The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Indoor Localization Methodology Based on Smart Phone in Home Environment

스마트 폰 기반의 가정환경 내 사용자 공간 위치 예측 기법

  • Ahn, Daye (Hongik University, Dept. of Computer Engineering, Real-time System Lab.) ;
  • Ha, Rhan (Hongik University, Dept. of Computer Engineering, Real-time System Lab.)
  • Received : 2013.12.24
  • Accepted : 2014.04.04
  • Published : 2014.04.30
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Abstract

In ubiquitous environment, User's location information is very important to serve personalized service to user. Previous works consider only User's locations in the big buildings and assume APs are fixed. Normal home environment, However, is consists of small spaces. And the state of APs is highly fluid. Previous research has focused on indoor localization in the building where has stationary AP environment. However, in this paper, we propose as User's Location Predicting System that finds out a space where a user is located based on Wi-Fi Fingerprint approach in home environments. The results that conducted real home environments are using the system show more than 80% accuracy.

유비쿼터스 환경에서 실내 공간의 사용자 위치정보는 다양한 응용분야에서 사용자에 특화된 서비스를 제공하는데 필요한 필수적인 정보이기 때문에 매우 중요하다. 기존연구들은 규모가 큰 건물에서의 사용자 위치 예측만 고려하고 있고 실험 대상이 되는 공간에서 고정된 AP가 다수 존재한다고 가정한다. 그러나 일반 가정은 면적이 좁은 공간들로 구성되며 고정된 AP가 소수이고 변화가 유동적인 환경이다. 본 논문에서는 기존 연구들이 AP환경이 비교적 안정적인 큰 건물에서의 사용자 위치 예측에 집중한 것과 달리, 일반 가정환경에서 와이파이 핑거프린트 방식을 기반으로 하여 공간을 식별하고 사용자의 위치를 Room-level로 예측하는 사용자 공간 예측 시스템을 제안한다. 실제 가정에서 실험을 한 결과 제안하는 시스템이 모든 가정에서 평균 80%이상의 정확도로 사용자가 위치한 공간을 예측함을 알 수 있었다.

Keywords

References

  1. L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, "LANDMARC: Indoor location sensing using RFID," Wirel. Netw., vol. 10, no. 6, pp. 701-710, 2004. https://doi.org/10.1023/B:WINE.0000044029.06344.dd
  2. A. Athalye, V. Savic, M. Bolic and P. M. Djuric, "A novel semi-passive RFID system for indoor localization," Sensors J. IEEE, vol. 13, no. 2, pp. 528-537, Feb. 2013. https://doi.org/10.1109/JSEN.2012.2220344
  3. M. Minami, Y. Fukuju, K. Hirasawa, S. Yokoyama, M. Mizumachi, H. Morikawa, and T. Aoyama, "DOLPHIN: A practical approach for implementing a fully distributed indoor ultrasonic positioning system," UbiComp 2004, Ubiquitous Computing, pp. 347-365, 2004.
  4. R. Want, A. Hopper, V. Falcao, and J. Gibbons, "The active badge location system," ACM Trans. Inf. Syst. (TOIS), vol. 10, no. 1, pp. 91-102, 1992. https://doi.org/10.1145/128756.128759
  5. N. B. Priyantha, A. Chakraborty, and H. Balakrishnan, "The cricket location-support system," in Proc. Int. conf. Mobile comput. netw., ACM, pp. 32-43, 2000.
  6. S. Y. Seidel and T. S. Rappaport, "914 MHz path loss prediction models for indoor wireless communications in multifloored buildings," IEEE Trans. Antennas Propagation, vol. 40, no. 2, pp. 207-217, Feb. 1992. https://doi.org/10.1109/8.127405
  7. K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan, "Indoor localization without the pain," in Proc. Int. conf. Mobile comput. netw., ACM, pp. 173-194, 2010.
  8. P. Bahl and V. N. Padmanabhan, "RADAR: An in-building RF-based user location and tracking system," in Proc. IEEE INFOCOM 2000, Vol. 2, pp. 775-784, 2000.
  9. P. Bahl, V. N. Padmanabhan, and A. Balachandran, Enhancements to the RADAR user location and tracking system, Microsoft Research, 2000.
  10. M. Youssef and A. Agrawala, "The horus WLAN location determination system," in Proc. Int. conf. Mobile Syst., Appl., Serv., ACM, pp. 205-218, 2005.
  11. M. Azizyan, I. Constandache and R. R. Choudhury, "SurroundSense: Mobile phone localization via ambience Fingerprinting," in Proc. Int. Conf. Mobile Comput. Netw., ACM, pp. 261-272, 2009.
  12. A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, "Zee: Zero-effort crowdsourcing for indoor localization," in Proc. Int. Conf. Mobile Comput. Netw., ACM, pp. 293-304, 2012
  13. O. B. Kwon and K. S. Kim, "The design and implementation of location information system using wireless fidelity in indoors," J. Digital Policy & Management, vol. 11, no. 4, pp. 243-249, Apr. 2013. https://doi.org/10.14400/JDPM.2013.11.11.243
  14. H. Jeon, N. Kim, and H. Park, "A study on effective location determination system in indoor environment," J. KICS, vol. 34, no. 2, pp. 119-129, Feb. 2009.
  15. J. Oh, "3D indoor postioning system based on smartphone," J. KICS, vol. 38C, no. 12, pp. 1126-1133, Dec. 2013.
  16. H. Kim, J. Bae, and J. Choi, "Wireless LAN based indoor postioning using received signal fingerprint and propagation prediction model," J. KICS, vol. 38A, no. 12, pp. 1021-1029, Dec. 2013. https://doi.org/10.7840/kics.2013.38A.12.1021
  17. Y. Jiang, X. Pan, K. Li, Q. Lv, R. P. Dick, M. Hannigan and L. Shang, "ARIEL: Automatic wi-fi based room fingerprinting for indoor localization," in ACM Ubicomp, pp. 441-450, Sept. 2012.
  18. C. Wu, Z. Yang, Y. Liu, and W. Xi, "WILL: Wireless indoor localization without site survey," in Proc. IEEE INFOCOM, pp. 64-72, Mar. 2012.
  19. Z. Yang, C. Wu and Y. Liu, "Locating in Fingerprint space: Wireless indoor localization with little human intervention," in Proc. Int. Conf. Mobile Comput. Netw., ACM,, pp. 269-280, Aug. 2012.
  20. Weka 3: Data mMining Software in Java, Retrieved Dec. 1, 2013, from http://www.cs.w aikato.ac.nz/ml/weka/
  21. Stanford Topic Modeling Toolbox Retrieved Dec., 1, 2013, from http://nlp.stanford.edu/soft ware/tmt/tmt-0.4/
  22. S. Geisser, Predictive inference: an introduction, CRC Press, 1993.
  23. T. T. Tanimoto, An elementary mathematical theory of classification and prediction, IBM Technical Report, 1958.

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