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An efficient sensor network architecture using open platform in vehicle environment

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dc.contributor.authorYim, Hong Bin-
dc.contributor.authorPark, Pyung Sun-
dc.contributor.authorMoon, Hee-Seok-
dc.contributor.authorJung, Jaeil-
dc.date.accessioned2022-12-21T05:09:05Z-
dc.date.available2022-12-21T05:09:05Z-
dc.date.created2022-09-16-
dc.date.issued2007-12-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179216-
dc.description.abstractVehicles have been developed with an objective of safety. A large number of sensors will be required in Advanced Safety Vehicles that provide intelligent and automatic services in future ITS (Intelligent Transport Systems) circumstances. Because current in-vehicle networks must be changed to add new sensors, the number of sensors that can be added is restricted in current in-vehicle networks. To manage the sensors more efficiently and to provide extensibility, we propose a SCSN (Smart Car Sensor Network), which is an in-vehicle architecture based on AMI-C and OSGi standards. In this architecture, Vehicle Interface (VI), defined in the AMI-C standard, performs as a gateway in an AMI-C network. An integrated VI structure has been developed to provide a Vehicle Service (VS) on a standard platform. An interworking structure with a CAN(Controller Area Network) interface is implemented to provide an efficient VI. In current telematics architecture, time delay occurs between the CAN network start-up time and the platform booting time. Message loss occurs during this time delay. In this paper, we propose an efficient gateway architecture to minimize message loss due to this time delay. The efficiency of this platform has been verified using CANoe, which is a vehicle-network simulation tool.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleAn efficient sensor network architecture using open platform in vehicle environment-
dc.typeArticle-
dc.contributor.affiliatedAuthorJung, Jaeil-
dc.identifier.doi10.1007/978-3-540-71789-8_24-
dc.identifier.scopusid2-s2.0-38049113102-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4412 LNCS, pp.232 - 241-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume4412 LNCS-
dc.citation.startPage232-
dc.citation.endPage241-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusIntelligent vehicle highway systems-
dc.subject.keywordPlusMessage passing-
dc.subject.keywordPlusNetwork security-
dc.subject.keywordPlusOptimization-
dc.subject.keywordPlusWireless sensor networks-
dc.subject.keywordPlusController Area Networks (CAN)-
dc.subject.keywordPlusIntelligent Transport Systems-
dc.subject.keywordPlusSmart Car Sensor Networks (SCSN)-
dc.subject.keywordPlusNetwork architecture-
dc.subject.keywordAuthorITS-
dc.subject.keywordAuthorSCSN(Smart Car Sensor Network)-
dc.subject.keywordAuthorSensor clustering node-
dc.subject.keywordAuthorSensor network-
dc.subject.keywordAuthorSensor network gateway-
dc.subject.keywordAuthorTelematics-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-540-71789-8_24-
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