Representation Learning for Sensor-based Device Pairing

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

Researchers

Research units

  • Hannover Medical School
  • Technische Universität Braunschweig

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.

Details

Original languageEnglish
Title of host publication2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
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

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