<div class="csl-bib-body">
<div class="csl-entry">Spinoza Andreo, G., Dardavesis, I., de Jong, M., Kumar, P., Prihanggo, M., Triantafyllou, G., van der Vaart, N., & Verbree, E. (2021). Building Rhythms: Reopening the Workspace with Indoor Localisation. In A. Basiri, G. Gartner, & H. Huang (Eds.), <i>LBS 2021: Proceedings of the 16th International Conference on Location Based Services</i> (pp. 106–116). https://doi.org/10.34726/1756</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/18835
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dc.identifier.uri
https://doi.org/10.34726/1756
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dc.description
Published in “Proceedings of the 16th International Conference on
Location Based Services (LBS 2021)”, edited by Anahid Basiri, Georg
Gartner and Haosheng Huang, LBS 2021, 24-25 November 2021,
Glasgow, UK/online.
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dc.description.abstract
Indoor localisation methods are an essential part for the management of
COVID-19 restrictions, social distancing, and the flow of people in the
indoor environment. Moving towards an open work space in this scenario
requires effective real-time localisation services and tools, along with a
comprehensive understanding of the 3D indoor space. This project’s main
objective is to analyse how ArcGIS Indoors can be used with location
awareness methods to elaborate and develop space management tools for
COVID-
19 restrictions in order to reopen the workspace for TU Delft
Campus. This was accomplished by using six Arduino micro controllers,
which were programmed in C++ to scan all available Wi-Fi
fingerprints in
the east wing of the Faculty of Architecture and the Built Environment of
TU Delft and send over the data to an ArcGIS Indoor Information Model
(AIIM). The data stored on the AIIM is then accessed using the app on the
user’s Android device using REST Application Programming Interface (API) where a kNN based matching algorithm then identifies the location of
the user. The results show that the localisation is not consistent for rooms
that are directly above each other or share common access points. However,
when functioning to locate different tables inside a room, the system proved
to uniquely distinguish between the specific tables. As a result, we can
conclude that based on the size of the rooms, more Arduino devices should
be installed to achieve an ideal accuracy. Finally, recommendations are
made for the continuation of this research.
en
dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
indoor localisation system
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dc.subject
Wi-Fi fingerprinting
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dc.subject
ArcGis
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dc.subject
Indoor model
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dc.subject
machine learning
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dc.title
Building Rhythms: Reopening the Workspace with Indoor Localisation
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/1756
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dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
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dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
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dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
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dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
-
dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
-
dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
-
dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
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dc.contributor.affiliation
Delft University of Technology, Netherlands (the)
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dc.contributor.editoraffiliation
University of Glasgow, United Kingdom of Great Britain and Northern Ireland (the)