DOI QR코드

DOI QR Code

Ontology-based Positioning Systems for Indoor LBS

온톨로지 기반의 실내 LBS를 위한 위치 추적 시스템

Hwang, Chi-Gon;Yoon, Chang-Pyo
황치곤;윤창표

  • Received : 2016.05.22
  • Accepted : 2016.06.08
  • Published : 2016.06.30

Abstract

Recently BLE beacon has been widely used as a method for measuring the indoor location in the IoT Technique. But it requires a filtering technique for the measurement of the correct position. It is used the most fixed beacon. It is not accurate that calculates the position information through the identification of the beacon signal. Therefore, filtering is important. So it takes a lot of time, position measurement and filtering. Thus, we is to measure the exact position at the indoor using a mobile beacon. The measured beacon signal is composed of an ontology for reuse in the same pattern. RSSI is measured the receiver is the distance of the beacon. And this value configure the location ontology to be normalized by the relationship analysis between the values. The ontology is a method for calculating the position information of the moving beacon. It can detect fast and accurate indoor position information and provide the service.

Keywords

LBS(Location based Service);iBeacon;BLE(Bluetooth Low Energy);Ontology;Indoor Positioning;RSSI(Receive Signal Strength Indication)

References

  1. Szymon Bobek, Olgierd Grodzki and Grzegorz J. Nalepa, "Indoor Microlocation with BLE Beacons and Incremental Rule Learning," In Proceeding of 2013 IEEE International Conference on Cybernetics (CYBCONF 2015), pp.91-96, 2015.
  2. iOS Developer Library(2016, March). Region Monitoring and iBeacon[Internet]. Available: https://developer.apple. com/library/ios/documentation/UserExperience/Conceptual/LocationAwarenessPG/RegionMonitoring/RegionMonitoring.html.
  3. B. Anja, "Bluetooth indoor positioning," M,S. thesis, University of Geneva, 2012.
  4. J. J. Yoo, Y. S. Cho, "Trends in Technical Development and Standardization of Indoor Location Based Services", ETRI 2014 Electronics and Telecommunications Trends, vol. 29, no. 5, pp.51-61, Oct. 2014.
  5. I. Oksar, "A Bluetooth signal strength based indoor localization method," in Proceeding of the 21st International Conference on Systems, Signals and Image Processing, IEEE, Dubrovnik, pp. 251-254, 2014.
  6. Bluetooth specification, [Internet], Available: http://www.bluetooth.com.
  7. Klusch, Matthias, Benedikt Fries, Katia Sycara. "OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services," Web Semantics: Science, Services and Agents on the World Wide Web. Vol. 7, No. 2, pp.121-133, Apr. 2009. https://doi.org/10.1016/j.websem.2008.10.001
  8. S. H. Halder and W. J. Kim, "A fusion approach of RSSI and LQI for indoor localization system using adaptive smoothers," Journal of Computer Networks and Communications, vol. 2012, pp.1-10, Aug. 2012.

Cited by

  1. Random forest and WiFi fingerprint-based indoor location recognition system using smart watch vol.9, pp.1, 2019, https://doi.org/10.1186/s13673-019-0168-7