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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.

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In indoor positioning problems, GPS technology used in outdoor positioning needs to be improved due to the characteristic features of wireless signals. There currently needs to be a generally accepted standard method for indoor positioning. In this study, an ecosystem consisting of Beacon devices, Bluetooth intelligent devices, and Wi-Fi access points has been created to propose an effective indoor location determination method by using Wi-Fi and BLE technologies in a hybrid way. First, RSSI (Received Signal Strength Indicator) data were collected using the fingerprint method. Then, Kalman Filter and Savitzky Golay Filter are used in a hybrid manner to reduce the noise on the obtained signal data and make it more stable. In the first part, using the collected data from Wi-Fi and Beacon devices, the Non-linear least squares method (NLLS), including Levenberg-Marquardt (LM), is used for indoor tracking. In the second part, a fingerprinting-based approach is tested. K Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms estimate the area where the client is located. Each algorithm’s accuracy rate are calculated on different training and test data and presented.