Vis enkel innførsel

dc.contributor.authorOvesen, Aril Bernhard
dc.contributor.authorNordmo, Tor-Arne Schmidt
dc.contributor.authorJohansen, Håvard D.
dc.contributor.authorRiegler, Michael Alexander
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorJohansen, Dag
dc.date.accessioned2022-02-14T08:31:59Z
dc.date.available2022-02-14T08:31:59Z
dc.date.created2021-10-21T11:39:36Z
dc.date.issued2021-10-18
dc.identifier.issn2078-2489
dc.identifier.urihttps://hdl.handle.net/11250/2978618
dc.description.abstractIn this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.en_US
dc.description.sponsorshipThis work is partially funded by the Research Council of Norway project numbers 274451 and 263248, and Lab Nord-Norge (“Samfunnsløftet”).en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesInformation;Volume 12, Issue 10
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectEdge computingen_US
dc.subjectPrivacy preservationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectFile systemsen_US
dc.subjectMachine learningen_US
dc.subjectDigital forensicsen_US
dc.titleFile System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environmentsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2021 by the authors.en_US
dc.source.articlenumber430en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.3390/info12100430
dc.identifier.cristin1947539
dc.source.journalInformationen_US
dc.source.volume12en_US
dc.source.issue10en_US
dc.source.pagenumber1-19en_US
dc.relation.projectNorges forskningsråd: 263248en_US
dc.relation.projectNorges forskningsråd: 274451en_US
dc.subject.nsiVDP::Informasjons- og kommunikasjonssystemer: 321en_US
dc.subject.nsiVDP::Information and communication systems: 321en_US


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal