Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

FingerPrint building method using Splite-tree based on Indoor Environment

실내 환경에서 WLAN 기반의 Splite-tree를 이용한 가상의 핑거 프린트 구축 기법

  • Received : 2012.12.29
  • Accepted : 2012.04.15
  • Published : 2012.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

A recent advance in smart phones is increasing utilization of location information. Existing positioning system was using GPS location for positioning. However, the GPS cannot be used indoors, if GPS location has an incorrectly problem. In order to solve indoor positioning problems of indoor location-based positioning techniques have been investigated. There are a variety of techniques based on indoor positioning techniques like as RFID, UWB, WLAN, etc. But WLAN location positioning techniques take advantage the bond in real life. WLAN indoor positioning techniques have a two kind of method that is centroid and fingerprint method. Among them, the fingerprint technique is commonly used because of the high accuracy. In order to use fingerprinting techniques make a WLAN signal map building that is need to lot of resource. In this paper, we try to solve this problem in an Indoor environment for WLAN-based fingerprint of a virtual building technique, which is proposed. Proposed technique is classified Cell environment in existed Indoor environment, all of fingerprint points are shown virtual grid map in each Cell. Its method can make fingerprint grid map very quickly using estimate virtual signal value. Also built signal value can take different value depending of the real estimate value. To solve this problem using a calibration technique for the Splite-tree is proposed. Through calibration technique that improves the accuracy for short period of time. It also is improved overall accuracy using predicted value of around position in cell.

최근 스마트 폰의 발전으로 위치 정보의 활용도가 높아지고 있다. 기존에 측위 시스템은 GPS를 이용한 위치 측위를 하였다. 하지만 실내는 GPS 신호를 사용할 수 없으며, GPS 사용할 경우 위치 부정확의 문제점이 발생된다. 실내에서의 측위 문제를 해결하기 위하여 실내를 기반으로 하는 위치 측위 기법이 연구되었다. 실내를 기반으로 하는 많은 측위 기법으로는 RFID, UWB, WLAN 등의 다양한 기법이 존재한다. 하지만 WLAN를 이용한 위치 측위기법이 실생활에 활용하기 가장 접합하다. WLAN을 이용한 실내 측위 기법은 크게 2가지로 나뉘는데 첫째는 삼각 측량 기법이고, 둘째는 핑거프린트 기법이다. 그중 핑거 프린트 기법이 정확도가 높기 때문에 많이 쓰인다. 하지만 핑거 프린트 기법을 사용하기 위해서 구축하는 시간이나 구축을 위한 자원 소모가 많은 단점이 있다. 본 논문에서는 이러한 문제점을 해결하기 위한 실내 환경에서 WLAN 기반의 가상의 핑거 프린트 구축 기법을 제안한다. 제안된 기법은 기존의 실내 환경을 셀 환경으로 구분하였으며, 각각의 셀 안의 핑거 프린트 위치 점을 가상의 그리드 맵으로 나타내었다. 그리고 가상의 위치점에 예측된 가상의 시그널 값을 넣어줌으로써 빠르게 핑거프린트 시스템을 구축한다. 또한 이렇게 구축된 시그널 값들은 실측위 값과는 다른 값을 가질수 있기 때문에 이러한 문제를 해결하기 위한 Splite-tree를 이용한 보정 기법을 제안한다. 보정 기법을 통하여 짧은 시간 동안 정확도가 향상됨을 보인다.

