Wi-Fi Fingerprint localisation using Density-based Clustering for public spaces: A case study in a shopping mall

Sian Lun Lau, Cornelius Toh, Yasir Saleem

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

5 Citations (SciVal)

Abstract

Indoor localisation is to-date still an active research area. This paper presents a case study on a localisation technique using Wi-Fi fingerprints built from radio information collected using commercially-off-the-shelf smartphones. The Wi-Fi fingerprints are built using density-based clustering-based algorithms. The investigation is carried out on normal operation scenarios, where a normal crowd was present during the experiments. A simplified version of the clustering algorithm, the Simplified Fingerprint Density-based Clustering Algorithm (SFDCA), is proposed, implemented as well as evaluated with a comparison to an existing indoor localisation algorithm called Density-based Cluster Combined Algorithm (DCCLA). Furthermore, a few changes are proposed and evaluated for the recognition algorithm. This paper discusses the obtained results, observations and issues faced in the case study.

Original languageEnglish
Title of host publicationProceedings of the 2016 6th International Conference
Subtitle of host publicationCloud System and Big Data Engineering, Confluence 2016
EditorsAbhay Bansal, Abhishek Singhal
PublisherIEEE Press
Pages356-360
Number of pages5
ISBN (Electronic)9781467382021
ISBN (Print)9781467382038
DOIs
Publication statusPublished - 09 Jul 2016
Externally publishedYes

Publication series

NameProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016

Keywords

  • Wi-Fi Fingerprint
  • Indoor Localisation
  • Density-based Clustering

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