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.
Visible Light Indoor Positioning with a Single LED and Distributed Single-Element OIRS: An Iterative Approach with Adaptive Beam Steering
Politecnico di Bari（巴里理工大学）；Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)（意大利大学电信联合联盟）；National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科尔斯基基辅理工学院”）；University of Luxembourg（卢森堡大学）
