A Comparative Analysis of N-Nearest Neighbors (N3) and Binned Nearest Neighbors (BNN) Algorithms for Indoor Localization
| Autor: | Serpil Üstebay, Tulin Atmaca, Ahmet Sertbas, M. Ali Aydin |
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| Přispěvatelé: | Department of Computer Engineering [Istanbul], Okan University, Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Département Réseaux et Services de Télécommunications (RST), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Transmitter Fingerprint (computing) Fingerprint 020302 automobile design & engineering 02 engineering and technology Signal k-nearest neighbors algorithm Power (physics) N3 and BNN algorithms [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Statistical classification 0203 mechanical engineering Position (vector) Received signal strength indication 0202 electrical engineering electronic engineering information engineering RSSI 020201 artificial intelligence & image processing Indoor localization Algorithm Accuracy |
| Zdroj: | Computer Networks ISBN: 9783319597669 CN Proceedings CN 2017 : 24th international conference on Computer Networks CN 2017 : 24th international conference on Computer Networks CN 2017 : 24th international conference on Computer Networks, Jun 2017, Lądek Zdrój, Poland. pp.81-90, ⟨10.1007/978-3-319-59767-6_7⟩ |
| DOI: | 10.1007/978-3-319-59767-6_7 |
| Popis: | International audience; In this study, performance 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 methode, the RSSI information is catured 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 Neighbor(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 Neighbor(BNN)algorithms are presented comparatively with respect to success of finding user position |
| Databáze: | OpenAIRE |
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