Keywords

References

  1. S. S. Shin, S. O, Kim, J. Y. Du, T. S, Kim and S. H. Kim, "The Development of an Indoor and Outdoor Navigation system," Proceedings of 17th ITS WorldCongress, Busan, 2010.
  2. J. Yin, Q. Yang, L. Ni, "Adaptive Temporal Radio Maps for Indoor Location Estimation," Proceedings of the 3rd IEEE Int'l Conf. on Pervasive Computing and Communications (PerCom 2005).
  3. C. Nerguizian, C. Despins, S. Affes, "Indoor Geolocation with Received Signal Strength Fingerprinting Technique and Neural Networks," ICT 2004, LNCS 3124, pp. 866-875, 2004.
  4. F. Barcelo-Arroyo et al, "Indoor Location for Safety Applications using Wireless Networks," in Proc of the 1st ERCIM Workshop on Mobility, Portugal, 2007.
  5. T. G. Kim, S. S. Shin, W. I. C, H. Y. B, "Effective indexing of moving objects for current position management in Road Networks," The korea society of computer information, 2011, 08. https://doi.org/10.9708/jksci.2011.16.10.033
  6. H. M. Khoury, V. R. Kamat, "Evaluation of position tracking technologies for user localization in indoor construction environments," Automation in Construction 18 (2009), pp. 444-457. https://doi.org/10.1016/j.autcon.2008.10.011
  7. H. Lu, B. Yang, C. S. Jensen, "Spatio-Temporal Joins on Symbolic Indoor Tracking Data," 27th IEEE International Conference on Data Engineering(ICDE), pp. 816-827, 2011.
  8. C. S. Jensen, H. Lu, and B. Yang, "Graph Model Based Indoor Tracking," Mobile Data Management: Systems, Services and Middleware, 2009. MDM '09. Tenth International Conference pp. 122-131, 18-20 May 2009.
  9. U. Grossmann, M. Schauch, and S. Hakobyan. "Rssi based Wlan indoor positioning with personal digital assistants," pp. 653-656, Sep. 2007.
  10. Y. Wang, X. Jia and H. K. Lee "An indoors wireless positioning system based on wireless local area network infrastructure," in 6th International Symposium on Satellite Navigation Technology including Mobile Positioning and Location Services, Melboume, July 2003.
  11. D Hongbin and J Yunde, "A robot of roaming the planet localization algorithm based on wireless sensor network," Beijing Institute of Technology, Robot, 2007.
  12. C Weike, "wireless sensor network localization weighted centroid algorithm based RSSI," Journal of Wuhan University of Technology, 2006.
  13. Bahl, P and Padmanabhan, V. N., "RADAR: An in-building RF-based user location and tracking system," Proc. IEEE INFOCOM 2000.
  14. P. Prasithsangaree, P. Krishnamurthy and P. K. Chrysanthis, "ON INDOOR POSITION LOCATION WITH WIRELESS LANS," University of Pittsburgh, USA, IEEE PIMRC, 2002.
  15. H Yan, H Hanying, and Z Shan, "A new TOA location algorithm," Zhen Zhou Information Engineering University, Radio Communications Technology, 2004.
  16. L Wei and C Chuanfeng, "Triangle centroid of wireless sensor network localization algorithm based on RSSI," Fu Zhou University, Sensor Technology, 2008.
  17. S Yun Cho, "Localization of the Arbitrary Deployed APs for Indoor Wireless Location-Based Applications," IEEE Transactions on Consumer Electronics, 2010.
  18. K Jones and L Liu, "What Where Wi: An Analysis of Millions of Wi-Fi Access Points," Portable Information Devices, 2007. PORTABLE07. IEEE International Conference on, pp. 25-29, 2007.
  19. R Battiti, "Neural network models for intelligent networks : deriving the location from signal patterns," University Trento, Poceedings of The First Annual Symposium on Autonomous Intelligent Networks and Systems UCLA2002.
  20. P Myllymaki and H Tirri, "A Probabilistic Approach to WLAN User Location Estimation," University of Helsinki, International Journal of Wireless Information Networks, July 2002.
  21. A Moustafa and Youssef, "WLAN Location Determination via Clustering and Probability Distributions," University of Maryland, USA, Proceedings of the First IEEE International Conference on Pervasive Computing and Communication, 2003.

Cited by

  1. Indoor localization algorithm based on WLAN using modified database and selective operation vol.37, pp.8, 2013, https://doi.org/10.5916/jkosme.2013.37.8.932
  2. Grid-based Semantic Cloaking Method for Continuous Moving Object Anonymization vol.18, pp.3, 2012, https://doi.org/10.9708/jksci.2013.18.3.047