Node Localization using Fractal Signal Preprocessing and Artificial Neural Network
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This thesis proposes an integrated artificial neural network based approach to classify the position of a wireless device in an indoor protected area. Our experiments are conducted in two different types of interference affected indoor locations. We found that the environment greatly influences the received signal strength. We realized the need of incorporating a complexity measure of the Wi-Fi signal as additional information in our localization algorithm. The inputs to the integrated artificial neural network were comprised of an integer dimension representation and a fractional dimension representation of the Wi-Fi signal. The integer dimension representation consisted of the raw signal strength, whereas the fractional dimension consisted of a variance fractal dimension of the Wi-Fi signal. The results show that the proposed approach performed 8.7% better classification than the “one dimensional input” ANN approach, achieving an 86% correct classification rate. The conventional Trilateration method achieved only a 47.97% correct classification rate.