Bluetooth Indoor Localization With Gaussian–Bernoulli Restricted Boltzmann Machine Plus Liquid State Machine
Xiaofeng Yang, Zhao Wu, Qiuming Zhang
Abstract
The location information of sensors and devices plays an important role in Internet of Things (IoT). Low cost and energy efficient Bluetooth Low Energy (BLE)-based localization solutions are ideal for extensive use in IoT applications. The main challenge is to combat the signal fluctuation in non-line-of-sight (NLOS) propagation. Most Machine Learning (ML) algorithms available in the literature to address this issue belong to classification and batch learning. This article proposes a novel online learning algorithm for BLE localization based on Gaussian–Bernoulli Restricted Boltzmann Machine (GBRBM) plus Liquid State Machine (LSM), which learns the training data one-by-one. Unsupervised GBRBM is able to extract sound patterns of fluctuated RSS inputs, and LSM manages to map the patterns to real-time position estimation. Extensive experimental results demonstrate the superiority of the proposed method over other state-of-the-art batch learning methods in terms of localization accuracy and complexity.