DOI QR코드

DOI QR Code

Indoor Zone Recognition System using RSSI of BLE Beacon

BLE Beacons의 RSSI를 이용한 실내 Zone인식 시스템

  • Received : 2016.07.14
  • Accepted : 2016.10.05
  • Published : 2016.10.31

Abstract

Recently, indoor location detection has become an important area in the IoT (Internet of Things) environment for various indoor location-based services. In this paper, our proposed method shows that a virtual region can be divided electromagnetically according to specific facilities or services in various IoT application areas called zones. The MLP (Multi-Layer Perceptron) method is applied to recognize the service zone at the current position. The MLP utilized an RSSI (Received Signal Strength Indicator) signal of the BLE (Bluetooth Low Energy) Beacon as input data and made decisions to affiliate the zone of the current region as output. In order to verify the proposed method, we constructed an experimental environment similar in size to an actual rail station using four of the beacon and two zones.

최근 IoT환경에서 다양한 위치기반의 서비스의 확산으로 인해 실내측위는 중요한 영역으로 자리잡고 있다. 이에 본 논문에서는 특정 공간에 시설물, 서비스 등을 고려한 가상의 영역을 Zone으로 설정하였고, 다층퍼셉트론(MLP: Multi-Layer Perceptron)을 사용하여 Zone을 인식하는 방법을 제안하였다. 제안방법의 다층퍼셉트론은 입력으로 BLE(Bluetooth Low Energy) Beacon의 RSSI(Received Signal Strength Indicator)신호를 입력으로 활용하였고 현재 위치의 소속된 Zone을 출력하였다. 제안방법의 검증을 위해서 실제 역사와 유사한 크기의 실험환경을 구축하였으며 4개의 Beacon을 설치하였고 2개의 Zone영역을 설정하였다.

Keywords

References

  1. Liu, Hui, et al. (2007) Survey of wireless indoor positioning techniques and systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), pp. 1067-1080. https://doi.org/10.1109/TSMCC.2007.905750
  2. Gu, Yanying, Anthony Lo, and Ignas Niemegeers (2009) A survey of indoor positioning systems for wireless personal networks, IEEE Communications surveys & tutorials, 11(1), pp. 13-32. https://doi.org/10.1109/SURV.2009.090103
  3. Mok, Esmond, and Bernard KS Cheung (2013) An improved neural network training algorithm for Wi-Fi fingerprinting positioning, ISPRS International journal of geo-information, 2(3), pp. 854-868. https://doi.org/10.3390/ijgi2030854
  4. Zhuang, Yuan, et al. (2016) Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons, Sensors, 16(5), p. 596.
  5. Koyuncu, Hakan, and Shuang Hua Yang (2010) A survey of indoor positioning and object locating systems, IJCSNS International Journal of Computer Science and Network Security, 10(5), pp. 121-128.
  6. Kriz, Pavel, Filip Maly, and Tomas Kozel (2016) Improving Indoor Localization Using Bluetooth Low Energy Beacons, Mobile Information Systems 2016.
  7. N. Patwari, J.N. Ash, S. Kyperountas, A.O. Hero III, R.L. Moses, and N.S. Correal (2005) Locating the nodes: cooperative localization in wireless sensor networks, IEEE Signal Processing, Magazine, 22(4), pp. 54-69. https://doi.org/10.1109/MSP.2005.1458287
  8. X. Li, K. Pahlavan, M. Latva-aho, and M. Ylianttila (2000) Comparison of indoor geolocation methods in DSSS and OFDM wireless LAN systems, Proceedings of the 52nd Vehicular Technology Conference, Boston, pp. 3015-3020,
  9. R. Peng and M.L. Sichitiu (2006) Angle of arrival localization for wireless sensor networks, Proceedings of the 3rd Annual IEEE Communications Society on Sensor and AdHoc Communications and Networks(Secon '06), Reston, pp. 374-382,
  10. Jin-Woo Song, Soo-Jung Hur, Yong-Wan Park, Kook-Yeol Yoo (2012) Database Investigation Algorithm for High-Accuracy based Indoor Positioning , IEMEK Journal of Embedded Systems and Applications, 7(2), pp. 85-93. https://doi.org/10.14372/IEMEK.2012.7.2.085
  11. Guzman-Quiros, Raul, et al. (2015) Integration of directional antennas in an RSS fingerprinting-based indoor localization system, Sensors, 16(1), p. 4. https://doi.org/10.3390/s16010004
  12. Wu, Chenshu, et al. (2013) WILL: Wireless indoor localization without site survey, IEEE Transactions on Parallel and Distributed Systems, 24(4), pp. 839-848. https://doi.org/10.1109/TPDS.2012.179
  13. T.N. Lin, P.C. Lin (2005) Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks, Proceedings on WIRLES'05, 2, pp. 1469-1574
  14. Hinton, Geoffrey, et al. (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), pp. 82-97. https://doi.org/10.1109/MSP.2012.2205597
  15. Kjaergaard, Mikkel Baun, Georg Treu, and Claudia Linnhoff-Popien (2007) Zone-based RSS reporting for location fingerprinting, International Conference on Pervasive Computing, Springer Berlin Heidelberg, Pisa, pp. 316-333.
  16. Chauvin, Yves, and David E. Rumelhart. (1995) Backpropagation: theory, architectures, and applications. Psychology Press, London, pp. 23-30.