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Utilising artificial intelligence in software defined wireless sensor network

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dc.contributor.author Matlou, OG
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2018-03-16T08:28:33Z
dc.date.available 2018-03-16T08:28:33Z
dc.date.issued 2017-10
dc.identifier.citation Matlou, O.G. and Abu-Mahfouz, A.M.I. 2017. Utilising artificial intelligence in software defined wireless sensor network. The 43rd Annual Conference of the IEEE on Industrial Electronics Society, IECON 2017, 29 October to 1 November 2017, Beijing, China en_US
dc.identifier.isbn 978-1-5386-1127-2
dc.identifier.isbn 978-1-5386-1128-9
dc.identifier.uri http://ieeexplore.ieee.org/document/8217065/
dc.identifier.uri DOI: 10.1109/IECON.2017.8217065
dc.identifier.uri http://hdl.handle.net/10204/10115
dc.description Copyright: 2017 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published paper. For access to the published version, please consult the publisher's website. en_US
dc.description.abstract Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning play an important role in our society, give rise to systems that can manage themselves. WSNs have been used in various industrial applications, where reliability and network performance are critical success factors. Many advanced AI techniques can be utilised to improve the performance and reliability of these applications. Investigating the AI algorithms applied to SDN may bring improved network management, security or routing in SDWSN which may result in a more reliable network. We look at machine learning algorithms applied in SDN and discuss the possibility of using these AI in SDWSN to address the WSN challenges and improve its performance and reliability. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;20465
dc.subject Software Defined Wireless Sensor Network en_US
dc.subject SDWSN en_US
dc.subject Machine learning en_US
dc.subject Traffic management en_US
dc.subject Artificial intelligence en_US
dc.subject AI en_US
dc.title Utilising artificial intelligence in software defined wireless sensor network en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Matlou, O., & Abu-Mahfouz, A. M. (2017). Utilising artificial intelligence in software defined wireless sensor network. IEEE. http://hdl.handle.net/10204/10115 en_ZA
dc.identifier.chicagocitation Matlou, OG, and Adnan MI Abu-Mahfouz. "Utilising artificial intelligence in software defined wireless sensor network." (2017): http://hdl.handle.net/10204/10115 en_ZA
dc.identifier.vancouvercitation Matlou O, Abu-Mahfouz AM, Utilising artificial intelligence in software defined wireless sensor network; IEEE; 2017. http://hdl.handle.net/10204/10115 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Matlou, OG AU - Abu-Mahfouz, Adnan MI AB - Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning play an important role in our society, give rise to systems that can manage themselves. WSNs have been used in various industrial applications, where reliability and network performance are critical success factors. Many advanced AI techniques can be utilised to improve the performance and reliability of these applications. Investigating the AI algorithms applied to SDN may bring improved network management, security or routing in SDWSN which may result in a more reliable network. We look at machine learning algorithms applied in SDN and discuss the possibility of using these AI in SDWSN to address the WSN challenges and improve its performance and reliability. DA - 2017-10 DB - ResearchSpace DP - CSIR KW - Software Defined Wireless Sensor Network KW - SDWSN KW - Machine learning KW - Traffic management KW - Artificial intelligence KW - AI LK - https://researchspace.csir.co.za PY - 2017 SM - 978-1-5386-1127-2 SM - 978-1-5386-1128-9 T1 - Utilising artificial intelligence in software defined wireless sensor network TI - Utilising artificial intelligence in software defined wireless sensor network UR - http://hdl.handle.net/10204/10115 ER - en_ZA


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