| Reference : Characterizing Driving Environments Through Bluetooth Discovery |
| Scientific congresses, symposiums and conference proceedings : Unpublished conference | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/32185 | |||
| Characterizing Driving Environments Through Bluetooth Discovery | |
| English | |
Bronzi, Walter  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Faye, Sébastien [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Frank, Raphaël [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Oct-2017 | |
| Yes | |
| International | |
| The 8th International Conference on ICT Convergence - ICTC 2017 | |
| from 18-10-2017 to 20-10-2017 | |
| [en] Context-aware Services ; Bluetooth ; Bluetooth Low Energy ; Sensing Systems ; Machine Learning | |
| [en] Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore, having knowledge of nearby Bluetooth devices, both inside and outside other vehicles, can provide the listening vehicles with enough data to learn about their environment. In this paper, we collect and analyze a dataset of Bluetooth Classic (BC) and Low Energy (BLE) discoveries. We evaluate their respective characteristics and ability to provide context-aware information from a vehicular perspective. By taking a look at data about the encountered devices, such as GPS location, quantity, quality of signal and device class information, we infer distinctive behaviors between BC and BLE relative to context and application. For this purpose, we propose a set a features to train a classifier for the recognition of different driving environments (i.e. road classes) from Bluetooth discovery data alone. Comparing the performance of our classifier with different sampling parameters, the presented results indicate that, with our feature selection, we are able to predict with reasonable confidence up to three classes (Highway, City, Extra-Urban) by using only discovery data and no geographical information. This outcome gives promising results targeted at low energy and privacy-friendly applications and can open up a wide range of research directions. | |
| http://hdl.handle.net/10993/32185 |
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