Subscribe

Issue No.01 - January (2012 vol.11)

pp: 100-110

Shih-Hau Fang , Yuan Ze University, Taoyuan

Tsung-Nan Lin , National Taiwan University, Taipei

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.30

ABSTRACT

This paper presents a novel approach to building a WLAN-based location fingerprinting system. Our algorithm intelligently transforms received signal strength (RSS) into principal components (PCs) such that the information of all access points (APs) is more efficiently utilized. Instead of selecting APs, the proposed technique replaces the elements with a subset of PCs to simultaneously improve the accuracy and reduce the online computation. Our experiments are conducted in a realistic WLAN environment. The results show that the mean error is reduced by 33.75 percent, and the complexity by 40 percent, as compared to the existing methods. Moreover, several benefits of our algorithm are demonstrated, such as requiring fewer training samples and enhancing the robustness to RSS anomalies.

INDEX TERMS

WLAN, indoor localization, fingerprinting, transformation, principal component.

CITATION

Shih-Hau Fang, Tsung-Nan Lin, "Principal Component Localization in Indoor WLAN Environments",

*IEEE Transactions on Mobile Computing*, vol.11, no. 1, pp. 100-110, January 2012, doi:10.1109/TMC.2011.30REFERENCES

- [1] K. Axel,
Location-Based Services: Fundamentals and Operation. John Wiley & Sons, 2005.- [2] S. Tekinay, “Wireless Geolocation Systems and Services,”
IEEE Comm. Magazine, vol. 36, no. 4, pp. 28-28, Apr. 1998.- [3] M. Kavitha, L. Maria, and H. Paul, “Towards Smart Surroundings: Enabling Techniques and Technologies for Localization,”
Proc. First Int'l Workshop Location and Context-Awareness (LoCA '05), pp. 350-362, 2005.- [4] M. Hazas, J. Scott, and J. Krumm, “Location-Aware Computing Comes of Age,”
Computer, vol. 37, no. 2, pp. 95-97, Feb. 2004.- [5] P. Prasithsangaree, P. Krishnamurthy, and P. Chrysanthis, “On Indoor Position Location with Wireless LANs,”
Proc. IEEE Int'l Symp. Personal, Indoor and Mobile Radio Comm., vol. 2, pp. 720-724, 2002.- [6] A. Krishnakumar and P. Krishnan, “On the Accuracy of Signal Strength-Based Estimation Techniques,”
Proc. IEEE INFOCOM, vol. 1, pp. 642-650, 2005.- [7] Y. Chen and H. Kobayashi, “Signal Strength Based Indoor Geolocation,”
Proc. IEEE Int'l Conf. Comm., vol. 1, pp. 436-439, 2002.- [8] A. Sayed, A. Tarighat, and N. Khajehnouri, “Network-Based Wireless Location: Challenges Faced in Developing Techniques for Accurate Wireless Location Information,”
IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 24-40, July 2005.- [9] T.S. Rappaport, J.H. Reed, and D. Woerner, “Position Location Using Wireless Communications on Highways of the Future,”
IEEE Comm. Magazine, vol. 34, no. 10, pp. 33-41, Oct. 1996.- [10] K.W. Kolodziej and J. Hjelm,
Local Positioning Systems: LBS Applications and Services. CRC Taylor & Francis, 2006.- [11] A. Smailagic and D. Kogan, “Location Sensing and Privacy in a Context-Aware Computing Environment,”
IEEE Wireless Comm., vol. 9, no. 5, pp. 10-17, Oct. 2002.- [12] A. Krishnakumar and P. Krishnan, “The Theory and Practice of Signal Strength-Based Location Estimation,”
Proc. Int'l Conf. Collaborative Computing: Networking, Applications and Worksharing, 2005.- [13] M. Kjærgaard, “A Taxonomy for Radio Location Fingerprinting,”
