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

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

6 Dyfyniadau (Scopus)

Crynodeb

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.

Iaith wreiddiolSaesneg
TeitlProceedings of the 2016 6th International Conference
Is-deitlCloud System and Big Data Engineering, Confluence 2016
GolygyddionAbhay Bansal, Abhishek Singhal
CyhoeddwrIEEE Press
Tudalennau356-360
Nifer y tudalennau5
ISBN (Electronig)9781467382021
ISBN (Argraffiad)9781467382038
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 09 Gorff 2016
Cyhoeddwyd yn allanolIe

Cyfres gyhoeddiadau

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

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