The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - September (2009 vol.8)
pp: 1250-1264
Carlos Figuera , University Rey Juan Carlos, Fuenlabrada
Inmaculada Mora-Jiménez , University Rey Juan Carlos, Fuenlabrada
Alicia Guerrero-Curieses , University Rey Juan Carlos, Madrid
José Luis Rojo-Álvarez , University Rey Juan Carlos, Madrid
Estrella Everss , University Rey Juan Carlos, Madrid
Mark Wilby , University Rey Juan Carlos, Madrid
Javier Ramos-López , University Rey Juan Carlos, Madrid
ABSTRACT
Indoor Location (IL) using Received Signal Strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (WiFi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our WiFi network, showing relevant information relating to the different fingerprinting schemes that can be used.
INDEX TERMS
Received signal strength, indoor location, fingerprinting, uncertainty, leave one out, bootstrap resampling, IEEE 802.11b, WiFi.
CITATION
Carlos Figuera, Inmaculada Mora-Jiménez, Alicia Guerrero-Curieses, José Luis Rojo-Álvarez, Estrella Everss, Mark Wilby, Javier Ramos-López, "Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location", IEEE Transactions on Mobile Computing, vol.8, no. 9, pp. 1250-1264, September 2009, doi:10.1109/TMC.2009.37
REFERENCES
[1] F. Gustafsson and F. Gunnarsson, “Mobile Positioning Using Wireless Networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 41-43, July 2005.
[2] G. Sun, J. Chen, W. Guo, and K.R. Liu, “Signal Processing Techniques in Network-Aided Postioning: A Survey of State-of-the-Art Positioning Designs,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 12-23, July 2005.
[3] A. Sayed, A. Tarighat, and N. Khajehbouri, “Network-Based Wireless Location,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 24-40, July 2005.
[4] N. Patwari, J.N. Ash, S. Kyperountas, A.O. Hero III, R.L. Moses, and N.S. Correal, “Locating the Nodes,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 54-69, July 2005.
[5] Y. Ke, J. Chen, and H. Refai, “WLAN-Based, Indoor Medical Residents Positioning System,” Proc. Second IFIP Int'l Conf. Wireless and Optical Comm. Networks (WOCN '05), pp. 556-560, Mar. 2005.
[6] R. Christ and R. Lavigne, “Radio Frequency-Based Personnel Location Systems,” Proc. IEEE 34th Ann. Int'l Carnahan Conf. Security Technology, pp. 141-150, Oct. 2000.
[7] A. Harder, L. Song, and Y. Wang, “Towards an Indoor Location System Using RF Signal Strength in IEEE 802.11 Networks,” Proc. Int'l Conf. Information Technology: Coding and Computing, vol. 2, pp.228-233, Apr. 2005.
[8] M. Anlauff and A. Sünbül, “Deploying Localization Services in Wireless Sensor Networks,” Proc. IEEE 24th Int'l Conf. Distributed Computing Systems Workshops (ICDCSW '04), pp. 782-787, Mar. 2004.
[9] K. Kaemarungsi, “Distribution of WLAN Received Signal Strength Indication for Indoor Location Determination,” Proc. First Int'l Symp. Wireless Pervasive Computing, pp. 6-11, Jan. 2006.
[10] M. Brunato and R. Battiti, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,” Computer Networks, vol. 47, no. 6, pp. 825-845, Apr. 2005.
[11] R. Jan and Y. Lee, “An Indoor Geolocation System for Wireless LANs,” Proc. IEEE Parallel Processing Workshops, pp. 442-454, Oct. 2003.
[12] T. Roos, P. Myllymäki, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” Int'l J. Wireless Information Networks, vol. 9, no. 3, pp. 155-164, July 2002.
[13] K. Kaemarungsi and P. Krishnamurthy, “Modeling of Indoor Positioning Systems Based on Location Figerprinting,” Proc. IEEE INFOCOM, vol. 2, pp. 1012-1022, Mar. 2004.
[14] P. Bahl and V. Padmanabhan, “Radar: A In-Building rf Based User Location and Tracking System,” Proc. IEEE INFOCOM, pp. 775-784, Mar. 2000.
[15] G. Wassi, C. Despins, Grenier, and C. Nerguizian, “Indoor Location Using Received Signal Strength of IEEE 802.11b Access Point,” Proc. IEEE Canadian Conf. Electrical and Computer Eng. (CCECE '05), pp. 1367-1370, May. 2005.
[16] J. Pan, J. Kwok, Q. Yang, and Y. Chen, “Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing,” IEEE Trans. Knowledge Data Eng., vol. 18, no. 9, pp. 1181-1193, Sept. 2006.
[17] P.P. Krishnan, A.S. Krishnakumar, J. Wen-Hua, C. Mallows, and S.N. Gamt, “A System for LEASE: Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks,” Proc. IEEE INFOCOM, vol. 2, pp. 1001-1011, Mar. 2004.
[18] S. Saha, K. Chaudhuri, D. Sanghi, and P. Bhagwat, “Location Determination of a Mobile Device Using IEEE 802.11b Access Point Signals,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC '03), pp. 1987-1992, Mar. 2003.
[19] M. Youssef, A. Agrawala, and A. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” Proc. IEEE Int'l Conf. Pervasive Computing and Comm. (PerCom '03), pp.143-150, Mar. 2003.
