Indoor fingerprint localization in WSN environment based on neural network

The indoor localization is an actual problem because there are more and more application areas. New technical solutions are available, which have contributed to the indoor localization researches. In this work Fingerprint (FP) localization methodology applied in the experimental indoor environment is presented. The Wireless Sensor Network technology (WSN) is used in real environment, which provided the necessary measurement results to the FP localization. For the processing Received Signal Strength Indicator (RSSI) and for determining the position the neural network model is used. The RSSI values used for the learning of the neural network are preprocessed (mean, median, standard deviation) in order to increase the accuracy of the system. The type of the neural network is a feed-forward network. During obtain learning different algorithms were applied. The mean square error of Euclidean distance between calculated and real coordinates and the histogram of precision were used to determine the accuracy of the neural network.