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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29645
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dc.contributor.authorGkikopoulos, Panagiotis-
dc.contributor.authorKropf, Peter-
dc.contributor.authorSchiavoni, Valerio-
dc.contributor.authorSpillner, Josef-
dc.date.accessioned2024-01-27T12:44:15Z-
dc.date.available2024-01-27T12:44:15Z-
dc.date.issued2024-05-01-
dc.identifier.issn0743-7315de_CH
dc.identifier.issn1096-0848de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29645-
dc.description.abstractSocieties and legislations are moving towards automated decision-making based on measured data in safety-critical environments. Over the next years, density and frequency of measurements will increase to generate more insights and get a more solid basis for decisions, including through redundant low-cost sensor deployments. The resulting data characteristics lead to large-scale system design in which small input data errors may lead to severe cascading problems including ultimately wrong decisions. To ensure internal data consistency to mitigate this risk in such IoT environments, fast-paced data fusion and consensus among redundant measurements need to be achieved. In this context, we introduce history-aware sensor fusion powered by accurate voting with clustering as a promising approach to achieve fast and informed consensus, which can converge to the output up to 4X faster than the state of the art history-based voting. Leveraging three case studies, we investigate different voting schemes and show how this approach can improve data accuracy by up to 30% and performance by up to 12% compared to state-of-the-art sensor fusion approaches. We furthermore contribute a specification format for easily deploying our methods in practice and use it to develop a pilot implementation.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofJournal of Parallel and Distributed Computingde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectConsensus votingde_CH
dc.subjectData reliabilityde_CH
dc.subjectData fusionde_CH
dc.subjectSensor redundancyde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleReliable IoT analytics at scalede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1016/j.jpdc.2024.104840de_CH
dc.identifier.doi10.21256/zhaw-29645-
zhaw.funding.euNode_CH
zhaw.issue104840de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume187de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedService Engineeringde_CH
zhaw.funding.zhawInnenraumnavigation für personalisiertes Einkaufende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://doi.org/10.5281/zenodo.8069916de_CH
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Gkikopoulos, P., Kropf, P., Schiavoni, V., & Spillner, J. (2024). Reliable IoT analytics at scale. Journal of Parallel and Distributed Computing, 187(104840). https://doi.org/10.1016/j.jpdc.2024.104840
Gkikopoulos, P. et al. (2024) ‘Reliable IoT analytics at scale’, Journal of Parallel and Distributed Computing, 187(104840). Available at: https://doi.org/10.1016/j.jpdc.2024.104840.
P. Gkikopoulos, P. Kropf, V. Schiavoni, and J. Spillner, “Reliable IoT analytics at scale,” Journal of Parallel and Distributed Computing, vol. 187, no. 104840, May 2024, doi: 10.1016/j.jpdc.2024.104840.
GKIKOPOULOS, Panagiotis, Peter KROPF, Valerio SCHIAVONI und Josef SPILLNER, 2024. Reliable IoT analytics at scale. Journal of Parallel and Distributed Computing. 1 Mai 2024. Bd. 187, Nr. 104840. DOI 10.1016/j.jpdc.2024.104840
Gkikopoulos, Panagiotis, Peter Kropf, Valerio Schiavoni, and Josef Spillner. 2024. “Reliable IoT Analytics at Scale.” Journal of Parallel and Distributed Computing 187 (104840). https://doi.org/10.1016/j.jpdc.2024.104840.
Gkikopoulos, Panagiotis, et al. “Reliable IoT Analytics at Scale.” Journal of Parallel and Distributed Computing, vol. 187, no. 104840, May 2024, https://doi.org/10.1016/j.jpdc.2024.104840.


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