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Disorder analytic model-based CMT algorithms in vehicular sensor networks

Date
2013
Abstract
Recently, vehicular sensor networks (VSNs) have emerged as a new intelligent transport networking paradigm in the Internet of Things. By sensing, collecting, and delivering traffic-related information, VSNs can significantly improve both driving experience and traffic flow control, especially in constrained urban environments. Latest technological advances enable vehicular devices to be equipped with multiple wireless interfaces, which can support cooperative communications for concurrent multipath transfer (CMT) in VSNs. However, path heterogeneity and vehicle mobilitycause CMT not to achieve the same high transport efficiency recorded in wired nonmobile network environments. This paper proposes a novel vehicular network-based CMT solution (VNCMT) to address the above issues and improve data delivery efficiency. VN-CMT is based on a CMTdisorder analytic model which can effectively and accurately evaluate the degree of out-of-order data. Based on this proposed model, a series of mechanisms are introduced as follows: (1) a packet disorder-reducing retransmission policy to reduce retransmission delay; (2) a path group selection algorithm to find the best path set for data multipath concurrent transfer; and (3) a data scheduling mechanism to distribute data according to each path’s capacity. Simulation results show how VN-CMT improves data delivery efficiency in comparison with an existing state-of-the-art solution.
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Description
peer-reviewed
Publisher
Hindawi Publishing Corporation
Citation
Hindawi International Journal of Distributed Sensor Networks;Article ID 460164,
Collections
Lero - The Science Foundation Ireland Research Centre for Software
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Xu_2013_disorder.pdf
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Keywords
VSNs, vehicular sensor networking, traffic flow control
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/3128
 https://doi.org/10.34961/1121
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Funding Information
National Basic Research Programme of China, National Natural Science Foundation of China, Jiangsu Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, Science Foundation Ireland (SFI)
Sustainable Development Goals
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Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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