Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks
KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0001-8968-3976
KTH, School of Electrical Engineering (EES).
KTH, School of Electrical Engineering (EES).
2018 (English)In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205, Vol. 28, no 11, p. 3288-3299Article in journal (Refereed) Published
Abstract [en]

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms' performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 28, no 11, p. 3288-3299
Keywords [en]
Visual feature extraction, sensor networks, divisible load theory, distributed optimization
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-239489DOI: 10.1109/TCSVT.2017.2745102ISI: 000449392100016Scopus ID: 2-s2.0-85028717046OAI: oai:DiVA.org:kth-239489DiVA, id: diva2:1266403
Note

QC 20181128

Available from: 2018-11-28 Created: 2018-11-28 Last updated: 2018-11-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Eriksson, Emil

Search in DiVA

By author/editor
Eriksson, EmilDánDán, GyörgyFodor, Viktoria
By organisation
School of Electrical Engineering (EES)
In the same journal
IEEE transactions on circuits and systems for video technology (Print)
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 20 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf