A Comparative Study of Machine-Learning Indoor Localization Using FM and DVB-T Signals in Real Testbed Environments

Yen Kai Cheng, Ronald Y. Chang, Ling Jyh Chen

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

3 Citations (Scopus)

Abstract

Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video broadcasting- terrestrial (DVB-T) signals in three real testbed environments. The consideration of readily available, ambient radio signals frees the need for dedicated radio transmitters. Noise-reduction techniques such as feature selection and ensemble learning are proposed in conjunction with SVM. Our results examine the performance comparisons between SVM and k-NN, as well as the performance comparisons of SVM-based methods incorporating different noise-reduction schemes, with noisy RSS data. Insights into the performance of learning- based localization schemes working with real database collected from real environments are provided.

Original languageEnglish
Title of host publication2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509059324
DOIs
Publication statusPublished - 2017 Nov 14
Externally publishedYes
Event85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, Australia
Duration: 2017 Jun 42017 Jun 7

Publication series

NameIEEE Vehicular Technology Conference
Volume2017-June
ISSN (Print)1550-2252

Conference

Conference85th IEEE Vehicular Technology Conference, VTC Spring 2017
Country/TerritoryAustralia
CitySydney
Period2017/06/042017/06/07

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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