Representation Learning for Sensor-based Device Pairing

Ngu Nguyen, Nico Jähne-Raden, Ulf Kulau, Stephan Sigg

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

123 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
PublisherIEEE
Pages508-511
Number of pages4
ISBN (Electronic)9781538632277
DOIs
Publication statusPublished - 2 Oct 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Pervasive Computing and Communications Workshops - Athens, Greece
Duration: 19 Mar 201823 Mar 2018

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications Workshops
Abbreviated titlePerCom Workshops
CountryGreece
CityAthens
Period19/03/201823/03/2018

Fingerprint Dive into the research topics of 'Representation Learning for Sensor-based Device Pairing'. Together they form a unique fingerprint.

  • Cite this

    Nguyen, N., Jähne-Raden, N., Kulau, U., & Sigg, S. (2018). Representation Learning for Sensor-based Device Pairing. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 (pp. 508-511). [8480412] IEEE. https://doi.org/10.1109/PERCOMW.2018.8480412