@inproceedings{f0db2e4478b1447bb58abbcea9ee6be6,
title = "Wi-Fi Fingerprint localisation using Density-based Clustering for public spaces: A case study in a shopping mall",
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.",
keywords = "Wi-Fi Fingerprint, Indoor Localisation, Density-based Clustering",
author = "Lau, {Sian Lun} and Cornelius Toh and Yasir Saleem",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",
year = "2016",
month = jul,
day = "9",
doi = "10.1109/confluence.2016.7508143",
language = "English",
isbn = "9781467382038",
series = "Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016",
publisher = "IEEE Press",
pages = "356--360",
editor = "Abhay Bansal and Abhishek Singhal",
booktitle = "Proceedings of the 2016 6th International Conference",
address = "United States of America",
}