In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.
An Inductive System Monitoring Approach for GNSS Activation / Abghari, S.; Boeva, V.; Casalicchio, E.; Exner, P.. - 647:(2022), pp. 437-449. (Intervento presentato al convegno 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022 tenutosi a grc) [10.1007/978-3-031-08337-2_36].
An Inductive System Monitoring Approach for GNSS Activation
Casalicchio E.;
2022
Abstract
In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
