Bluetooth Low Energy (BLE) is a wireless technology for exchanging data, over short distances, designed for the Internet-of-Things era. As widely supported by wearable devices, BLE has the potential to become an alternative for indoor-localization and proximity sensing. The aim of this work was to perform a thorough characterization of the RSSI-distance relationship under controlled conditions using two BLE devices. Four calibration models underwent to a comparative evaluation analysis. The best results were obtained using a polynomial model with a mean distance percentage error equal to 25.7% (0.4 m) in the range 0-3 m. An overall improvement of 14.3% (0.24 m) in the distance estimate compared to the exponential model commonly adopted in the literature was reported.

Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models / Bertuletti, Stefano; Cereatti, Andrea; Caldara, Michele; Galizzi, Michael; DELLA CROCE, Ugo. - (2016), pp. 506-510. (Intervento presentato al convegno 11th IEEE Sensors Applications Symposium, SAS 2016 tenutosi a Catania; Italy) [10.1109/SAS.2016.7479899].

Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models

Bertuletti Stefano
;
Della Croce Ugo
2016

Abstract

Bluetooth Low Energy (BLE) is a wireless technology for exchanging data, over short distances, designed for the Internet-of-Things era. As widely supported by wearable devices, BLE has the potential to become an alternative for indoor-localization and proximity sensing. The aim of this work was to perform a thorough characterization of the RSSI-distance relationship under controlled conditions using two BLE devices. Four calibration models underwent to a comparative evaluation analysis. The best results were obtained using a polynomial model with a mean distance percentage error equal to 25.7% (0.4 m) in the range 0-3 m. An overall improvement of 14.3% (0.24 m) in the distance estimate compared to the exponential model commonly adopted in the literature was reported.
2016
11th IEEE Sensors Applications Symposium, SAS 2016
Bluetooth Low Energy; distance estimation; Indoor-localization; RSSI; wearable devices; Electrical and Electronic Engineering; Instrumentation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models / Bertuletti, Stefano; Cereatti, Andrea; Caldara, Michele; Galizzi, Michael; DELLA CROCE, Ugo. - (2016), pp. 506-510. (Intervento presentato al convegno 11th IEEE Sensors Applications Symposium, SAS 2016 tenutosi a Catania; Italy) [10.1109/SAS.2016.7479899].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1188665
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