WiFi Based Distance Estimation Using Supervised Machine Learning

Kahraman Kostas, Rabia Yasa Kostas, Francisco Zampella, Firas Alsehly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication12th International Conference on Indoor Positioning and Indoor Navigation 2022
PublisherIEEE
ISBN (Electronic)9781728162188
DOIs
Publication statusPublished - 26 Oct 2022
Event12th International Conference on Indoor Positioning and Indoor Navigation 2022 - Beijing, China
Duration: 5 Sep 20227 Sep 2022

Conference

Conference12th International Conference on Indoor Positioning and Indoor Navigation 2022
Abbreviated titleIPIN 2022
Country/TerritoryChina
CityBeijing
Period5/09/227/09/22

Keywords

  • distance estimation indoor positioning
  • machine learning
  • RSSI
  • supervised learning
  • WiFi fingerprinting

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Fingerprint

Dive into the research topics of 'WiFi Based Distance Estimation Using Supervised Machine Learning'. Together they form a unique fingerprint.

Cite this