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An Adaptive Clustering Scheme for Improving the Scalability in Intelligent Transportation Systems
Author(s) -
Yen-Wen Lin,
Hao-Chun Weng,
Tsung-Han Lee,
Shan-Yin Hou
Publication year - 2013
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2013/964698
Subject(s) - computer science , scalability , computer network , the internet , quality of service , software deployment , context (archaeology) , gateway (web page) , scheme (mathematics) , default gateway , intelligent transportation system , safer , cluster analysis , h.248 , distributed computing , computer security , world wide web , database , paleontology , mathematical analysis , civil engineering , mathematics , engineering , biology , operating system , machine learning
Intelligent Transportation Systems (ITSs) aim at making the vehicle users be safer, comfortable, and better informed. Internet-enabled ITS applications get increasingly attractive recently. In this context, gateway is the portal of accessing the Internet. The efficiency of gateway management in ITSs thus profoundly affects the applicability of Internet-enabled applications. When the number of vehicles on the road greatly increased, the effectiveness of gateway requisition challenges the usage of the ITS services. To remedy the scalability problem in accessing the Internet in ITSs, an adaptive clustering scheme for gateway management is proposed in this paper. In the proposed scheme, the gateways are organized as a two-level cluster architecture. The gateways are dynamically employed according to the QoS requirements of the applications and current networking conditions. As shown in the simulation results, the proposed scheme is able to achieve the QoS requirements with fair gateway deployment cost.

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