KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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A Location Tracking System using BLE Beacon Exploiting a Double-Gaussian Filter

  • Lee, Jae Gu (Department of Computer Engineering, Smart Computing Lab, Hallym University) ;
  • Kim, Jin (Department of Computer Engineering, Smart Computing Lab, Hallym University) ;
  • Lee, Seon Woo (Department of Electronic Engineering, Hallym University) ;
  • Ko, Young Woong (Department of Computer Engineering, Smart Computing Lab, Hallym University)
  • Received : 2016.08.28
  • Accepted : 2017.01.06
  • Published : 2017.02.28
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Abstract

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

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References

  1. Seco, Fernando, "Improving RFID-based indoor positioning accuracy using Gaussian processes," in Proc. of Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on. IEEE, pp. 1-8, 2010.
  2. Jung, Ho Min, "ICLS: Intelligent cricket-based location tracking system using sensor fusion," in Proc. of Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD'08. Ninth ACIS International Conference on. IEEE, pp. 461-466, 2008.
  3. Choliz, Juan "Comparison of algorithms for uwb indoor location and tracking systems," in Proc. of Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, pp. 1-5, 2011.
  4. Gluhak and Alexander, "A survey on facilities for experimental internet of things research," IEEE Communications Magazine, vol. 49, no.11, pp. 58-67, 2011. https://doi.org/10.1109/MCOM.2011.6069710
  5. Kjaergaard and Mikkel Baun, "Indoor positioning using GPS revisited," in Proc. of International conference on pervasive computing, Springer Berlin Heidelberg, pp. 38-56, 2010.
  6. Yang, Jingjing, Zhihui Wang, and Xiao Zhang, "An iBeacon-based Indoor Positioning Systems for Hospitals," International Journal of Smart Home, vol. 9, no. 7, pp. 161-168, 2015.
  7. Martin, Paul, "An iBeacon primer for indoor localization: demo abstract," in Proc. of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, ACM, 2014.
  8. Newman, Nic. "Apple ibeacon technology briefing," Journal of Direct, Data and Digital Marketing Practice, vol. 15, no. 3, pp. 222-225, 2014. https://doi.org/10.1057/dddmp.2014.7
  9. Gomez, Carles, Joaquim Oller, and Josep Paradells, "Overview and evaluation of bluetooth low energy: An emerging low-power wireless technology," Sensors, vol. 12, no. 9, pp. 11734-11753, 2012. https://doi.org/10.3390/s120911734
  10. Zhao, Jizhong, "Localization of wireless sensor networks in the wild: Pursuit of ranging quality," IEEE/ACM Transactions on Networking (TON), vol. 21, no. 1, pp. 311-323, 2013. https://doi.org/10.1109/TNET.2012.2200906
  11. Cheng-Xu, Feng, and Liu Zhong, "A new node self-localization algorithm based RSSI for wireless sensor networks," in Proc. of Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on. IEEE, pp. 1616-1619, 2013.
  12. Shi, Hongyu, "A new weighted centroid localization algorithm based on RSSI," in Proc. of Information and Automation (ICIA), 2012 International Conference on. IEEE, pp. 1-4, 2012.
  13. Nick, Theresa, "Comparison of extended and unscented Kalman filter for localization of passive UHF RFID labels," in Proc. of General Assembly and Scientific Symposium, 2011 XXXth URSI. IEEE, 2011.
  14. Suarez, Alvaro, Kholoud Atalah Elbatsh, and Elsa Macias, "Gradient RSSI filter and predictor for wireless network algorithms and protocols," Network Protocols and Algorithms, vol. 2, no. 2, pp. 1-26, 2010.
  15. Raghavan, Aswin N., "Accurate mobile robot localization in indoor environments using Bluetooth," in Proc. of Robotics and Automation (ICRA), 2010 IEEE International Conference on. IEEE, pp.4391-4396, 2010.
  16. ZHU, Ming-hui, and Hui-qing ZHANG, "Research on model of indoor distance measurement based on RSSI [J]," Transducer and Microsystem Technologies8, pp. 008, 2010.
  17. Chowdhury, T. I., "A multi-step approach for RSSi-based distance estimation using smartphones," in Proc. of Networking Systems and Security (NSysS), 2015 International Conference on. IEEE, pp. 1-5, 2015.
  18. Xu, Jiuqiang, "Distance measurement model based on RSSI in WSN,"Wireless Sensor Network, vol. 2, no. 08, pp. 606, 2010. https://doi.org/10.4236/wsn.2010.28072
  19. Zhao, Junhui, Hao Zhang, and Rong Ran., "Distance Geometry-based Wireless Location Algorithms in Cellular Networks with NLOS Errors," KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 9, no.6, pp. 2132-2143, 2015. https://doi.org/10.3837/tiis.2015.06.009
  20. Parameswaran, Ambili Thottam, Mohammad Iftekhar Husain, and Shambhu Upadhyaya, "Is rssi a reliable parameter in sensor localization algorithms: An experimental study," in Proc. of Field Failure Data Analysis Workshop (F2DA09), pp. 5, 2009.
  21. Wang, Yapeng, "Bluetooth positioning using RSSI and triangulation methods," in Proc. of 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), IEEE, pp. 837-842, 2013.
  22. Liu, Hui, "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, 2007. https://doi.org/10.1109/TSMCC.2007.905750
  23. Wang, Yapeng, "Bluetooth indoor positioning using RSSI and least square estimation," IEEE ICFCC, pp. 837-42, 2013.
  24. Liu, Li-jun, and Hai-jun Ma, "Study on Wireless Sensor Network Boundary Localization Based on RSSI," in Proc. of Wireless Communication and Sensor Network (WCSN), 2014 International Conference on. IEEE, pp. 232-235, 2014.
  25. Faragher, Ramsey, and Robert Harle. "Location fingerprinting with bluetooth low energy beacons," IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418-2428, 2015. https://doi.org/10.1109/JSAC.2015.2430281
  26. Subhan, Fazli, "Indoor positioning in bluetooth networks using fingerprinting and lateration approach," in Proc. of Information Science and Applications (ICISA), 2011 International Conference on. IEEE, 2011.
  27. Li, Dong, "Measurement-based AP Deployment Mechanism for Fingerprint-based Indoor Location Systems," KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, vol. 10, no. 4, 2016.

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