Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28483
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dc.contributor.authorSaeed, Ahmeden_UK
dc.contributor.authorKolberg, Marioen_UK
dc.date.accessioned2019-01-11T01:04:22Z-
dc.date.available2019-01-11T01:04:22Z-
dc.date.issued2018-12-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/28483-
dc.description.abstractEnergy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on Machine Learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed, Context-aware Listen Interval (CALI) power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behaviour and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. Comparative results show that more than 99% of accuracy can be achieved. Our study indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on levels of interaction in the background.en_UK
dc.language.isoenen_UK
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_UK
dc.relationSaeed A & Kolberg M (2018) Towards Optimising WLANs Power Saving: Novel Context-aware Network Traffic Classification Based on a Machine Learning Approach. IEEE Access, 7, pp. 3122-3135. https://doi.org/10.1109/access.2018.2888813.en_UK
dc.rightsCopyright 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.en_UK
dc.subject802.11en_UK
dc.subjectEnergy consumptionen_UK
dc.subjectmachine learning (ML)en_UK
dc.subjectPower Save Mode (PSM)en_UK
dc.subjecttraffic classificationen_UK
dc.subjectWLANen_UK
dc.titleTowards Optimising WLANs Power Saving: Novel Context-aware Network Traffic Classification Based on a Machine Learning Approachen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1109/access.2018.2888813en_UK
dc.citation.jtitleIEEE Accessen_UK
dc.citation.issn2169-3536en_UK
dc.citation.volume7en_UK
dc.citation.spage3122en_UK
dc.citation.epage3135en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.date19/12/2018en_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.identifier.isi000456058500001en_UK
dc.identifier.scopusid2-s2.0-85058903616en_UK
dc.identifier.wtid1082356en_UK
dc.contributor.orcid0000-0002-0930-2385en_UK
dc.date.accepted2018-12-09en_UK
dc.date.firstcompliantdepositdate2019-01-08en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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