Artigo em revista científica Q1
Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings
Ricardo Santos (Santos, R.); Ricardo Leonardo (Leonardo, R.); Marilia Barandas (Barandas, M.); Dinis Moreira (Moreira, D.); Tiago Rocha (Rocha, T.); Pedro Alves (Alves, P.); João Pedro Oliveira (Oliveira, J.); Hugo Gamboa (Gamboa, H.); et al.
Título Revista
IEEE Access
Ano (publicação definitiva)
2021
Língua
Inglês
País
Estados Unidos da América
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Abstract/Resumo
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.
Agradecimentos/Acknowledgements
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Palavras-chave
Wireless fidelity,Crowdsourcing,Buildings,Trajectory,Sensors,Smart phones,IP networks,Fingerpriting,Indoor location,Inertial tracking,Magnetic field,Multi-storey,Unsupervised,Wi-Fi
  • Ciências da Computação e da Informação - Ciências Naturais
  • Outras Ciências Naturais - Ciências Naturais
  • Engenharia Civil - Engenharia e Tecnologia
  • Engenharia Eletrotécnica, Eletrónica e Informática - Engenharia e Tecnologia
  • Engenharia dos Materiais - Engenharia e Tecnologia
Registos de financiamentos
Referência de financiamento Entidade Financiadora
POCI-01-0247-FEDER-033479 Comissão Europeia
UIDB/04466/2020 Fundação para a Ciência e a Tecnologia