Optimal Wireless Access Point Placement for Location-Dependent Services

In mobile computing scenarios, context-aware applications are more effective in relieving from the mobile user the burden of introducing information that can be automatically derived from the environment. In particular, the physical position of the mobile system (and hence of the user) is fundamental for many types of applications. User position estimation methods based on strength of the radio signals received from multiple wireless access points have been recently proposed and implemented by several independent research groups. In this paper a new approach to wireless access point placement is proposed. While previous proposals focus on optimal coverage aimed at connectivity, the proposed method integrates coverage requirements with the reduction of the error of the user position estimate. In particular, this paper proposes a mathematical model of user localization error based on the variability of signal strength measurements. This model has been designed to be independent from the actual localization technique, therefore it is only based on generic assumptions on the behavior of the localization algorithm employed. The proposed error model is used by local search heuristic techniques, such as local search, a prohibition-based variation and simulated annealing. Near-optimal access point placements are computed for various kinds of optimization criteria: localization error minimization, signal coverage maximization, a mixture of the two. The different criteria are not expected to be compatible: maximizing signal coverage alone can lead to degradation of the average positioning error, and vice versa. Some experiments have been dedicated to quantify this phenomenon and to introduce possible trade-offs.

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

[2]  Margaret H. Wright,et al.  Optimization methods for base station placement in wireless applications , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[3]  Lajos Nagy,et al.  Indoor base station location optimization using genetic algorithms , 2000, 11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No.00TH8525).

[4]  Hisashi Kobayashi,et al.  Signal strength based indoor geolocation , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[5]  Mauro Brunato,et al.  Transparent Location Fingerprinting for Wireless Services , 2002 .

[6]  Roberto Battiti,et al.  The Reactive Tabu Search , 1994, INFORMS J. Comput..

[7]  Li Xiu-he.  Signal Strength Based Indoor Geolocation , 2004 .

[8]  Hanif D. Sherali,et al.  Optimal location of transmitters for micro-cellular radio communication system design , 1996, IEEE J. Sel. Areas Commun..

[9]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[10]  Andrew R Nix,et al.  The automatic location of base-stations for optimised cellular coverage: a new combinatorial approach , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

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

[12]  Steve Hurley.  Automatic base station selection and configuration in mobile networks , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

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

[14]  Henry Tirri,et al.  A Statistical Modeling Approach to Location Estimation , 2002, IEEE Trans. Mob. Comput..

[15]  M. Unbehaun,et al.  Coverage planning for outdoor wireless LAN systems , 2002, 2002 International Zurich Seminar on Broadband Communications Access - Transmission - Networking (Cat. No.02TH8599).

[16]  Alex Hills,et al.  Large-scale wireless LAN design , 2001, IEEE Commun. Mag..

[17]  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).

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

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