Distributed Growing Radial Basis Function Neural Networks for Location Estimation in Indoor Wireless Networks

Location aware computing is popularized and use of location information has become important due to huge application of mobile computing devices and local area wireless networks. To serve us well, mobile computing applications need to know the physical location of things so that they can record them and report them to us. Therefore in the future ubiquitous services, location estimation will be a key technology. This paper present distributed growing radial basis function neural networks (DGRBFNN) for location estimation of mobile device in wireless networks. DGRBFNN partitions the location space into clusters on the basis of availability of signals from various access points, and employ separate neural network architecture for each cluster to estimate the location of mobile device in indoor wireless networks. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.