A novel infrastructure WLAN locating method based on neural network

Comparing to the client based locating, infrastructure based methods do not require install special software or hardware at client side. So it not only fits for real deployment, but also supports some specific requirement (ex target tracking). Former researchers mainly adopt k-NN method in infrastructure based locating. However, its computing complexity is proportional to the size of sample set, which makes it unscalable when the system grows large. This paper proposes a novel infrastructure WLAN locating method by utilizing neural networks and a new training method to overcome the effect of different power levels of client devices. Through real deployment and testing, the result shows that the computing complexity is much lower than the k-NN method, while the accuracy is very close.

[1]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[3]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[4]  M. Ciurana,et al.  A novel TOA-based indoor tracking system over IEEE 802.11 networks , 2007, 2007 16th IST Mobile and Wireless Communications Summit.

[5]  Jesús Favela,et al.  Estimating User Location in a WLAN Using Backpropagation Neural Networks , 2004, IBERAMIA.

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

[7]  R. Battiti,et al.  Neural network models for intelligent networks : deriving the location from signal patterns , 2002 .

[8]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) using AOA , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[9]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[10]  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).

[11]  C. Despins,et al.  Indoor location using received signal strength of IEEE 802.11b access point , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[12]  Marcela D. Rodríguez,et al.  Location-aware access to hospital information and services , 2004, IEEE Transactions on Information Technology in Biomedicine.

[13]  Tsung-Nan Lin,et al.  Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[14]  M. Russo,et al.  Location Determination in Indoor Environment based on RSS Fingerprinting and Artificial Neural Network , 2007, 2007 9th International Conference on Telecommunications.

[15]  A. S. Krishnakumar,et al.  Infrastructure-based location estimation in WLAN , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[16]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[17]  A. S. Krishnakumar,et al.  Bayesian indoor positioning systems , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[18]  V. Padmanabhan,et al.  Enhancements to the RADAR User Location and Tracking System , 2000 .

[19]  A. S. Krishnakumar,et al.  The theory and practice of signal strength-based location estimation , 2005, 2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[20]  Sungyoung Lee,et al.  In-building Localization using Neural Networks , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.