Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

• Main Authors: Paolo Baronti, Paolo Barsocchi, Stefano Chessa, Fabio Mavilia, Filippo Palumbo
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Sensors
• Subjects: indoor localization; tracking; social interaction; Bluetooth Low Energy; dataset
• Online Access: https://www.mdpi.com/1424-8220/18/12/4462

Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.