LAUSR

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.