Indoor Positioning Systems (IPSs) hold significant potential for enhancing visitor experiences in cultural heritage. By enabling personalized navigation, efficient artifacts organization, and better interaction with exhibits, IPSs can transform how individuals engage with museums, galleries and libraries. However, these institutions face several challenges in implementing IPSs, including environmental constraints, technical limits, and limited experimentation. Received Signal Strength (RSS)-based approaches using Bluetooth Low Energy (BLE) and WiFi have emerged as preferred solutions due to their non-invasive nature and minimal infrastructure requirements. Nevertheless, the lack of publicly available RSS datasets that specifically reflect museum environments presents a substantial barrier to developing and evaluating positioning algorithms designed for the intricate spatial characteristics typical of cultural heritage sites. To address this limitation, we present BAR, a novel RSS dataset collected in front of 90 artworks across 13 museum rooms using two different platforms, i.e., Android and iOS. We provide an advanced position classification baseline taking advantage of a proximity-based method and k-NN algorithms. In our experiments, room-level accuracy ranges from 92.36 % to 99.97 % and artwork Top-3 from 75.15 % to 98.26 %, depending on the configuration, with cross-platform scenarios revealing significant challenges.
Ferrato, A., Gasparetti, F., Limongelli, C., Mastandrea, S., Sansonetti, G., Torres-Sospedra, J. (2025). Cross-platform Smartphone Positioning at Museums. In Proceedings of the 15th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2025 (pp.1-6). New York City, New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/ipin66788.2025.11212937].
Cross-platform Smartphone Positioning at Museums
Ferrato, Alessio
;Gasparetti, Fabio;Limongelli, Carla;Mastandrea, Stefano;Sansonetti, Giuseppe;
2025-01-01
Abstract
Indoor Positioning Systems (IPSs) hold significant potential for enhancing visitor experiences in cultural heritage. By enabling personalized navigation, efficient artifacts organization, and better interaction with exhibits, IPSs can transform how individuals engage with museums, galleries and libraries. However, these institutions face several challenges in implementing IPSs, including environmental constraints, technical limits, and limited experimentation. Received Signal Strength (RSS)-based approaches using Bluetooth Low Energy (BLE) and WiFi have emerged as preferred solutions due to their non-invasive nature and minimal infrastructure requirements. Nevertheless, the lack of publicly available RSS datasets that specifically reflect museum environments presents a substantial barrier to developing and evaluating positioning algorithms designed for the intricate spatial characteristics typical of cultural heritage sites. To address this limitation, we present BAR, a novel RSS dataset collected in front of 90 artworks across 13 museum rooms using two different platforms, i.e., Android and iOS. We provide an advanced position classification baseline taking advantage of a proximity-based method and k-NN algorithms. In our experiments, room-level accuracy ranges from 92.36 % to 99.97 % and artwork Top-3 from 75.15 % to 98.26 %, depending on the configuration, with cross-platform scenarios revealing significant challenges.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
