Optimizing indoor localization accuracy with neural network performance metrics and software-defined IEEE 802.11az Wi-Fi set-up

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dc.authorid0000-0003-0693-4114
dc.authorid0000-0002-4615-7914
dc.authorid0000-0003-0946-9561
dc.authorid0000-0002-6691-9779
dc.contributor.authorKouhalvandi, Lidaen_US
dc.contributor.authorAygün, Sercanen_US
dc.contributor.authorMatekovits, Ladislauen_US
dc.contributor.authorMiramirkhani, Farshaden_US
dc.date.accessioned2023-12-20T14:41:14Z
dc.date.available2023-12-20T14:41:14Z
dc.date.issued2023-10-28
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineeringen_US
dc.description.abstractAccurately classifying regions based on Wi-Fi signals can be a difficult task, especially when considering different frequency values. In this study, we aimed to improve the accuracy of indoor localization by developing a novel approach that does not rely on pre-trained models. To achieve this, fingerprints from the IEEE 802.11az standard were randomly selected, and the data samples were trained using parameterized station characteristics and neural network hyperparameters. The impact of each parameter on the localization accuracy was measured, and performance monitoring metrics such as F1-Measure and confusion matrix-based metrics were evaluated. Furthermore, the Thompson sampling (TS) algorithm was employed to determine the optimal parameters, which helped to achieve the best possible accuracy. The proposed approach demonstrated improved accuracy in region localization compared to conventional heuristic approaches which typically yield an accuracy range of 65% to 77%. The proposed approach achieved up to 80% accuracy in region localization and could be a promising solution for indoor localization in various settings.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKouhalvandi, L., Aygün, S., Matekovits, L. & Miramirkhani, F. (2023). Optimizing indoor localization accuracy with neural network performance metrics and software-defined IEEE 802.11az Wi-Fi set-up. Paper presented at the 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), 1-4. doi:10.1109/WINCOM59760.2023.10322984en_US
dc.identifier.doi10.1109/WINCOM59760.2023.10322984
dc.identifier.endpage4
dc.identifier.isbn9798350329674
dc.identifier.isbn9798350329681
dc.identifier.issn2769-9994
dc.identifier.issn2769-9986
dc.identifier.scopus2-s2.0-85179510490
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11729/5822
dc.identifier.urihttp://dx.doi.org/10.1109/WINCOM59760.2023.10322984
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMiramirkhani, Farshaden_US
dc.institutionauthorid0000-0002-6691-9779
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof10th International Conference on Wireless Networks and Mobile Communications (WINCOM)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural network (CNN)en_US
dc.subjectIEEE 802.11az Wi-Fi standarden_US
dc.subjectOptimizationen_US
dc.subjectThompson sampling (TS)en_US
dc.subjectHeuristic methodsen_US
dc.subjectIEEE standardsen_US
dc.subjectIndoor positioning systemsen_US
dc.subjectWi-Fien_US
dc.subjectWireless local area networks (WLAN)en_US
dc.subjectIndoor localizationen_US
dc.subjectLocalisationen_US
dc.subjectLocalization accuracyen_US
dc.subjectNeural-networksen_US
dc.titleOptimizing indoor localization accuracy with neural network performance metrics and software-defined IEEE 802.11az Wi-Fi set-upen_US
dc.typeConference Objecten_US

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