Building Rhythms

Building Rhythms: Reopening the workspace with indoor localisation

de Jong, Michiel (TU Delft Architecture and the Built Environment)
Triantafyllou, Giorgos (TU Delft Architecture and the Built Environment)
Spinoza Andreo, Guilherme (TU Delft Architecture and the Built Environment)
Dardavesis, Ioannis (TU Delft Architecture and the Built Environment)
Kumar, Pratyush (TU Delft Architecture and the Built Environment)
Maundri Prihanggo, Maundri (TU Delft Architecture and the Built Environment)

Verbree, E. (mentor)
van der Vaart, C.G. (graduation committee)
Valks, B. (graduation committee)

Delft University of Technology

Geomatics

Synthesis Project 2021

2021-07-01

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.

Indoor localisation system
Wi-Fi fingerprinting
ArcGIS
Indoor model
Machine learning

http://resolver.tudelft.nl/uuid:060d104f-bce9-4608-9aa9-a73132317254

Student theses

student report

© 2021 Michiel de Jong, Giorgos Triantafyllou, Guilherme Spinoza Andreo, Ioannis Dardavesis, Pratyush Kumar, Maundri Maundri Prihanggo