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Fingerprint localization technique is an effective positioning technique to determine the object locations by using radio signal strength (RSSI), values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localization technique is deployed by using classical k-NN method to determine the unknown object locations. Additionally, several artificial neural networks, (ANN), are employed, using fingerprint data, such as SFFNN, MFFNN, MBPNN, GRNN and DNN to determine the same unknown object locations. Fingerprint database is built by RSSI measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-NN method and it was found that DNN was the best neural network technique providing the maximum positioning accuracies.
PAPER REVISED: 2018-10-30
PAPER ACCEPTED: 2018-11-16
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THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 1, PAGES [S99 - S111]
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© 2019 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence