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.
翻译:随着物联网(IoT)的出现,室内环境的本地化成为不可避免,近年来引起了大量注意。在信号干扰的情况下,我们作出了一些努力来应对精确定位系统的挑战。在本文件中,我们提议通过使用人工神经网络(AugBoost-ANNN)在室内本地化应用中利用人工神经网络(AugBoost-ANNN)进行人工本地化培训,通过渐进式推介增强功能增强的渐进式增强功能,在室内本地化应用中采用人工神经网络(AugBoost-ANNN),进行室内本地化应用。为此,我们建议使用恒星网络的地形学来收集蓝牙低能信号强度指标(RSSI)模块,在室内环境中采用“草莓Pi”作为接入点,从而应对准确定位系统的挑战。实验数据集在不同时期收集,以适应真实环境。接下来,我们要应对AugBoost-ANNN培训的挑战,这种培训增强了使用深层神经网络和转移学习技术来做决策树的特征。实验结果显示,从最近获得的精确度上提高了我们提议的模型,在深度学习程度方面的精确度。7 。