DOI QR코드

DOI QR Code

A Stable Access Point Selection Method Considering RSSI Variation in Fingerprinting for Indoor Positioning

실내측위를 위한 핑거프린팅에서의 RSSI 변동을 고려한 안정된 AP 선출방법

  • 황동엽 (아주대학교 컴퓨터공학과) ;
  • 김강석 (아주대학교 사이버보안학과)
  • Received : 2017.04.07
  • Accepted : 2017.07.05
  • Published : 2017.09.30

Abstract

Recently, an RSSI-based fingerprinting localization technology has been widely used in indoor location-based services. In the conventional fingerprinting method, as many APs as possible are used to increase the accuracy of location estimation. In another study, a part of APs having the strongest RSSI signal intensity are selected and used to reduce the time spent for positioning. However, it does not reflect the influence of RSSI occurred from the changes of the surrounding environments such as human movement or moving obstacles in a real environment. The environmental changes may cause the difference between the predicted RSSI signal strength value and the measured value, and thus occur an unpredictable error in the position estimation. Therefore, in order to mitigate the error caused by environmental factors, it is necessary to select APs suitable for indoor positioning estimation considering the changes in the surrounding environments. In this paper, we propose a method to select stable APs considering the influence of surrounding environments and derive a suitable positioning algorithm. In addition, we compare and analyze the performance of the proposed method with that of the existing AP selection methods through experiments.

최근, RSSI 기반의 핑거프린팅 위치추정 기술은 실내 위치 기반 서비스에 널리 사용되고 있다. 기존의 핑거프린팅 기법에서는 위치추정 정확도를 높이기 위해 수신 되는 최대한 많은 수의 AP들을 사용하거나, 측위에 소요되는 시간을 줄이기 위해 RSSI 신호의 세기가 가장 강한 일부의 AP만을 선택하여 사용하였다. 그러나 실제 환경에서 발생하는 사람의 움직임이나 이동하는 장애물등의 주변 환경 변화에 따른 RSSI의 오차가 반영되지 않았다. 이러한 환경변화는 RSSI 신호 세기의 예측값과 실측값과의 차이를 발생시키는 원인이 되며, 위치추정에 예측할 수 없는 오차를 발생시키게 된다. 따라서, 환경요인에 의한 오차 발생을 완화하기 위해서 주변 환경 변화를 고려하여 실내 측위에 적절한 AP들을 선택하여 사용하는 기술이 필요하다. 본 논문에서는 주변 환경의 영향을 고려하여 안정적인 AP만을 선택하는 방식과 이에 적합한 측위 알고리즘을 제안한다. 또한 실험을 통하여 제안한 방식과 기존의 AP 선출 방식의 성능을 비교 분석하였다.

Keywords

References

  1. S. He and S. H. G. Chan, "Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons," IEEE Communications Surveys and Tutorials, Vol.18, No.1, pp.466-490, 2016. https://doi.org/10.1109/COMST.2015.2464084
  2. X. Li, J. Wang, C. Liu, L. Zhang, and Z. Li, "Integrated wifi/pdr/smartphone using an adaptive system noise extended kalman filter algorithm for indoor localization," ISPRS International Journal of Geo-Information, Vol.5, No.2, pp.8, 2016. http://dx.doi.org/10.3390/ijgi5020008
  3. P. Jiang, Y. Zhang, W. Fu, H. Liu, and X. Su, "Indoor mobile localization based on wi-fi fingerprint's important access point," International Journal of Distributed Sensor Networks, Vol.11, 2015. http://dx.doi.org/10.1155/2015/429104
  4. K. S. Bok, Y. H. Park, and J. I. Pee, "Location acquisition method based on RFID in indoor environments," Computer Graphics and Broadcasting (MulGraB '11) in Proceedings of the International Conference on Multimedia, pp.310-18, 2011.
  5. S. Feldmann, K. Kyamakya, A. Zapater, Z. Lue, "An indoor Bluetooth-based positioning system: concept, implementation and experimental evaluation," International Conference on Wireless Networks, 2003.
  6. Song, Chunjing, Jian Wang, and Guan Yuan, "Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best- Discriminating AP Selection," ISPRS International Journal of Geo-Information, Vol.5, No.10, 2016.
  7. Sun, W., Liu, J., Wu, C., Yang, Z., Zhang, X., and Liu, Y., "MoLoc: On distinguishing fingerprint twins," IEEE 33rd International Conference on Distributed Computing Systems (ICDCS), pp.226-235. 2013.
  8. Z. Xiao, H. Wen, A. Markham, N. Trigoni, P. Blunsom, and J. Frolik, "Non-line-of-sight identification and mitigation using received signal strength," IEEE Transactions on Wireless Communications, Vol.14, No.3, pp.1689-1702, 2013. https://doi.org/10.1109/TWC.2014.2372341
  9. Y. Kim, H. Shin, Y. Chon, and H. Cha, "Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variation problem," Pervasive and Mobile Computing, Vol.9, No.3, pp.406-420, 2013. https://doi.org/10.1016/j.pmcj.2012.12.003
  10. C. Laoudias, R. Pich, and C. G. Panayiotou, "Device selfcalibration in location systems using signal strength histograms," Journal of Location Based Services, Vol.7, No.3, pp.165-181, 2013. https://doi.org/10.1080/17489725.2013.816792
  11. S. C. Yeh and Y. J. Peng, "Designing on indoor positioning system based on the WiFi fingerprinting mechanism," Technical Report in Ming Chung University, 2006.
  12. D. M. Hawkins, "The problem of overfitting," Journal of Chemical Information and Computer Sciences, Vol.44, No.1 pp.1-12. 2003. https://doi.org/10.1021/ci0342472
  13. Paramvir Bahl and Venkata N. Padmanabhan, "RADAR: An In-Building RF-based User Location and Tracking System," Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Vol.2, pp.775-784, 2000.
  14. O. Pathak, P. Palaskar, R. Palkar, and M. Tawari, "Wi-Fi Indoor Positioning System Based on RSSI Measurements from Wi-Fi Access Points-A Tri-lateration Approach," International Journal of Scientific & Engineering Research (IJSER), Vol.5, No.4, pp.1234-1238, 2014.
  15. F. Lassabe, P. Canalda, P. Chatonnay, F. Spies, and O. Baala, "A Friis-based calibrated model for WiFi terminals positioning," in Proceedings of the Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, pp.382-387, 2005.
  16. I. K. Eltahir, "The Impact of Different Radio Propagation Models for Mobile Ad hoc NETworks (MANET) in Urban Area Environment," in Proceedings of the The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, p.30, 2007.
  17. L. Kanaris, A. Kokkinis, G. Fortino, A. Liotta, and S. Stavrou, "Sample size determination algorithm for fingerprint-based indoor localization systems," Computer Networks, Vol.101 pp.169-177, 2016. https://doi.org/10.1016/j.comnet.2015.12.015.
  18. A. Zhang, Y. Yuan, Q. Wu, S. Zhu, and J. Deng, "Wireless localization based on RSSI fingerprint feature vector," International Journal of Distributed Sensor Networks, 2015.
  19. Matlab version 8.5.0 (r2015a): The Mathworks Inc., 2015. [Internet], http://www.mathworks.com.