Reliable indoor location prediction using conformal prediction

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• 作者：Khuong An Nguyen ; Zhiyuan Luo
• 关键词：Conformal prediction ; Fingerprinting ; Indoor localisation ; Bluetooth tracking
• 刊名：Annals of Mathematics and Artificial Intelligence
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：74
• 期：1-2
• 页码：133-153
• 全文大小：974 KB
• 参考文献：1.Anastasi, G., Bandelloni, R., Conti, M., Delmastro, F., Gregori, E., Mainetto, G.: Experimenting an indoor bluetooth-based positioning service. In: 23rd International Conference on Distributed Computing Systems Workshops, pp. 480鈥?83. IEEE (2003)
  2.Bahl, P., Padmanabhan, V.N.: Radar: An in-building rf-based user location and tracking system. In: INFOCOM 2000. Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies., vol.聽2, pp. 775鈥?84. IEEE (2000)
  3.Bargh, M.S., de聽Groote, R.: Indoor localization based on response rate of bluetooth inquiries. In: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments, pp. 49鈥?4. ACM (2008)
  4.Battiti, R., Le, N.T., Villani, A.: Location-aware computing: a neural network model for determining location in wireless lans. Tech. Rep. DIT-02-0083 (2002)
  5.Bellotti, T., Luo, Z., Gammerman, A., Van聽Delft, F.W., Saha, V.: Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines. Int. J. Neural Syst. 15(4), 247鈥?58 (2005)View Article
  6.Brunato, M., Kiss聽Kallo, C.: Transparent location fingerprinting for wireless services. Tech. Rep. DIT-02-071 (2002)
  7.Bruno, R., Delmastro, F.: Design and analysis of a bluetooth-based indoor localization system. In: Personal Wireless Communications, pp. 711鈥?25. Springer (2003)
  8.Chen, Y., Lymberopoulos, D., Liu, J., Priyantha, B.: Fm-based indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp.聽169鈥?82. ACM (2012)
  9.Cheung, K.C., Intille, S.S., Larson, K.: An inexpensive bluetooth-based indoor positioning hack. In: Proc. UbiComp06 Extended Abstracts (2006)
  10.Dashevskiy, M., Luo, Z.: Reliable probabilistic classification of internet traffic. IJIA 6(2), 133鈥?46 (2009)
  11.Draper, N.R., Smith, H., Pownell, E.: Applied Regression Analysis. Wiley, New York (1966)
  12.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-interscience (2012)
  13.Fang, S.H., Lin, T.N.: Indoor location system based on discriminant-adaptive neural network in ieee 802.11 environments. IEEE Trans. Neural Netw. 19(11), 1973鈥?978 (2008)View Article
  14.Hallberg, J., Nilsson, M., Synnes, K.: Positioning with bluetooth. In: 10th International Conference on Telecommunications, ICT 2003, vol.聽2, pp. 954鈥?58. IEEE (2003)
  15.Hay, S., Harle, R.: Bluetooth tracking without discoverability. In: Location and Context Awareness, pp. 120鈥?37. Springer (2009)
  16.Hightower, J., Borriello, G.: Location systems for ubiquitous computing. Computer 34(8), 57鈥?6 (2001)View Article
  17.Huang, A.: The use of bluetooth in linux and location aware computing. Ph.D. thesis, Massachusetts Institute of Technology (2005)
  18.Jevring, M., de聽Groote, R., Hesselman, C.: Dynamic optimization of bluetooth networks for indoor localization. In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, pp. 663鈥?68. ACM (2008)
  19.Kemper, J., Linde, H.: Challenges of passive infrared indoor localization. In: 5th Workshop on Positioning, Navigation and Communication. WPNC 2008, pp. 63鈥?0. IEEE (2008)
  20.Kothari, N., Kannan, B., Glasgwow, E.D., Dias, M.B.: Robust indoor localization on a commercial smart phone. Procedia Computer Science 10, 1114鈥?120 (2012)View Article
  21.Letchner, J., Fox, D., LaMarca, A.: Large-scale localization from wireless signal strength. In: Proceedings of the national conference on artificial intelligence, vol.聽20, pp. 15鈥?0. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999 (2005)
  22.Lin, T.N., Lin, P.C.: Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks. In: 2005 International Conference on Wireless Networks, Communications and Mobile Computing, vol.聽2, pp. 1569鈥?574. IEEE (2005)
  23.Link, J.A.B., Smith, P., Viol, N., Wehrle, K.: Footpath: Accurate map-based indoor navigation using smartphones. In: 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1鈥?. IEEE (2011)
