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Monitoring Approach of Cyber-physical Systems by Quality Measures

hal.structure.identifierLab-STICC_TB_CID_DECIDE
hal.structure.identifierDépartement Image et Traitement Information [ITI]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorMERINO LASO, Pedro
hal.structure.identifierInstitut de Recherche de l'Ecole Navale [IRENAV]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorBROSSET, David
hal.structure.identifierLab-STICC_TB_CID_DECIDE
hal.structure.identifierDépartement Image et Traitement Information [ITI]
hal.structure.identifierChaire cyberdéfense systèmes navals (Ecole Navale, IMT-Atlantique, THALES, DCNS)
dc.contributor.authorPUENTES, John
dc.date.accessioned2021-05-14T09:50:09Z
dc.date.available2021-05-14T09:50:09Z
dc.date.issued2017
dc.date.conference2016-12-01
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/77259
dc.description.abstractEnModern cities, industrial plants, cars, trucks, and vessels, among others, make extensive use of cyber-physical systems and sensors. These systems are very critical and contribute to assist decision making. Large data streams are thus produced and analyzed to extract information that allows building knowledge through a set of principles called wisdom. However, because of multiple imperfections, as well as intrinsic, contextual, and extrinsic conditions that alter data, the quality of the generated streams must be evaluated, to determine how relevant they are for decision support. This paper presents a methodology to monitor cyber-physical systems by quality estimation, which defines suitable evaluation characteristics for pertinent analysis. Quality assessment is defined for data imperfections, information dimensions, knowledge factors, and wisdom aspects. The case study of a cyber-physical network of a liquid container training platform is presented in detail, to show how the approach can be applied. Obtained measures are multidimensional, heterogeneous, and variable.
dc.language.isoen
dc.publisherSpringer
dc.source.titleProceedings S-CUBE 2016 : 7th International Conference on Sensor Systems and Software
dc.subject.enMonitoring
dc.subject.enSensor data processing
dc.subject.enMulti-source sensor network
dc.subject.enCyber-physical system
dc.subject.enData quality
dc.subject.enInformation quality
dc.title.enMonitoring Approach of Cyber-physical Systems by Quality Measures
dc.typeCommunication dans un congrès avec actes
dc.identifier.doi10.1007/978-3-319-61563-99
dc.subject.halInformatique [cs]/Automatique
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halInformatique [cs]/Performance et fiabilité [cs.PF]
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
bordeaux.page105 - 117
bordeaux.volume205
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.countryFR
bordeaux.title.proceedingS-CUBE 2016 : 7th International Conference on Sensor Systems and Software
bordeaux.conference.cityNice
bordeaux.peerReviewedoui
hal.identifierhal-01609035
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01609035v1
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