Abstract
In this study, the indoor localization was performed on indoor networks. WiFi technology is located in almost every building. For this reason, WiFi technology has been selected to perform positioning, and RSSI values from WiFi technology access points have been examined. For this purpose, RFKON_MB_WIFI dataset in RFKON database which is a sample database is used and data of 18480 signal strength are analyzed. The Fingerprinting method of Scene Analysis methods is used to perform the localization process. As a first step, the signal strengths in the data set are normalized by preprocessing. In the second step, positioning was performed using SVM, PCA, LDA, KNN, N3, BNN, Naive Bayes Classification, and Deep Learning methods. When the results obtained are compared, the most successful result is obtained from deep learning that is known to have a high accuracy on big data with an accuracy of 95.95%.
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Acknowledgements
This work is also a part of the Ph.D. thesis titled “Mobility Management for Internet of Things” at Istanbul University, Institute of Physical Sciences.
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Turgut, Z., Üstebay, S., Zeynep Gürkaş Aydın, G., Sertbaş, A. (2019). Deep Learning in Indoor Localization Using WiFi. In: Boyaci, A., Ekti, A., Aydin, M., Yarkan, S. (eds) International Telecommunications Conference. Lecture Notes in Electrical Engineering, vol 504. Springer, Singapore. https://doi.org/10.1007/978-981-13-0408-8_9
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DOI: https://doi.org/10.1007/978-981-13-0408-8_9
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