Next Article in Journal
Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection
Next Article in Special Issue
Advanced Heterogeneous Feature Fusion Machine Learning Models and Algorithms for Improving Indoor Localization
Previous Article in Journal
An SPR Sensor Chip Based on Peptide-Modified Single-Walled Carbon Nanotubes with Enhanced Sensitivity and Selectivity in the Detection of 2,4,6-Trinitrotoluene Explosives
Previous Article in Special Issue
Integrating Moving Platforms in a SLAM Agorithm for Pedestrian Navigation
Article

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

1
Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
2
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4462; https://doi.org/10.3390/s18124462
Received: 20 November 2018 / Revised: 12 December 2018 / Accepted: 13 December 2018 / Published: 17 December 2018
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
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. View Full-Text
Keywords: indoor localization; tracking; social interaction; Bluetooth Low Energy; dataset indoor localization; tracking; social interaction; Bluetooth Low Energy; dataset
Show Figures

Graphical abstract

MDPI and ACS Style

Baronti, P.; Barsocchi, P.; Chessa, S.; Mavilia, F.; Palumbo, F. Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction. Sensors 2018, 18, 4462. https://doi.org/10.3390/s18124462

AMA Style

Baronti P, Barsocchi P, Chessa S, Mavilia F, Palumbo F. Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction. Sensors. 2018; 18(12):4462. https://doi.org/10.3390/s18124462

Chicago/Turabian Style

Baronti, Paolo, Paolo Barsocchi, Stefano Chessa, Fabio Mavilia, and Filippo Palumbo. 2018. "Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction" Sensors 18, no. 12: 4462. https://doi.org/10.3390/s18124462

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop