Abstract
In the area of pervasive computing a key concept is context-awareness. One type of context information is location information of wireless network clients. Research in indoor localization of wireless network clients based on signal strength is receiving a lot of attention. However, not much of this research is directed towards handling the issue of adapting a signal strength based indoor localization system to the hardware and software of a specific wireless network client, be it a tag, PDA or laptop. Therefore current indoor localization systems need to be manually adapted to work optimally with specific hardware and software. A second problem is that for a specific hardware there will be more than one driver available and they will have different properties when used for localization. Therefore the contribution of this paper is twofold. First, an automatic system for evaluating the fitness of a specific combination of hardware and software is proposed. Second, an automatic system for adapting an indoor localization system based on signal strength to the specific hardware and software of a wireless network client is proposed. The two contributions can then be used together to either classify a specific hardware and software as unusable for localization or to classify them as usable and then adapt them to the signal strength based indoor localization system.
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References
Haeberlen, A., Flannery, E., Ladd, A.M., Rudys, A., Wallach, D.S., Kavraki, L.E.: Practical robust localization over large-scale 802.11 wireless networks. In: Proceedings of the Tenth ACM International Conference on Mobile Computing and Networking, MOBICOM (2004)
Youssef, M., Agrawala, A.: The horus WLAN location determination system (2005)
Sun, G., Chen, J., Guo, W., K, J., Liu, R.: Signal processing techniques in network-aided positioning: A survey. IEEE Signal Processing Magazine (2005)
Muthukrishnan, K., Lijding, M., Havinga, P.: Towards smart surroundings: Enabling techniques and technologies for localization. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 350–362. Springer, Heidelberg (2005)
Bahl, P., Padmanabhan, V.N.: RADAR: An in-building rf-based user location and tracking system (2000)
Krumm, J., Horvitz, E.: LOCADIO: Inferring motion and location from wi-fi signal strengths. In: First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, Mobiquitous 2004 (2004)
Roos, T., Myllymäki, P., Tirri, H., Misikangas, P., Sievänen, J.: A probabilistic approach to WLAN user location estimation. Int. Journal of Wireless Information Networks 9(3), 155–164 (2002)
Ladd, A.M., Bekris, K.E., Rudys, A., Marceau, G., Kavraki, L.E., Wallach, D.S.: Robotics-based location sensing using wireless ethernet. In: Eight ACM International Conference on Mobile Computing and Networking (MOBICOM 2002), pp. 227–238. ACM Press, New York (2002)
Castro, P., Chiu, P., Kremenek, T., Muntz, R.: A probabilistic room location service for wireless networked environments (2001)
Locher, T., Wattenhofer, R., Zollinger, A.: Received-signal-strength-based logical positioning resilient to signal fluctuation (2005)
Ekahau, http://www.ekahau.com
PanGo, http://www.pangonetworks.com
Kontkanen, P., Myllymäki, P., Roos, T., Tirri, H., Valtonen, K., Wettig, H.: Topics in probabilistic location estimation in wireless networks. In: Invited talk at the 15th IEEE Symposium on Personal, Indoor and Mobile Radio Communications (2004)
Bahl, P., Padmanabhan, V.N., Balachandran, A.: A software system for locating mobile users: Design, evaluation, and lessons. Microsoft Research Technical Report MSR-TR-2000-12, Microsoft (2000)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Wikipedia, http://en.wikipedia.org/wiki/Autocorrelation
Crassidis, J., Junkins, J.: Optimal Estimation of Dynamic Systems. Chapman & Hall/CRC Press, Boca Raton, FL (2004)
Berry, D.A., Lindgren, B.W.: Statistics: Theory and Methods, 2nd edn. Duxbury/Wadsworth, Belmont, CA (1996)
Placelab, http://www.placelab.org
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Kjærgaard, M.B. (2006). Automatic Mitigation of Sensor Variations for Signal Strength Based Location Systems. In: Hazas, M., Krumm, J., Strang, T. (eds) Location- and Context-Awareness. LoCA 2006. Lecture Notes in Computer Science, vol 3987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752967_3
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DOI: https://doi.org/10.1007/11752967_3
Publisher Name: Springer, Berlin, Heidelberg
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