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Sensors (Basel). 2019 Oct 19;19(20). pii: E4550. doi: 10.3390/s19204550.

An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons.

Author information

1
Department of Management Science and Technology, Athens University of Economics and Business, 76 Patission Str., 10434 Athens, Greece. stavrouv@aueb.gr.
2
Department of Informatics and Telematics, Harokopio University of Athens, 9 Omirou Str., 17778 Athens, Greece. cleobar@hua.gr.
3
Department of Business Administration, University of West Attica, Campus 2, 250 Thivon & P. Ralli, 12241 Athens, Greece. dpapak@uniwa.gr.
4
Department of Management Science and Technology, Athens University of Economics and Business, 76 Patission Str., 10434 Athens, Greece. k.pramatari@aueb.gr.

Abstract

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers' moving) that impede indoor localization. To the best of the authors' knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers' shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

KEYWORDS:

BLE Beacons; Bluetooth Low Energy; ensemble filter; fingerprinting; indoor positioning; retail store

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