Hybrid building/floor classification and location coordinates regression using a single-input and multi-output deep neural network for large-scale indoor localization based on Wi-Fi fingerprinting

Kyeong Soo Kim*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

16 Citations (Scopus)

Abstract

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.

Original languageEnglish
Title of host publicationProceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages196-201
Number of pages6
ISBN (Electronic)9781538691847
DOIs
Publication statusPublished - 26 Dec 2018
Event6th International Symposium on Computing and Networking Workshops, CANDARW 2018 - Takayama, Japan
Duration: 27 Nov 201830 Nov 2018

Publication series

NameProceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018

Conference

Conference6th International Symposium on Computing and Networking Workshops, CANDARW 2018
Country/TerritoryJapan
CityTakayama
Period27/11/1830/11/18

Keywords

  • Classification
  • Deep learning
  • Indoor localization
  • Neural networks
  • Regression
  • Wi-Fi fingerprinting

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