Low Cost Solution for Location Determination of Mobile Nodes in a Wireless Local Area Network

In this research paper we undertook the problem of location determination of mobile nodes in a wireless local area network, as finding out the location in a wireless environment is to-date not very easy, cheap and accurate using 802.11b technology. We used the classical method of triangulation with a little alteration which is later on described in the paper. Another method we used is the methods of calibration by research to produce more accurate results and also to cross verify the results of the method of triangulation. The work in this paper attempts to illustrate the different aspects of the problem of location determination in WiFi networks. We started with an investigation of the influence that network characteristics have on the ability of middleware location determination protocols to accurately estimate position of mobile nodes. We presented an implementation based study that demonstrated the strong relationship between the two of the location determination methods and their performance. The main objective of this paper is to provide method in which no dedication hardware is required. Only wireless LAN card is required and can be placed in any suitable location. The significance of this result lies in allowing a network designer to make a suitable tradeoff between QoS of location determination and other application protocols while choosing a network topology. The combination of two methods reduced the error rate of locating a mobile node to a great extent. The results were being plotted on a given map using both the algorithms

[1]  Simon R. Saunders,et al.  Antennas and Propagation for Wireless Communication Systems , 1999 .

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

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

[4]  Gaetano Borriello,et al.  SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength , 2000 .

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

[6]  Kaveh Pahlavan,et al.  A new statistical model for site-specific indoor radio propagation prediction based on geometric optics and geometric probability , 2002, IEEE Trans. Wirel. Commun..

[7]  Eric W. Weisstein.  Normal Sum Distribution , 2000 .

[8]  H. Hashemi,et al.  The indoor radio propagation channel , 1993, Proc. IEEE.

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

[10]  M. Klepal,et al.  Wireless LAN Network Design: Site Survey or Propagation Modeling? , 2003 .

[11]  R.L. Hamilton,et al.  Ray tracing as a design tool for radio networks , 1991, IEEE Network.

[12]  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).