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Design and Realization of Precise Indoor Localization Mechanism for Wi-Fi Devices

  • Su, Weideng (School of Electronics and Information, Tongji University) ;
  • Liu, Erwu (School of Electronics and Information, Tongji University) ;
  • Auge, Anna Calveras (Wireless Network Group, Department of Telematics Engineering, Universitat Politecnica de Catalunya) ;
  • Garcia-Villegas, Eduard (Wireless Network Group, Department of Telematics Engineering, Universitat Politecnica de Catalunya) ;
  • Wang, Rui (School of Electronics and Information, Tongji University) ;
  • You, Jiayi (School of Electronics and Information, Tongji University)
  • Received : 2016.07.31
  • Accepted : 2016.10.30
  • Published : 2016.12.31

Abstract

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.

Keywords

References

  1. M. A. Al-Ammar, S. Alhadhrami, A. Al-Salman, A. Alarifi, H. S. Al-Khalifa, A. Alnafessah, and M. Alsaleh, "Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms," in Proc. of International Conference on Cyberworlds, pp. 245-252, Oct., 2014.
  2. Wi-Fi Alliance, "Total Wi-Fi device shipments to surpass ten billion this month," Jan., 2015.
  3. C. Yang, H. R. Shao, "WiFi-based indoor positioning," IEEE Communications Magazine, vol. 53, no. 3, pp. 150-157, Mar., 2015.
  4. I. Bisio, M. Cerruti, and et al., "A trainingless wifi fingerprint positioning approach over mobile devices," IEEE Antennas and Wireless Propagation Letters, vol.13, pp. 832-835, 2014. https://doi.org/10.1109/LAWP.2014.2316973
  5. J. Tang, Y. Chen, L. Chen, J. Liu, J. Hyyppa, A. Kukko, H. Kaartinen, H. Hyyppa, and R. Chen, "Fast Fingerprint Database Maintenance for Indoor Positioning Based on UGV SLAM," Sensors, vol. 15, no. 3, pp. 5311-5330, Mar., 2015. https://doi.org/10.3390/s150305311
  6. Y. Du, D. Yang, C. Xiu, "A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower," Sensors, vol. 15, no. 4, pp. 8358-8381, Apr., 2015. https://doi.org/10.3390/s150408358
  7. Y. Mo, Z. Zhang, Y. Lu, and et al., "A Novel Technique for Human Traffic based Radio Map Updating in Wi-Fi Indoor Positioning Systems," KSII Transactions on Internet and Information Systems (TIIS), vol. 9, no. 5, pp. 1881-1903, 2015.
  8. J. Huang, D. Millman, and et al., "Efficient, generalized indoor wifi graphslam," in Proc. of Robotics and Automation (ICRA), 2011 IEEE International Conference on., pp. 1038-1043, Sept., 2011.
  9. L. Bruno, P. Robertson, "Wislam: Improving footslam with wifi," in Proc. of Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on., IEEE, pp. 1-10, May, 2011.
  10. H. Wang, S. Sen, and et al., "No need to war-drive: unsupervised indoor localization," in Proc. of the 10th international conference on Mobile systems, applications, and services, pp. 197-210, 2012.
  11. C. H. Lim, Y. Wan, and et al., "A real-time indoor WiFi localization system utilizing smart antennas," IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 618-622, May, 2007. https://doi.org/10.1109/TCE.2007.381737
  12. D. J. Suroso, P. Cherntanomwong, P. Sooraksa, and J. Takada, "Fingerprint-based technique for indoor localization in wireless sensor networks using Fuzzy C-Means clustering algorithm," in Proc. of 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 1-5, Dec., 2011.
  13. P. Sunantasaengtong and S. Chivapreecha, "Mixed K-means and GA-based weighted distance fingerprint algorithm for indoor localization system," in Proc. of TENCON 2014-2014 IEEE Region 10 Conference, pp. 1-5, Oct., 2014.
  14. M. Shon, M. Jo, and et al., "An interactive cluster-based MDS localization scheme for multimedia information in wireless sensor networks," Computer communications, vol. 35, no. 15, pp. 1921-1929, Sept., 2012. https://doi.org/10.1016/j.comcom.2012.05.002
  15. L. Gogolak, S. Pletl, and D. Kukolj, "Indoor fingerprint localization in WSN environment based on neural network," in Proc. of 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics, pp. 293-296, Sept., 2011.
  16. Z. Chen, H. Zou, H. Jiang, Q. Soh, and et al., "Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization," Sensors, vol. 15, no. 1, pp. 715-732, Jan., 2015.
  17. D. Sanchez-Rodríguez, P. Hernandez-Morera, J. M. Quinteiro, I. Alonso-Gonzalez, "A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization," Sensors, vol. 15, no. 6, pp. 14809-14829, Jun., 2015. https://doi.org/10.3390/s150614809
  18. A. Patri and S. P. Rath, "Elimination of Gaussian noise using entropy function for a RSSI based localization," in Proc. of 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), pp. 690-694, Dec., 2013.
  19. C. H. Huang, L. H. Lee, and et al., "Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation," IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 3, pp. 728-739, Mar., 2015. https://doi.org/10.1109/TIM.2014.2347691
  20. B. Solhjoo and M. A. Tinati, "An adaptive environmental modeling localization method in Wireless Sensor Networks," in Proc. of 20th Iranian Conference on Electrical Engineering (ICEE2012), pp.1397-1402, May, 2012.
  21. Y. Guo, K. Huang, N. Jiang, X. Guo, Y. Li, and G. Wang, "An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking," IEEE Trans. on Mobile Comput., vol. 14, no. 3, pp. 484-494, Mar., 2015. https://doi.org/10.1109/TMC.2014.2329007
  22. C. B. Lim, S. H. Kang, H. H. Cho, S. W. Park, and J. G. Park, "An Enhanced Indoor Localization Algorithm Based on IEEE 802.11 WLAN Using RSSI and Multiple Parameters," in Proc. of 2010 Fifth International Conference on Systems and Networks Communications, pp. 238-242, Aug., 2010.
  23. J. S. Seybold, "Introduction to RF propagation," John Wiley & Sons, 2005.
  24. A. Bose, C. H. Foh, "A practical path loss model for indoor WiFi positioning enhancement," in Proc. of Information, Communications & Signal Processing, 2007 6th International Conference on., pp. 1-5, Dec., 2007.
  25. H. Ni, W. Xu, Y. Li, M. Tao, S. Song, H. Fan, "An Improved Method of Self-Adaptive Localization for Wireless Sensor Network in Dynamic Indoor Environment," in Proc. of 31st Chinese Control Conference (CCC), pp.6574-6577, Jul., 2012.
  26. W. Daamen, S. P. Hoogendoorn, "Free speed distributions for pedestrian traffic," in Proc. of the 85th Annual Meeting of Transportation Research Board, pp. 22-26, 2006.
  27. E. C. Chan, G. Baciu, S. C. Mak, "Using Wi-Fi signal strength to localize in wireless sensor networks," in Proc. of Communications and Mobile Computing, CMC'09. WRI International Conference on., IEEE, vol. 1, pp. 538-542, Jun., 2009.
  28. V. Erceg, "TGn channels models," IEEE P802.11TGn, doc IEEE 802.11-03/0940r4., 2004.