ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI

dc.contributor.authorUnlersen, Muhammed Fahri
dc.date.accessioned2024-02-23T14:35:08Z
dc.date.available2024-02-23T14:35:08Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractThe widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the path loss adapted, and exponential transformed) to train and test an artificial neural network (ANN). A systematic approach to the determination of neuron numbers in the hidden layers and activation functions of ANN is provided. The ANN is trained by the artificial bee colony algorithm. Five ML methods have been employed for estimation. The best performance has been achieved with ABC-ANN by the path loss adapted database with the MAE of 1.01 m. The estimation done using processed RSSI values has better performance than raw RSSI values. In addition, 33% less error occurs with the mentioned method compared to the data set source study.en_US
dc.identifier.doi10.3390/electronics11234054
dc.identifier.issn2079-9292
dc.identifier.issue23en_US
dc.identifier.scopus2-s2.0-85143729650en_US
dc.identifier.urihttps://doi.org/10.3390/electronics11234054
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15895
dc.identifier.volume11en_US
dc.identifier.wosWOS:000896039000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIndoor Position Estimationen_US
dc.subjectIpeen_US
dc.subjectArtificial Bee Colonyen_US
dc.subjectWi-Fi Rssien_US
dc.subjectNeural Networken_US
dc.titleABC-ANN Based Indoor Position Estimation Using Preprocessed RSSIen_US
dc.typeArticleen_US

Dosyalar