Journals / CMC / Vol.68, No.2
Table of Content

Research Article

BEST PAPER 2021

Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones

Imran Ashraf, Soojung Hur, Yousaf Bin Zikria, Yongwan Park*
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Korea
* Corresponding Author: Yongwan Park. Email:
(This article belongs to this Special Issue: Reinforcement Learning Based solutions for Next-Generation Wireless Networks Coexistence)

Abstract

Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors: Smartphone heterogeneity and smaller data lengths. The use of multifarious smartphones cripples the performance of such approaches owing to the variability of the magnetic field data. In the same vein, smaller lengths of magnetic field data decrease the localization accuracy substantially. The current study proposes the use of multiple neural networks like deep neural network (DNN), long short term memory network (LSTM), and gated recurrent unit network (GRN) to perform indoor localization based on the embedded magnetic sensor of the smartphone. A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user. Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity, this study utilizes the normalized magnetic field data for this purpose. Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices, i.e., LG G7, Galaxy S8, and LG Q6. Experiments are performed during different times of the day to analyze the impact of time variability. Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy. Performance comparison with three approaches reveals that the proposed approach outperforms them in mean, 50%, and 75% error even using a lesser amount of magnetic field data than those of other approaches.

Keywords

Indoor localization; magnetic field data; long short term memory network; data normalization; gated recurrent unit network; deep learning
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