Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2017

10.05.2017

An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

verfasst von: Changgeng Li, Zhengyang Qiu, Changtong Liu

Erschienen in: Wireless Personal Communications | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The weighted K-nearest neighbor algorithm (WKNN) is widely used in indoor positioning based on Wi-Fi. However, the accuracy of this traditional algorithm using Euclidean distance is not high enough due to the ignorance of statistical regularities from the training set. In this paper, the Manhattan distance is introduced to the WKNN algorithm to distinguish the influence of different reference nodes. Simultaneously, a new method is proposed to increase the accuracy of the algorithm by adjusting the weight of adjacent reference nodes. The simulation and experiment results show that the improved algorithm can have a better performance by increasing the accuracy by 33.82%.
Vorheriger Artikel Evaluation of Electromagnetic Interference in Wireless Broadband Systems
Nächster Artikel An Efficient Proposed MAC Protocol for M2M Networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Shim, Y. (2012). A study on mobile tour information application using LBS (location based service). Journal of Communication Design, 41, 187–195.
2.
Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, Cybernetics, Systems Part C—Applications and Reviews, 37(6), 1067–1080.CrossRef
3.
Hernandez, A., Badorrey, R., Choliz, J., Alastruey, I., & Valdovinos, A. (2008). Accurate indoor wireless location with IR UWB systems a performance evaluation of joint receiver structures and TOA based mechanism. IEEE Transactions on Consumer Electronics, 54(2), 381–389.CrossRef
4.
Alavi, B., & Pahlavan, K. (2006). Modeling of the TOA-based distance measurement error using UWB indoor radio measurements. IEEE Communications Letters, 10(4), 275–277.CrossRef
5.
Sharp, I., & Yu, K. G. (2014). Indoor TOA error measurement, modeling, and analysis. IEEE Transactions on Instrumentation and Measurement, 63(9), 2129–2144.CrossRef
6.
Taponecco, L., D’Amico, A. A., & Mengali, U. (2011). Joint TOA and AOA estimation for UWB localization applications. IEEE Transactions on Wireless Communications, 10(7), 2207–2217.CrossRef
7.
Dai, L. L., Wang, Z. C., Wang, J., & Yang, Z. X. (2010). Positioning with OFDM signals for the next-generation GNSS. IEEE Transactions Consumer Electronics, 56(2), 374–379.CrossRef
8.
Liao, S. H., Chui, C. C., Ho, M. H., & Lin, C. H. (2012). Optimal relay antenna location in indoor environment using particle swarm optimizer and genetic algorithm. Wireless Personal Communications, 62, 599–615.CrossRef
9.
Montaser, A., & Moselhi, O. (2014). RFID indoor location identification for construction projects. Automation in Construction, 39, 167–179.CrossRef
10.
Gu, Y. Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. Communication Surveys Tutorials, 11(1), 13–32.CrossRef
11.
Kong, Y., Kwon, Y., & Park, G. (2009). Robust localization over obstructed interferences for inbuilding wireless applications. IEEE Transactions on Consumer Electronics, 55(1), 105–111.CrossRef
12.
Chen, L., Pei, L., Kuusniemi, H., Chen, Y., Kröger, T., & Chen, R. (2013). Bayesian fusion for indoor positioning using bluetooth fingerprints. Wireless Personal Communications, 40, 1735–1745.CrossRef
13.
Yang, Z., Wu, C., & Liu, Y. (2012). Locating in fingerprint space: Wireless indoor localization with little human intervention. In Annual international conference on mobile computing and networking (pp. 269–280), Istanbul, Turkey.
14.
Zhou, M., Xu, Y. B., & Ma, L. (2010). Radio-map establishment based on fuzzy clustering for WLAN hybrid KNN/ANN indoor positioning. China Communications, 7(3), 64–80.
15.
Oussalah, M., Alakhras, M., & Hussein, M. I. (2015). Multivariable fuzzy inference system for fingerprinting indoor localization. Fuzzy Sets and Systems, 269, 65–89.MathSciNetCrossRef
16.
Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.MATH
17.
Wu, X. D., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.CrossRef
18.
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.CrossRefMATH
19.
Gholoobi, A., & Stavrou, S. (2015). RSS based localization using a new WKNN approach. In 2015 7th international conference on computational intelligence, communication systems and networks (CICSyN) (pp. 27–30). IEEE.
20.
Li, D., Zhang, B., Yao, Z., & Li, C. (2014). A feature scaling based k-nearest neighbor algorithm for indoor positioning system. In 2014 IEEE global communications conference (pp. 436–441). IEEE.
21.
Wang, Y., Yang, Q., Zhang, G., & Zhang, P. (2016). Indoor positioning system using Euclidean distance correction algorithm with bluetooth low energy beacon. In International conference on internet of things and applications (IOTA) (pp. 243–247). IEEE.
22.
Yang, L., Xu, Z., Yufei, J., Yi, H. & Eng Gee, L. (2015). Energy-efficient positioning for cellular networks with unknown path loss exponent. In IEEE international conference on consumer electronics (pp. 502–503), Taiwan, China.
23.
Andersen, J. B., Rappaport, T. S., & Yoshida, S. (1995). Propagation measurements and models for wireless communications channels. IEEE Communications Magazine, 33(1), 42–49.CrossRef
24.
Keenan, J. M., & Motley, A. J. (1990). Radio coverage in building. British Telecom Technology Journal, 8(1), 19–24.
25.
Beomju, S., Ho, L. J., Taikjin, L. & Seok, K. H. (2012). Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems. In International conference on computing technology and information management (pp. 574–577), Seoul, Korea.
Metadaten
Titel
An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning
verfasst von
Changgeng Li
Zhengyang Qiu
Changtong Liu
Publikationsdatum
10.05.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4295-z

Weitere Artikel der Ausgabe 2/2017

Wireless Personal Communications 2/2017 Zur Ausgabe

OriginalPaper

A New Mobility Management Scheme for Intelligent Transportation Systems

OriginalPaper

CE-OFDM Schemes: Spectral Characterization and Optimum Performance

OriginalPaper

ACO and PSO Algorithms for Developing a New Communication Model for VANET Applications in Smart Cities

OriginalPaper

An Efficient Proposed MAC Protocol for M2M Networks

OriginalPaper

A Novel Scalable Key Pre-distribution Scheme for Wireless Sensor Networks Based on Residual Design

OriginalPaper

Blind CFO Estimations Based on Subspace-Based Approaches Under Single Data Block for Interleaved OFDMA Uplink

Neuer Inhalt

    Bildnachweise