Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization.

Ahmed Salih Al-Khaleefa,Reza Firsandaya Malik,Mona Riza Mohd Esa,Yazan Aljeroudi,Mohd Riduan Ahmad,Mohammed Ahmed Jubair,Azmi Awang Md Isa

doi:10.3390/s19102397

Abstract

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

Highlights

In this present era of mobile technologies, a broad range of emerging and innovative applications are adopted to enhance communications
This study aims to tackle the cyclic dynamic behavior in indoor localization and to develop a machine-learning model based on online sequential extreme learning machine (ELM)
The evaluation performed in this study shows the superiority of the developed KP-online sequential extreme learning machine (OSELM)

Summary

Introduction

In this present era of mobile technologies, a broad range of emerging and innovative applications are adopted to enhance communications. Most of these applications connect individuals digitally and are used in transmitting and receiving data via access to a secure cloud environment or to an internal device [1]. Constructing systems that can provide acceptable position estimations within indoor environments is crucial. Services that are reliant on IPS are called indoor location-based services (ILBSs) [2] Examples of these services include ILBSs for tourists who require tools or location guiding services to locate important but unfamiliar places of interest [3].

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