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Publication Detailed Description
Journal Title
IEEE Access
Year (definitive publication)
2021
Language
English
Country
United States of America
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Abstract
The number of available indoor location solutions has been growing, however with insufficient
precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on
ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the timeconsuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper
proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings,
leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data
from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply
a multimodal approach that joins inertial data, local magnetic field and Wi-Fi signals to construct highly
accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through
Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories
obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements.
Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping
to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory
into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical
locations are finally obtained. Experimental results from an office and a university building show that this
solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for
fingerprinting-based solutions automatic setup.
Acknowledgements
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Keywords
Wireless fidelity,Crowdsourcing,Buildings,Trajectory,Sensors,Smart phones,IP networks,Fingerpriting,Indoor location,Inertial tracking,Magnetic field,Multi-storey,Unsupervised,Wi-Fi
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
- Other Natural Sciences - Natural Sciences
- Civil Engineering - Engineering and Technology
- Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
- Materials Engineering - Engineering and Technology
Funding Records
Funding Reference | Funding Entity |
---|---|
POCI-01-0247-FEDER-033479 | Comissão Europeia |
UIDB/04466/2020 | Fundação para a Ciência e a Tecnologia |