  24.Madhavapeddy, A., Tse, A.: A study of bluetooth propagation using accurate indoor location mapping. In: UbiComp 2005: Ubiquitous Computing, pp. 105鈥?22. Springer (2005)
  25.Naya, F., Noma, H., Ohmura, R., Kogure, K.: Bluetooth-based indoor proximity sensing for nursing context awareness. In: Ninth IEEE International Symposium on Wearable Computers, pp. 212鈥?13. IEEE (2005)
  26.Nguyen, K., Luo, Z.: Evaluation of bluetooth properties for indoor localisation. In: Progress in Location-Based Services, pp. 127鈥?49. Springer (2013)
  27.Nguyen, K.A.: Robot-based evaluation of bluetooth fingerprinting. Master鈥檚 thesis, Computer Lab, University of Cambridge (2011)
  28.Pandya, D., Jain, R., Lupu, E.: Indoor location estimation using multiple wireless technologies. In: 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications. PIMRC 2003, vol.聽3, pp. 2208鈥?212. IEEE (2003)
  29.Priyantha, N.B.: The cricket indoor location system. Ph.D. thesis, Massachusetts Institute of Technology (2005)
  30.Rizos, C., Dempster, A.G., Li, B., Salter, J.: Indoor positioning techniques based on wireless lan (2007)
  31.Robertson, P., Angermann, M., Krach, B.: Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors. In: Proceedings of the 11th international conference on Ubiquitous computing, pp. 93鈥?6. ACM (2009)
  32.Sch枚lkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization and beyond. MIT Press (2002)
  33.Shafer, G., Vovk, V.: A tutorial on conformal prediction. The Journal of Machine Learning Research 9, 371鈥?21 (2008)MATH MathSciNet
  34.Taheri, A., Singh, A., Emmanuel, A.: Location fingerprinting on infrastructure 802.11 wireless local area networks (wlans) using locus. In: 29th Annual IEEE International Conference on Local Computer Networks, pp. 676鈥?83. IEEE (2004)
  35.Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer Science+ Business Media (2005)MATH
  36.Want, R., Hopper, A., Falc茫o, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems (TOIS) 10(1), 91鈥?02 (1992)View Article
  37.Ward, A., Jones, A., Hopper, A.: A new location technique for the active office. IEEE Personal Communications 4(5), 42鈥?7 (1997)View Article
  38.W枚lfle, G., Hoppe, R., Zimmermann, D., Landstorfer, F.M.: Enhanced localization technique within urban and indoor environments based on accurate and fast propagation models. In: European Wireless, pp. 25鈥?8 (2002)
  39.Woodman, O., Harle, R.: Pedestrian localisation for indoor environments. In: Proceedings of the 10th international conference on Ubiquitous computing, pp. 114鈥?23. ACM (2008)
  40.Xiang, Z., Song, S., Chen, J., Wang, H., Huang, J., Gao, X.: A wireless lan-based indoor positioning technology. IBM Journal of Research and Development 48(5.6), 617鈥?26 (2004)View Article
  41.Youssef, M., Agrawala, A.: The horus location determination system. Wireless Networks 14(3), 357鈥?74 (2008)View Article
  42.Youssef, M.A., Agrawala, A.: On the optimality of wlan location determination systems. Tech. Rep. CS-TR-4459 (2003)
  
• 作者单位：Khuong An Nguyen (1)
  Zhiyuan Luo (1)
  
  1. Department of Computer Science, Royal Holloway, University of London Egham, Surrey, TW20 0EX, UK
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mathematics
  Computer Science, general
  Complexity
  
• 出版者：Springer Netherlands
• ISSN：1573-7470

文摘

Indoor localisation is the state-of-the-art to identify and observe a moving human or an object inside a building. However, because of the harsh indoor conditions, current indoor localisation systems remain either too expensive or not accurate enough. In this paper, we tackle the latter issue in a different direction, with a new conformal prediction algorithm to enhance the accuracy of the prediction. We handle the common indoor signal attenuation issue, which introduces errors into the training database, with a reliability measurement for our prediction. We show why our approach performs better than other solutions through empirical studies with two testbeds. To the best of our knowledge, we are the first to apply conformal prediction for the localisation purpose in general, and for the indoor localisation in particular.