Representation learning for sensor-based device pairing

Publikationstyp
Conference Paper
Date Issued
2018-10-08
Sprache
English
Author(s)
Nguyen, Ngu  
Jähne-Raden, Nico  
Kulau, Ulf  
Sigg, Stephan  
TORE-URI
http://hdl.handle.net/11420/10841
Start Page
508
End Page
511
Article Number
8480412
Citation
IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018: 8480412, 508-511 (2018-10-02)
Contribution to Conference
16th IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018  
Publisher DOI
10.1109/PERCOMW.2018.8480412
Scopus ID
2-s2.0-85056484633
Publisher
IEEE
The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.
DDC Class
600: Technik