Proc. Int'l Conf. Location-and Context-Awareness, pp. 139-156, 2007.- [14] C. Patterson, R. Muntz, and C. Pancake, “Challenges in Location-Aware Computing,”
IEEE Pervasive Computing, vol. 2, no. 2, pp. 80-89, Apr.-June 2003.- [15] T.-N. Lin and P.-C. Lin, “Performance Comparison of Indoor Positioning Techniques Based on Location Fingerprinting in Wireless Networks,”
Proc. Int'l Conf. Wireless Networks, Comm. and Mobile Computing, pp. 1569-1574, 2005.- [16] M. Youssef, A. Agrawala, and A.U. Shankar, “WLAN Location Determination via Clustering and Probability Distributions,”
Proc. IEEE Int'l Conf. Pervasive Computing and Comm., pp. 143-150, 2003.- [17] Y. Chen, J. Yin, X. Chai, and Q. Yang, “Power-Efficient Access-Point Selection for Indoor Location Estimation,”
IEEE Trans. Knowledge and Data Eng., vol. 18, no. 7, pp. 877-888, July 2006.- [18] T. King, T. Haenselmann, and W. Effelsberg, “On-Demand Fingerprint Selection for 802.11-Based Positioning Systems,”
Proc. Int'l Symp. a World of Wireless, Mobile and Multimedia Networks, 2008.- [19] A. Kushki, K.N. Plataniotis, and A.N. Venetsanopoulos, “Kernel-Based Positioning in Wireless Local Area Networks,”
IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689-705, June 2007.- [20] R. Duda, P. Hart, and D. Stork,
Pattern Classification. John Wiley & Sons, 2000.- [21] I.T. Jollife,
Principal Component Analysis. Springer-Verlag, 2002.- [22] K.I. Diamantaras and S.Y. Kung,
Principal Component Neural Networks. John Wiley & Sons, 1996.- [23] T. King, T. Butter, H. Lemelson, T. Haenselmann, and W. Effelsberg, “Loc(lib,trace,eva,ana): Research Tools for 802.11-Based Positioning Systems,”
Proc. ACM Int'l Workshop Wireless Network Testbeds, Experimental Evaluation and Characterization, pp. 67-74, 2007.- [24] T. King, T. Haenselmann, and W. Effelsberg, “Deployment, Calibration, and Measurement Factors for Position Errors in 802.11-Based Indoor Positioning Systems,”
Proc. Int'l Conf. Location- and Context-Awareness, pp. 17-34, 2007.- [25] X. Li and K. Pahlavan, “Super-Resolution TOA Estimation with Diversity for Indoor Geolocation,”
IEEE Trans. Wireless Comm., vol. 3, no. 1, pp. 224-234, Jan. 2004.- [26] K. Pahlavan, X. Li, and J. Makela, “Indoor Geolocation Science and Technology,”
IEEE Comm. Magazine, vol. 40, no. 2, pp. 112-118, Feb. 2002.- [27] G. Sun, J. Chen, W. Guo, and K. Liu, “Signal Processing Techniques in Network-Aided Positioning: A Survey of State-of-the-Art Positioning Designs,”
IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 12-23, July 2005.- [28] P. Bahl and V.N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,”
Proc. IEEE INFOCOM, pp. 775-784, 2000.- [29] A. Agiwal, P. Khandpur, and H. Saran, “LOCATOR: Location Estimation System for Wireless LANs,”
Proc. ACM Int'l Workshop Wireless Mobile Applications and Services on WLAN Hotspots, pp. 102-109, 2004.- [30] R. Battiti, A. Villani, and T.L. Nhat, “Neural Network Models for Intelligent Networks: Deriving the Location from Signal Patterns,”
Proc. Ann. Symp. Autonomous Intelligent Networks and Systems, 2002.- [31] R. Battiti, T.L. Nhat, and A. Villani, “Location-Aware Computing: A Neural Network Model for Determining Location in Wireless LANs,” Technical Report DIT-02-0083, Dept. of Information and Comm. Technology, Univ. of Trento, 2002.
- [32] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,”
Wireless Information Networks, vol. 9, no. 3, pp. 155-164, 2002.- [33] S.H. Fang and T.N. Lin, “Indoor Localization by a Novel Probabilistic Approach,”
Proc. IEEE Eighth Workshop Signal Processing Advances in Wireless Comm., pp. 1-4, 2007.- [34] D. Madigan, E. Einahrawy, R. Martin, W.-H. Ju, P. Krishnan, and A. Krishnakumar, “Bayesian Indoor Positioning Systems,”
Proc. IEEE INFOCOM, vol. 2, pp. 1217-1227, 2005.- [35] D. Fox, J. Hightower, L. Liao, and D. Schulz, “Bayesian Filtering for Location Estimation,”
IEEE Pervasive Computing, vol. 2, no. 3, pp. 24-33, July-Sept. 2003.- [36] V. Seshadri, G. Zaruba, and M. Huber, “A Bayesian Sampling Approach to in-Door Localization of Wireless Devices Using Received Signal Strength Indication,”
Proc. IEEE Int'l Conf. Pervasive Computing and Comm., pp. 75-84, 2005.- [37] M. Youssef and A. Agrawala, “Handling Samples Correlation in the Horus System,”
Proc. IEEE INFOCOM, pp. 1023-1031, 2004.- [38] M. Youssef and A. Agrawala, “The Horus WLAN Location Determination System,”
Proc. Int'l Conf. Mobile Systems, Applications and Services, pp. 205-218, 2005.- [39] M. Youssef and A. Agrawala, “The Horus Location Determination System,”
Wireless Networks, vol. 14, no. 3, pp. 357-374, 2008.- [40] Z. li Wu, C. hung Li, J.-Y. Ng, and K.R. Leung, “Location Estimation via Support Vector Regression,”
IEEE Trans. Mobile Computing, vol. 6, no. 3, pp. 311-321, Mar. 2007.- [41] J. Pan, J. Kwok, Q. Yang, and Y. Chen, “Accurate and Low-Cost Location Estimation Using Kernels,”
Proc. Int'l Joint Conf. Artificial Intelligent, pp. 1366-1370, 2005.- [42] J.J. Pan, J.T. Kwok, Q. Yang, and Y. Chen, “Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing,”
IEEE Trans. Knowledge and Data Eng., vol. 18, no. 9, pp. 1181-1193, Sept. 2006.- [43] C. Nerguizian, C. Despins, and S. Affes, “Geolocation in Mines with an Impulse Response Fingerprinting Technique and Neural Networks,”
Proc. Vehicular Technology Conf., pp. 3589-3594, 2004.- [44] 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.- [45] T. King, S. Kopf, T. Haenselmann, C. Lubberger, and W. Effelsberg, “Compass: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses,”
Proc. Int'l Workshop Wireless Network Testbeds, Experimental Evaluation and Characterization, 2006.- [46] S. Golden and S. Bateman, “Sensor Measurements for Wi-Fi Location with Emphasis on Time-of-Arrival Ranging,”
IEEE Trans. Mobile Computing, vol. 6, no. 10, pp. 1185-1198, Oct. 2007.- [47] J. Yin, Q. Yang, and L.M. Ni, “Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation,”
IEEE Trans. Mobile Computing, vol. 7, no. 7, pp. 869-883, July 2008.- [48] S.-H. Fang, T.-N. Lin, and P.-C. Lin, “Location Fingerprinting in a Decorrelated Space,”
IEEE Trans. Knowledge and Data Eng., vol. 20, no. 5, pp. 685-691, May 2008.- [49] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara, “Accurate GSM Indoor Localization,”
Proc. Seventh Int'l Conf. Ubiquitous Computing , pp. 141-158, 2005.- [50] S.-H. Fang, J.-C. Chen, H.-R. Huang, and T.-N. Lin, “Is FM a Rf-Based Positioning Solution in a Metropolitan-Scale Environment? A Probabilistic Approach with Radio Measurements Analysis,”
IEEE Trans. Broadcasting, vol. 55, no. 3, pp. 577-588, Sept. 2009.- [51] X. Chai and Q. Yang, “Reducing the Calibration Effort for Probabilistic Indoor Location Estimation,”
IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 649-662, June 2007.- [52] L.F.M. de Moraes and B.A.A. Nunes, “Calibration-Free WLAN Location System Based on Dynamic Mapping of Signal Strength,”
Proc. ACM Int'l Workshop Mobility Management and Wireless Access, pp. 92-99, 2006.- [53] E.A. Martínez, R. Cruz, and J. Favela, “Estimating User Location in a WLAN Using Backpropagation Neural Networks,”
Proc. Ninth Ibero-Am. Conf. AI, pp. 737-746, 2004.- [54] M.B. Kjærgaard, G. Treu, and C. Linnhoff-Popien, “Zone-Based RSS Reporting for Location Fingerprinting,”
Proc. Fifth Int'l Conf. Pervasive Computing, pp. 316-333, 2007.- [55] K. Kaemarungsi, “Efficient Design of Indoor Positioning Systems Based on Location Fingerprinting,”
Proc. Int'l Conf. Wireless Networks, Comm. and Mobile Computing, pp. 181-186, 2005.- [56] Y. Xu, J. Winter, and W. Lee, “Prediction-Based Strategies for Energy Saving in Object Tracking Sensor Networks,”
Proc. IEEE Int'l Conf. Mobile Data Management, pp. 346-357, 2004.- [57] Y. Xu and W. Lee, “On Localized Prediction for Power Efficient Object Tracking in Sensor Networks,”
Proc. Int'l Conf. Distributed Computing Systems Workshop, pp. 434-439, 2003.- [58] IEEE 802.11e/D5.0,
Draft Supplement to Part 11: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Medium Access Control (MAC) Enhancements for Quality of Service (QoS), IEEE, 2003.- [59] M. Youssef, M. Abdallah, and A. Agrawala, “Multivariate Analysis for Probabilistic WLAN Location Determination Systems,”
Proc. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services, pp. 353-362, 2005.- [60] A. Haeberlen, E. Flannery, A.M. Ladd, A. Rudys, D.S. Wallach, and L.E. Kavraki, “Practical Robust Localization over Large-Scale 802.11 Wireless Networks,”
Proc. ACM MobiCom, 2004.- [61] J. Small, A. Smailagic, and D.P. Siewiorek, “Determining User Location for Context Aware Computing through the Use of a Wireless LAN Infrastructure,” technical report, Inst. for Complex Eng. Systems, Carnegie Mellon Univ., 2000.
- [62] P. Krishnan, A. Krishnakumar, W.-H. Ju, C. Mallows, and S. Gamt, “A System for LEASE: Location Estimation Assisted by Stationery Emitters for Indoor RF Wireless Networks,”
Proc. IEEE INFOCOM, vol. 2, pp. 1001-1011, 2004.- [63] M. Youssef, Dept. of Computer Science, Univ. of Maryland, http://www.cs.umd.edu/usersmoustafa, 2001.
- [64] M. Brunato and R. Battiti, “Statistical Learning Theory for Location Fingerprinting in Wireless LANs,”
Computer Networks, vol. 47, no. 6, pp. 825-845, 2005.- [65] V. Honkavirta, T. Perala, S. Ali-Loytty, and R. Piche, “A Comparative Survey of Wlan Location Fingerprinting Methods,”
Proc. Sixth Workshop Positioning, Navigation and Comm., pp. 243-251, 2009.- [66] P. Castro, P. Chiu, T. Kremenek, and R.R. Muntz, “A Probabilistic Room Location Service for Wireless Networked Environments,”
Proc. Int'l Conf. Ubiquitous Computing, pp. 18-34, 2001.- [67] A. Ladd, K. Bekris, G. Marceau, A. Rudys, L. Kavraki, and D. Wallach, “Robotics-Based Location Sensing Using Wireless Ethernet,” Technical Report TR02-393, Dept. of Computer Science, Rice Univ., 2002.
- [68] K. Kaemarungsi and P. Krishnamurthy, “Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting,”
Proc. First Ann. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services, pp. 14-23, 2004.- [69] K. Kaemarungsi, “Distribution of WLAN Received Signal Strength Indication for Indoor Location Determination,”
Proc. Int'l Symp. Wireless Pervasive Computing, p. 6, 2006.- [70] P. Castro and R. Munz, “Managing Context Data for Smart Spaces,”
IEEE Personal Comm., vol. 7, no. 5, pp. 44-46, Oct. 2000. |