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dc.date.accessioned2022-08-17T15:31:15Z
dc.date.available2022-08-17T15:31:15Z
dc.date.created2022-08-01T11:13:55Z
dc.date.issued2022
dc.identifier.citationAlslie, Joakim Aalstad Ovesen, Aril Bernhard Nordmo, Tor-Arne Schmidt Johansen, Håvard D. Halvorsen, Pål Riegler, Michael Johansen, Dag . Áika: A Distributed Edge System for AI Inference. Big Data and Cognitive Computing. 2022
dc.identifier.urihttp://hdl.handle.net/10852/95015
dc.description.abstractVideo monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew members. In this paper, we present Áika, a robust system for executing distributed Artificial Intelligence (AI) applications on the edge. Áika provides engineers and researchers with several building blocks in the form of Agents, which enable the expression of computation pipelines and distributed applications with robustness and privacy guarantees. Agents are continuously monitored by dedicated monitoring nodes, and provide applications with a distributed checkpointing and replication scheme. Áika is designed for monitoring and surveillance in privacy-sensitive and unstable offshore environments, where flexible access policies at the storage level can provide privacy guarantees for data transfer and access.
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleÁika: A Distributed Edge System for AI Inference
dc.title.alternativeENEngelskEnglishÁika: A Distributed Edge System for AI Inference
dc.typeJournal article
dc.creator.authorAlslie, Joakim Aalstad
dc.creator.authorOvesen, Aril Bernhard
dc.creator.authorNordmo, Tor-Arne Schmidt
dc.creator.authorJohansen, Håvard D.
dc.creator.authorHalvorsen, Pål
dc.creator.authorRiegler, Michael
dc.creator.authorJohansen, Dag
cristin.unitcode185,15,5,75
cristin.unitnameDIS Digital infrastruktur og sikkerhet
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin2040283
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Big Data and Cognitive Computing&rft.volume=&rft.spage=&rft.date=2022
dc.identifier.jtitleBig Data and Cognitive Computing
dc.identifier.volume6
dc.identifier.issue2
dc.identifier.doihttps://doi.org/10.3390/bdcc6020068
dc.identifier.urnURN:NBN:no-97581
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2504-2289
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/95015/1/BDCC-06-00068.pdf
dc.type.versionPublishedVersion
cristin.articleid68


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