  With the emerge of the Internet of Things (IoT), localization within indoor
environments has become inevitable and has attracted a great deal of attention
in recent years. Several efforts have been made to cope with the challenges of
accurate positioning systems in the presence of signal interference. In this
paper, we propose a novel deep learning approach through Gradient Boosting
Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network
(AugBoost-ANN) for indoor localization applications as it trains over labeled
data. For this purpose, we propose an IoT architecture using a star network
topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth
Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in
an indoor environment. The dataset for the experiments is gathered in the real
world in different periods to match the real environments. Next, we address the
challenges of the AugBoost-ANN training which augments features in each
iteration of making a decision tree using a deep neural network and the
transfer learning technique. Experimental results show more than 8\%
improvement in terms of accuracy in comparison with the existing gradient
boosting and deep learning methods recently proposed in the literature, and our
proposed model acquires a mean location accuracy of 0.77 m.
