. <i>IEEE Access, 10</i>, http://hdl.handle.net/10204/12510 |
en_ZA |
dc.identifier.chicagocitation |
Jurado-Lasso, FF, L Marchegiani, JF Jurado, Adnan MI Abu Mahfouz, and X Fafoutis "A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges." <i>IEEE Access, 10</i> (2022) http://hdl.handle.net/10204/12510 |
en_ZA |
dc.identifier.vancouvercitation |
Jurado-Lasso F, Marchegiani L, Jurado J, Abu Mahfouz AM, Fafoutis X. A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges. IEEE Access, 10. 2022; http://hdl.handle.net/10204/12510. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Jurado-Lasso, FF
AU - Marchegiani, L
AU - Jurado, JF
AU - Abu Mahfouz, Adnan MI
AU - Fafoutis, X
AB - Wireless Sensor Network (WSN), which are enablers of the Internet of Things (IoT) technology, are typically used en-masse in widely physically distributed applications to monitor the dynamic conditions of the environment. They collect raw sensor data that is processed centralised. With the current traditional techniques of state-of-art WSN programmed for specific tasks, it is hard to react to any dynamic change in the conditions of the environment beyond the scope of the intended task. To solve this problem, a synergy between Software-Defined Networking (SDN) and WSN has been proposed. This paper aims to present the current status of Software-Defined Wireless Sensor Network (SDWSN) proposals and introduce the readers to the emerging research topic that combines Machine Learning (ML) and SDWSN concepts, also called ML-SDWSNs. ML-SDWSN grants an intelligent, centralised and resource-aware architecture to achieve improved network performance and solve the challenges currently found in the practical implementation of SDWSNs. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSN, mainly the current state-of-art, ML techniques, and open issues.
DA - 2022-02
DB - ResearchSpace
DP - CSIR
J1 - IEEE Access, 10
KW - Wireless Sensor Networks
KW - WSNs
KW - Internet of Things
KW - IoT
KW - Machine learning
KW - Software-Defined Wireless Sensor Networks
KW - SDWSNs
KW - Machine Learning Software-Defined Wireless Sensor Networks
KW - ML-SDWSNs
LK - https://researchspace.csir.co.za
PY - 2022
SM - ML-SDWSNs
T1 - A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges
TI - A survey on machine learning software-defined wireless sensor networks (ml-SDWSNS): Current status and major challenges
UR - http://hdl.handle.net/10204/12510
ER - |
en_ZA |
dc.identifier.worklist |
26133 |
en_US |