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dc.contributor.authorGuney, Selda
dc.contributor.authorErdogan, Alperen
dc.contributor.authorAktas, Melih
dc.contributor.authorErgun, Mert
dc.date.accessioned2020-12-31T11:34:41Z
dc.date.available2020-12-31T11:34:41Z
dc.date.issued2020
dc.identifier.isbn978-1-7281-6376-5en_US
dc.identifier.urihttp://hdl.handle.net/11727/5352
dc.description.abstractIndoor positioning is one of the major challenges for the future large-scale technologies. Nowadays, it has become an attractive research subject due to growing demands on it. Several algorithms and techniques have been developed over the decades. One of the most cost-effective technique is Wi-Fi-based positioning systems. This technique is infrastructure-free and able to use existing wireless access points in public or private areas. These systems aim to classify user's location according to pre-defined set of grids. However, Wi-Fi signals could be affected by interference, blockage of walls and multipath effect which increases error of classification. In this study Deep Neural Networks and conventional machine learning classifiers are utilized to classify 22 squared grids which represent locations. Five primary Wireless Access Points (WAPs) were mounted indoor environment and 177 secondary WAPs are observed by Wi-Fi module. Dataset was created with using five primary and 177 secondary WAPs. The performance of proposed method was tested using Deep Neural Networks and machine learning classifiers. The results show that Deep Neural Network present the best performance as compared to machine learning classifiers. 95.45% accuracy was achieved by using five primary WAPs and 97.27% accuracy was achieved by using five primary and 177 secondary WAPs together for Deep Neural Network.en_US
dc.description.sponsorshipBrno Univ Technol, Dept Telecommunicat; Budapest Univ Technol 7 Econ, Dept Telecommunicat & Media Informa; Czech Tech Univ Prague, Dept Telecommunicat Engn; Isik Univ, Dept Elect & Elect Engn; Istanbul Tech Univ, Elect & Commun Engn Dept; Josip Juraj Strossmayer Univ Osijek, Fac Elect Engn, Comp Sci & Informat Technol; Karadeniz Tech Univ, Dept Elect & Elect Engn; Natl Taiwan Univ Sci & Technol, Dept Elect & Comp Engn; Seikei Univ, Grad Sch, Fac Sci & Technol, Informat Networking Lab; Slovak Univ Technol Bratislava, Inst Multimedia Informat & Commun Technologies; Escola Univ Politecnica Mataro, Tecnocampus; Technical University of Sofia, Faculty of Telecommunications; Univ Paris 8, UFR MITSIC, Lab Informatique Avancee Saint Denis; Univ Politehnica Bucharest, Ctr Adv Res New Mat, Prod & Innovat Proc; Univ Ljubljana, Lab Telecommunicat; Univ Patras, Phys Dept; VSB Tech Univ Ostrava, Dept Telecommunicat; W Pomeranian Univ Technol, Fac Elect Engn; IEEE Reg 8; IEEE Italy Sect & Italy Sect SP Chapter, Italy Sect VT COM Joint Chapter; IEEE Czechoslovakia Sect; Sci Assoc Infocommunicat; IEEE Czechoslovakia Sect SP CAS COM Joint Chapteren_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIndoor Positioningen_US
dc.subjectDeep Neural Networksen_US
dc.subjectClassificationen_US
dc.subjectWi-Fien_US
dc.titleWi-Fi Based Indoor Positioning System with Using Deep Neural Networken_US
dc.typeProceedings Paperen_US
dc.relation.journal2020 43RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)en_US
dc.identifier.startpage225en_US
dc.identifier.endpage228en_US
dc.identifier.wos000577106400049en_US
dc.identifier.scopus2-s2.0-85090584586en_US


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