[20] D. Madigan, E. Elnahrawy, R. Martin, W.-H. Ju, P. Krishnan, and A. Krishnakumar, “Bayesian Indoor Positioning Systems,” Proc. IEEE INFOCOM, vol. 2, pp. 1217-1227, Mar. 2005.
[21] A.S. Krishnakumar and P. Krishnan, “The Theory and Practice of Signal Strength-Based Location Estimation,” Proc. IEEE Int'l Conf. Collaborative Computing: Networking, Applications and Worksharing, p. 10, Dec. 2005.
[22] P. Castro, P. Chiu, T. Kremenek, and R. Muntz, “A Probabilistic Room Location Service for Wireless Networked Environments,” Proc. Third Int'l Conf. Ubiquitous Computing (Ubicomp '01), pp. 18-34, Sept./Oct. 2001.
[23] A. Kushki, K.N. Plataniotis, A.N. Venetsanopoulos, and C.S. Regazzoni, “Radio Map Fusion for Indoor Positioning in Wireless Local Area Networks,” Proc. IEEE Seventh Int'l Conf. Information Fusion, vol. 2, pp. 1311-1318, July 2005.
[24] U. Ahmad, A. Gavrilov, S. Lee, and Y.-K. Lee, “Modular Multilayer Perceptron for WLAN Based Localization,” Proc. IEEE Int'l Conf. Neural Networks (IJCNN '06), pp. 3465-3471, July 2006.
[25] O. Baala and A. Caminada, “WLAN-Based Indoor Positioning System: Experimental Results for Stationary and Tracking MS,” Proc. Int'l Conf. Comm. Technology (ICCT '06), pp. 1-4, Nov. 2006.
[26] A. Kushki and A.V.K.N. Plataniotis, “Kernel-Based Positioning in Wireless Local Area Networks,” IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689-705, June 2007.
[27] E. Elnahrawy, X. Li, and R. Martin, “The Limits of Localization Using Signal Strength: A Comparative Study,” Proc. IEEE Int'l Conf. Sensor and Ad Hoc Comm. and Networks (SECON '04), pp. 406-414, Oct. 2004.
[28] A. Howard, S. Siddiji, and G.S. Sukhatme, “An Experimental Study of Location Using Wireless Ethernet,” Proc. Fourth Int'l Conf. Field and Service Robotics, vol. 24, pp. 145-153, July 2003.
[29] S. Ito and N. Kawaguchi, “Data Correction Method Using Ideal Wireless LAN Model in Positioning System,” Proc. IEEE 17th Int'l Symp. Personal, Indoor and Mobile Radio Comm., pp. 1-5, Sept. 2006.
[30] Netstumbler Web Page, http:/www.netstumbler.com, 2009.
[31] Ndis Developer's Reference, http:/www.ndis.com, 2009.
[32] T. Mantoro and C.W. Johnson, “$\eta k$ -Nearest Neighbor Algorithm for Estimation of Symbolic User Location in Pervasive Computing Environments,” Proc. IEEE Int'l Symp. World of Wireless Mobile and Multimedia Networks (WoWMoM '05), pp. 472-474, June 2005.
[33] A. Ault, X. Zhong, and E.J. Coyle, “K-Nearest-Neighbor Analysis of Received Signal Strength Distance Estimation across Environments,” Proc. IEEE First Workshop Wireless Network Measurements (WiNMee '05), pp. 75-84, Apr. 2005.
[34] A. Ladd, K. Bekris, A. Rudys, D. Wallach, and L. Kavraki, “On the Feasibility of Using Wireless Ethernet for Indoor Localization,” IEEE Trans. Robotics and Automation, vol. 20, no. 3, pp. 555-559, June 2004.
[35] C. Nerguizian, C. Despins, and S. Affes, “Geolocation in Mines with an Impulse Response Fingerprinting Technique and Neural Networks,” IEEE Trans. Wireless Comm., vol. 5, no. 3, pp. 603-611, Mar. 2006.
[36] R. Battiti, A. Villani, and T.L. Nha, “Neural Network Models for Intelligent Networks: Deriving the Location from Signal Patterns,” Proc. Symp. Autonomous Intelligent Networks and Systems (AINS '02), May 2002.
[37] L. Tsung-Nan and L. Po-Chiang, “Performance Comparison of Indoor Positioning Techniques Based on Location Fingerprinting in Wireless Networks,” Proc. Int'l Conf. Wireless Networks, Comm. and Mobile Computing, vol. 2, pp. 1569-1574, June 2005.
[38] B. Efron and R. Tibshirani, An Introduction to the Bootstrap. Chapman & Hall/CRC, 1994.
[39] V. Vapnik, The Nature of Statistical Learning Theory. Springer Verlag, 1995.
[40] O. Duda, P. Hart, and D. Stork, Pattern Classification. John Wiley & Sons, 2001.
[41] P. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach. Prentice-Hall Int'l, 1982.
[42] C. Archambeau, M. Valle, A. Assenza, and M. Verleysen, “Assessment of Probability Density Estimation Methods: Parzen Window and Finite Gaussian Mixtures,” Proc. Int'l Symp. Circuits and Systems (ISCAS '06), pp. 1-4, May 2006.
[43] D.F. Specht, “A General Regression Neural Network,” IEEE Trans. Neural Networks, vol. 2, no. 6, pp. 568-576, Nov. 1991.
498 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool