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An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning

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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%.

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References

  1. Shim, Y. (2012). A study on mobile tour information application using LBS (location based service). Journal of Communication Design, 41, 187–195.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  5. Sharp, I., & Yu, K. G. (2014). Indoor TOA error measurement, modeling, and analysis. IEEE Transactions on Instrumentation and Measurement, 63(9), 2129–2144.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  9. Montaser, A., & Moselhi, O. (2014). RFID indoor location identification for construction projects. Automation in Construction, 39, 167–179.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Google Scholar 

  15. Oussalah, M., Alakhras, M., & Hussein, M. I. (2015). Multivariable fuzzy inference system for fingerprinting indoor localization. Fuzzy Sets and Systems, 269, 65–89.

    Article  MathSciNet  Google Scholar 

  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  Google Scholar 

  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.

    Article  Google Scholar 

  18. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  24. Keenan, J. M., & Motley, A. J. (1990). Radio coverage in building. British Telecom Technology Journal, 8(1), 19–24.

    Google Scholar 

  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.

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Acknowledgements

This work was supported by Science and Technology Planning Project of Hunan Province, China (Project Number: 2015GK3003) and Postgraduate Innovation Project of Central South University (Project Number: 2016zzts232).

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Authors and Affiliations

  1. School of Physics and Electronics, Central South University, Middle Xiaoxiang Road, Changsha, China

    Changgeng Li, Zhengyang Qiu & Changtong Liu

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  1. Changgeng Li
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  2. Zhengyang Qiu
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  3. Changtong Liu
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Correspondence to Changgeng Li.

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Li, C., Qiu, Z. & Liu, C. An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning. Wireless Pers Commun 96, 2239–2251 (2017). https://doi.org/10.1007/s11277-017-4295-z

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  • DOI: https://doi.org/10.1007/s11277-017-4295-z

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