2024-03-29T12:56:31Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1458322024-03-13T09:52:51Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134243
A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building
Rana, Soumya Prakash
Prieto Tejedor, Javier
Dey, Maitreyee
Dudley, Sandra
Corchado RodrÃguez, Juan Manuel
Indoor localization
Received signal strength indicator
Fingerprinting
Machine learning
1203.17 Informática
[EN] Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.
2021-05-13T11:42:37Z
2021-05-13T11:42:37Z
2018-11-04
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Rana, S., Prieto, J., Dey, M., Dudley, S. and Corchado, J., 2018. A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building. Sensors, 18(11), p.3766. https://doi.org/10.3390/s18113766
1424-8220
http://hdl.handle.net/10366/145832
10.3390/s18113766
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess