WiFi Based Distance Estimation Using Supervised Machine Learning

TL;DR

This paper presents a machine learning approach to improve WiFi fingerprint-based indoor distance estimation, using feature selection and testing across diverse datasets for venue-independent accuracy.

Contribution

It introduces a novel machine learning framework with feature selection for more accurate, venue-independent WiFi distance estimation in indoor positioning.

Findings

Machine learning models outperform traditional methods in distance estimation accuracy.

Feature selection enhances model performance and generalization.

Models demonstrate robustness across multiple unseen datasets.

Abstract

In recent years WiFi became the primary source of information to locate a person or device indoor. Collecting RSSI values as reference measurements with known positions, known as WiFi fingerprinting, is commonly used in various positioning methods and algorithms that appear in literature. However, measuring the spatial distance between given set of WiFi fingerprints is heavily affected by the selection of the signal distance function used to model signal space as geospatial distance. In this study, the authors proposed utilization of machine learning to improve the estimation of geospatial distance between fingerprints. This research examined data collected from 13 different open datasets to provide a broad representation aiming for general model that can be used in any indoor environment. The proposed novel approach extracted data features by examining a set of commonly used signal distance metrics via feature selection process that includes feature analysis and genetic algorithm. To demonstrate that the output of this research is venue independent, all models were tested on datasets previously excluded during the training and validation phase. Finally, various machine learning algorithms were compared using wide variety of evaluation metrics including ability to scale out the test bed to real world unsolicited datasets.