Location determination of mobile device for indoor WLAN application using neural network

Due to increases in the use of wireless local networks (WLANs) and mobile computing devices, and the popularity of location-based services, determining the location of a device at any time is important. Although numerous GPS-based applications have been developed and successfully utilized in various fields, they have serious limitations. Specify applicable to outdoor applications. Therefore, to develop and approach that determines the location of what that is suitable for indoor environments is necessary. This study presents a novel location determination mechanism that uses an indoor WLAN and back-propagation neural network (BPN). A museum is taken as an example indoor environment. Location determination is achieved using the combined strengths of 802.11b wireless access signals. With a significant numerous access points (APs) installed in the museum, hand-held devices can sense the strengths of the signals from all access points to which the devices can connect. Using a back-propagation network, device locations can be estimated with sufficient accuracy. A novel adaptive algorithm is implemented.

[1]  Bernard Widrow,et al.  Layered neural nets for pattern recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[2]  R.J.F. Dow,et al.  Neural net pruning-why and how , 1988, IEEE 1988 International Conference on Neural Networks.

[3]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[4]  Robert L. Smith,et al.  Simulated annealing for constrained global optimization , 1994, J. Glob. Optim..

[5]  Eric R. Ziegel,et al.  Neural Networks in Computer Intelligence@@@Fundamentals of Neural Networks , 1995 .

[6]  Brijesh Verma,et al.  Fast training of multilayer perceptrons , 1997, IEEE Trans. Neural Networks.

[7]  Bahram Alidaee,et al.  Global optimization for artificial neural networks: A tabu search application , 1998, Eur. J. Oper. Res..

[8]  Randall S. Sexton,et al.  Comparing backpropagation with a genetic algorithm for neural network training , 1999 .

[9]  Ingoo Han,et al.  Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index , 2000 .

[10]  Hubert Cardot,et al.  A Neural Network Architecture for Data Classification , 2001, Int. J. Neural Syst..

[11]  A. Yamazaki,et al.  Optimization of neural network weights and architectures for odor recognition using simulated annealing , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[12]  Antanas Verikas,et al.  Feature selection with neural networks , 2002, Pattern Recognit. Lett..

[13]  Ranadhir Ghosh,et al.  A Hierarchical Method for Finding Optimal Architecture and Weights Using Evolutionary Least Square Based Learning , 2003, Int. J. Neural Syst..

[14]  James Scott,et al.  User-Friendly Surveying Techniques for Location-Aware Systems , 2003, UbiComp.

[15]  Juan Julián Merelo Guervós,et al.  Evolving Multilayer Perceptrons , 2000, Neural Processing Letters.

[16]  B. Zovko-Cihlar,et al.  WLAN location determination model based on the artificial neural networks , 2005, 47th International Symposium ELMAR, 2005..

[17]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[18]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[19]  Randall S. Sexton,et al.  Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem , 2006, Eur. J. Oper. Res..

[20]  Mineichi Kudo,et al.  Non-parametric classifier-independent feature selection , 2006, Pattern Recognit..

[21]  K. Kaemarungsi,et al.  Distribution of WLAN received signal strength indication for indoor location determination , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

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

[23]  Shuo-Yan Chou,et al.  A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks , 2008, Expert Syst. Appl..