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dc.contributor.authorGuegan, Loic
dc.contributor.authorRais, Issam
dc.contributor.authorAnshus, Otto Johan
dc.date.accessioned2024-02-15T13:39:46Z
dc.date.available2024-02-15T13:39:46Z
dc.date.issued2023-09-27
dc.description.abstract<p>In Cyber-Physical Systems (CPS) such as Wireless Sensors Networks (WSN), disseminating data is crucial. Under energy constraints with limited communications capabilities, performing data dissemination is challenging. In such contexts, common data dissemination methods cannot be used. Nodes must rely on device-to-device communications policies to mitigate the impact of communications on the nodes energy consumption. However, depending on nodes configuration (up-times duration, wireless technology capabilities and energy consumption), choosing a suitable communication policy is challenging. <p>This work exposes the problem statement for using analytic algorithms to predict the most suitable device-to-device communication policy, for a given node configuration, to match a given coverage and energy consumption target in a constrained environment.en_US
dc.identifier.citationGuegan L, Rais I, Anshus O: Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics. In: Kanhere S, Silvestri, Saukh, Nikoletseas S. Proceeding of the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things - DCOSS-IoT 2023, 2023. IEEE Computer Society Digital Library p. 68-70en_US
dc.identifier.cristinIDFRIDAID 2158997
dc.identifier.doi10.1109/DCOSS-IoT58021.2023.00019
dc.identifier.isbn979-8-3503-4649-7
dc.identifier.issn2325-2936
dc.identifier.issn2325-2944
dc.identifier.urihttps://hdl.handle.net/10037/32944
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.projectIDNorges forskningsråd: 270672en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleTowards Data Dissemination Policy Prediction for Constrained Environments Using Analyticsen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)