Location-Clustering Techniques For Wlan Location Determination Systems

Abstract We present location clustering as a technique to significantly reduce the computational requirements of WLAN location determination systems. We provide two algorithms, joint clustering and incremental triangulation, and describe their trade-offs between computational cost and location determination accuracy. Both techniques reduce computational cost by more than an order of magnitude, allowing noncentralized implementation on mobile clients and enabling new context-aware applications. We present a performance comparison of the two techniques in an actual testbed implementation.

[1]  Moustafa Youssef,et al.  Small-scale compensation for WLAN location determination systems , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[2]  Asim Smailagic,et al.  Location sensing and privacy in a context-aware computing environment , 2002, IEEE Wirel. Commun..

[3]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[4]  Moustafa Youssef,et al.  WLAN location determination via clustering and probability distributions , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[5]  Moustafa Youssef,et al.  Handling samples correlation in the Horus system , 2004, IEEE INFOCOM 2004.

[6]  Kostas E. Bekris,et al.  Robotics-Based Location Sensing Using Wireless Ethernet , 2002, MobiCom '02.

[7]  Ted Kremenek,et al.  A Probabilistic Room Location Service for Wireless Networked Environments , 2001, UbiComp.

[8]  Henry Tirri,et al.  A Probabilistic Approach to WLAN User Location Estimation , 2002, Int. J. Wirel. Inf. Networks.

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  Moustafa Youssef,et al.  Continuous space estimation for WLAN location determination systems , 2004, Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969).

[11]  Colin L. Mallows,et al.  A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks , 2004, IEEE INFOCOM 2004.

[12]  Moustafa Youssef,et al.  A Probabilistic Clustering-Based Indoor Location Determination System , 2002 .

[13]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).