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Santos, R., Leonardo, R., Barandas, M., Moreira, D., Rocha, T., Alves, P....Gamboa, H. (2021). Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings. IEEE Access. 9, 31143-31160
R. Santos et al., "Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings", in IEEE Access, vol. 9, pp. 31143-31160, 2021
@article{santos2021_1725560343922, author = "Santos, R. and Leonardo, R. and Barandas, M. and Moreira, D. and Rocha, T. and Alves, P. and Oliveira, J. and Gamboa, H.", title = "Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings", journal = "IEEE Access", year = "2021", volume = "9", number = "", doi = "10.1109/ACCESS.2021.3060123", pages = "31143-31160", url = "https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639" }
TY - JOUR TI - Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings T2 - IEEE Access VL - 9 AU - Santos, R. AU - Leonardo, R. AU - Barandas, M. AU - Moreira, D. AU - Rocha, T. AU - Alves, P. AU - Oliveira, J. AU - Gamboa, H. PY - 2021 SP - 31143-31160 SN - 2169-3536 DO - 10.1109/ACCESS.2021.3060123 UR - https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 AB - 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. ER -