Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation

Kai Liu*, Hao Zhang, Joseph K Y NG, Yusheng Xia, Liang Feng, Victor C.S. Lee, Sang H. Son

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

Original languageEnglish
Pages (from-to)898-908
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number3
DOIs
Publication statusPublished - Mar 2018

Scopus Subject Areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Fingerprint-based technique
  • indoor localization
  • transfer learning

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