Stammdaten

Titel: Resilient Self-Calibration in Distributed Visual Sensor Networks
Untertitel:
Kurzfassung:

Today, camera networks are pervasively used in

smart environments such as intelligent homes, industrial automa-

tion or surveillance. These applications often require cameras to

be aware of their spatial neighbors or even to operate on a

common ground plane. A major concern in the use of sensor

networks in general is their robustness and reliability even in

the presence of attackers.

This paper addresses the challenge of detecting malicious

nodes during the calibration phase of camera networks. Such

a resilient calibration enables robust and reliable localization

results and the elimination of attackers right after the network

deployment. Specifically, we consider the problem of identifying

subverted nodes which manipulate calibration data and can not

be detected by standard cryptographic methods. The experiments

in our network show that our self-calibration algorithm enables

location-unknown cameras to successfully detect malicious nodes

while autonomously calibrating the network.

Schlagworte: Visual sensor networks Security and privacy issues Distributed trust generation Self-calibration
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Proceedings of the International Conference on Distributed Computing in Sensor Systems Workshops
Proceedings of the International Conference on Distributed Computing in Sensor Systems Workshops
zur Publikation
 ( IEEE; )
Erscheinungsdatum: 2019
Titel der Serie: -
Bandnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 271 - 278

Identifikatoren

ISBN:
  • 978-1-7281-0570-3
ISSN: 2325-2944
DOI: http://dx.doi.org/10.1109/DCOSS.2019.00065
AC-Nummer: -
Homepage: https://ieeexplore.ieee.org/document/8804797
Open Access
  • Kein Open-Access

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Vernetzte und Eingebettete Systeme
Universitätsstraße 65-67
9020  Klagenfurt am Wörthersee
Österreich
  -993640
   kornelia.lienbacher@aau.at
https://nes.aau.at/
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 102021 - Pervasive Computing
  • 102025 - Verteilte Systeme
  • 202017 - Embedded Systems
  • 202022 - Informationstechnik
  • 202036 - Sensorik
Forschungscluster
  • Selbstorganisierende Systeme
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: II)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Pervasive Computing Group

Kooperationen

Organisation Adresse
JOANNEUM RESEARCH Institute for Robotics and Mechatronics
Lakeside B08
9020  Klagenfurt
Österreich
Lakeside B08
AT - 9020  Klagenfurt

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