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An efficient sensor network architecture using open platform in vehicle environment

Authors
Yim, Hong BinPark, Pyung SunMoon, Hee-SeokJung, Jaeil
Issue Date
Dec-2007
Publisher
Springer Verlag
Keywords
ITS; SCSN(Smart Car Sensor Network); Sensor clustering node; Sensor network; Sensor network gateway; Telematics
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4412 LNCS, pp.232 - 241
Indexed
SCOPUS
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
4412 LNCS
Start Page
232
End Page
241
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179216
DOI
10.1007/978-3-540-71789-8_24
ISSN
0302-9743
Abstract
Vehicles 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.
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