Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors

TL;DR

This paper explores machine learning techniques for indoor localization using mobile phone sensors, achieving high accuracy and speed improvements, and assessing real-world applicability with live experiments.

Contribution

It introduces a hybrid instance-based approach that enhances speed without sacrificing accuracy and evaluates dataset size effects on localization performance.

Findings

Mean error as low as 0.76 meters

Tenfold speed increase with hybrid approach

Effective in live, in-motion deployment

Abstract

In this paper we investigate the problem of localizing a mobile device based on readings from its embedded sensors utilizing machine learning methodologies. We consider a real-world environment, collect a large dataset of 3110 datapoints, and examine the performance of a substantial number of machine learning algorithms in localizing a mobile device. We have found algorithms that give a mean error as accurate as 0.76 meters, outperforming other indoor localization systems reported in the literature. We also propose a hybrid instance-based approach that results in a speed increase by a factor of ten with no loss of accuracy in a live deployment over standard instance-based methods, allowing for fast and accurate localization. Further, we determine how smaller datasets collected with less density affect accuracy of localization, important for use in real-world environments. Finally, we demonstrate that these approaches are appropriate for real-world deployment by evaluating their performance in an online, in-motion experiment.