Abstract
In this study, performances of classification algorithms N-Nearest Neighbors (N3) and Binned Nearest Neighbor (BNN) are analyzed in terms of indoor localizations. Fingerprint method which is based on Received Signal Strength Indication (RSSI) is taken into consideration. RSSI is a measurement of the power present in a received radio signal from transmitter. In this method, the RSSI information is captured at the reference points and recorded for creating a signal map. The obtained signal map is knows as fingerprint signal map and in the second stage of algorithm is creating a positioning model to detect individual’s position with the help of fingerprint signal map. In this work; N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are used to create an indoor positioning model. For this purpose; two different signal maps are used to test the algorithms. UJIIndoorLoc includes multi-building and multi floor signal information while different from this RFKON includes a single-building single floor signal information. N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) algorithms are presented comparatively with respect to success of finding user position.
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This work is also a part of the PhD thesis titled “Design of an Efficient User Localization System for Next Generation Wireless Networks” at Istanbul University, Institute of Physical Sciences.
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Ustebay, S., Aydin, M.A., Sertbas, A., Atmaca, T. (2017). A Comparative Analysis of N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) Algorithms for Indoor Localization. In: Gaj, P., Kwiecień, A., Sawicki, M. (eds) Computer Networks. CN 2017. Communications in Computer and Information Science, vol 718. Springer, Cham. https://doi.org/10.1007/978-3-319-59767-6_7
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