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Enhanced Accurate Indoor Localization System Using RSSI Fingerprint Overlapping Method in Sensor Network

센서네트워크에서 무선 신호세기 Fingerprint 중첩 방식을 적용한 정밀도 개선 실내 위치인식 시스템

  • 조형곤 (경북대학교 전자전기컴퓨터학부 실시간시스템 연구실) ;
  • 정설영 (경북대학교 전자전기컴퓨터학부 실시간시스템 연구실) ;
  • 강순주 (경북대학교 전자전기컴퓨터학부 실시간시스템 연구실)
  • Received : 2011.10.17
  • Accepted : 2012.07.05
  • Published : 2012.08.31

Abstract

To offer indoor location-aware services, the needs for efficient and accurate indoor localization system has been increased. In order to meet these requirement, we presented the BLIDx(Bidirectional Location ID exchange) protocol that is efficient localization system based on sensor network. The BLIDx protocol can cope with numerous mobile nodes simultaneously but the precision of the localization is too coarse because that uses cell based localization method. In this paper, in order to compensate for these disadvantage, we propose the fingerprint overlapping method by modifying a fingerprinting methods in WLAN, and localization system using proposed method was designed and implemented. Our experiments show that the proposed method is more accurate and robust to noise than fingerprinting method in WLAN. In this way, it was improved that low location precision of BLIDx protocol.

최근 실내 위치인식 서비스를 제공하기 위해 효율적이면서도 정밀한 실내 위치인식 시스템에 대한 요구가 증가하고 있다. 이러한 요구를 만족하기 위해 본 연구팀은 센서네트워크 기반의 위치인식 방법인 BLIDx(Bidirectional Location ID exchange) 프로토콜을 제안하였다. 하지만 BLIDx 프로토콜은 동시에 수많은 이동노드에 대해 신속한 위치인식을 할 수 있으나 셀 기반 위치인식을 사용하기 때문에 위치 정밀도가 낮은 단점이 있었다. 본 논문은 이러한 단점을 보완하기 위해 WLAN의 fingerprinting 방법을 변형한 fingerprint 중첩 방법을 제안하고, 제안한 방법을 사용한 위치인식 시스템을 설계 및 구현하였다. 성능평가 결과 제안된 위치인식 방법은 기존 fingerprinting 방법보다 정확도 및 오차 견고성이 높게 나타났다. 이러한 방법을 통해 BLIDx의 낮은 위치 정밀도를 개선하였다.

Keywords

References

  1. Y. Gu, A. Lo, and I. Niemegeers, "A survey of indoor positioning systems for wireless personal networks," IEEE Communications Surveys &Tutorials, vol. 11, no. 1, pp. 13-32, 2009. https://doi.org/10.1109/SURV.2009.090103
  2. G. MAO, B. FIDAN, and B. ANDERSON, "Wireless sensor network localization techniques," Computer Networks, vol. 51, no. 10, pp. 2529-2553, Jul. 2007. https://doi.org/10.1016/j.comnet.2006.11.018
  3. H. Liu, H. Darabi, P. Banerjee, and J. Liu, "Survey of Wireless Indoor Positioning Techniques and Systems," IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067-1080, Nov. 2007. https://doi.org/10.1109/TSMCC.2007.905750
  4. H. S. Ahn and W. Yu, "Indoor localization techniques based on wireless sensor networks," Mobile Robots-State of the Art in Land, Sea, Air, and Collaborative Missions, InTech, 2009, pp. 277-302.
  5. V. Kaseva, T. D. Hamalainen, and M. Hannikainen, "A Wireless Sensor Network for Hospital Security: From User Requirements to Pilot Deployment," EURASIP Journal on Wireless Communications and Networking, vol. 2011, no. ii, pp. 1-15, 2011.
  6. D.K. Lee, T.H. Kim, S.Y. Jeong, and S.J. Kang, "A three-tier middleware architecture supporting bidirectional location tracking of numerous mobile nodes under legacy WSN environment," Journal of Systems Architecture, vol. 57, no. 8, pp. 735-748, Sep. 2011. https://doi.org/10.1016/j.sysarc.2011.05.004
  7. M. Saxena, P. Gupta, and B. N. Jain, "Experimental analysis of RSSI-based location estimation in wireless sensor networks," 3rd International Conference on Communication Systems Software and Middleware and Workshops, 2008, pp. 503-510.
  8. P. Bahl and V. N. Padmanabhan, "RADAR: an in-building RF-based user location and tracking system," in Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775-784, Tel Aviv.Israel, March 2000.
  9. Y.C. Cheng, Y. Chawathe, A. LaMarca, and J. Krumm, "Accuracy characterization for metropolitan-scale Wi-Fi localization," Proceedings of the 3rd international conference on Mobile systems, applications, and services, p. 233, 2005.
  10. T. King, S. Kopf, T. Haenselmann, and C. Lubberger, "COMPASS : A Probabilistic Indoor Positioning System Based on 802 . 11 and Digital Compasses," Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization, pp. 34-40, 2006.
  11. Q. Yao, F.Y Wang, H. Gao, K. Wang, and H. Zhao, "Location estimation in ZigBee Network based on fingerprinting," in 2007 IEEE International Conference on Vehicular Electronics and Safety, 2007, pp. 1-6.
  12. J. V. M. Aviles and R. M. Prades, "Pattern Recognition Comparative Analysis Applied to Fingerprint Indoor Mobile Sensors Localization," 10th IEEE International Conference on Computer and Information Technology, no. Cit, pp. 730-736, Jun. 2010.
  13. A. Parameswaran, M. I. Husain, and S. Upadhyaya, "Is rssi a reliable parameter in sensor localization algorithms: An experimental study," Field Failure Data Analysis Workshop, New York, 2009.

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