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Performance analysis of machine learning and deep learning classification methods for indoor localization in Internet of things environment
Author(s) -
Turgut Zeynep,
Üstebay Serpil,
Ali Aydın Muhammed,
Gürkaş Aydın Gülsüm Zeynep,
Sertbaş Ahmet
Publication year - 2019
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3705
Subject(s) - computer science , artificial intelligence , field (mathematics) , autoencoder , machine learning , fingerprint (computing) , bluetooth , indoor positioning system , internet of things , deep learning , real time computing , data mining , wireless , embedded system , telecommunications , accelerometer , mathematics , pure mathematics , operating system
The ability to detect the mobile user's location with high precision in indoor networks is particularly difficult due to the environmental characteristics and high dynamics of the indoor networks. The use of different technologies in the system to be developed to determine the position with high accuracy is important for overcoming the disadvantage(s) of any technology. To design a high‐precision indoor positioning method, it is important create an Internet of things (IoT) environment which uses hybrid technologies. The proposed system has been tested on an IoT environment by using two phases: preproccessing and localization. The corresponding environment is an original IoT environment, which allows to collect a hybrid dataset consists of WiFi, Bluetooth Low Energy, and Earth's magnetic field values. HALICDB dataset is created in the related ecosystem. To make a comparison with HALICDB, RFKONDB is used to create RFKON_HIBRID with similar signal values. The signal values obtained from the datasets are first passed through a particle filter. In the localization phase, a reference signal map is obtained for the detection of the moving objects in the indoor areas that are in the IoT environment using the fingerprint method. In the offline phase, the different machine learning methods are used in fingerprint maps for classification. It is seen that the highest accuracy is received through stacked sparse autoencoder from the deep learning methods due to the overcomplete network structure offered by the IoT environment on both datasets.