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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

41 Scopus Citations
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Author(s)

  • Kai Liu
  • Hao Zhang
  • Joseph Kee-Yin Ng
  • Yusheng Xia
  • Liang Feng
  • Sang H. Son

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)898-908
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume14
Issue number3
Online published8 Sep 2017
Publication statusPublished - Mar 2018

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.

Research Area(s)

  • Fingerprint-based technique, indoor localization, transfer learning

Citation Format(s)

Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning : Design, Implementation, and Evaluation. / Liu, Kai; Zhang, Hao; Ng, Joseph Kee-Yin; Xia, Yusheng; Feng, Liang; Lee, Victor C. S.; Son, Sang H.

In: IEEE Transactions on Industrial Informatics, Vol. 14, No. 3, 03.2018, p. 898-908.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review