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https://doi.org/10.1109/vnc.20...
Article . 2012 . Peer-reviewed
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https://dx.doi.org/10.1184/r1/...
Other literature type . 2012
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https://dx.doi.org/10.1184/r1/...
Other literature type . 2012
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https://dx.doi.org/10.48550/ar...
Article . 2013
License: arXiv Non-Exclusive Distribution
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Improving VANET protocols via network science

Authors: Wantanee Viriyasitavat; Susana Sargento; Romeu Monteiro; Ozan K. Tonguz;

Improving VANET protocols via network science

Abstract

Developing routing protocols for Vehicular Ad Hoc Networks (VANETs) is a significant challenge in these large, self-organized and distributed networks. We address this challenge by studying VANETs from a network science perspective to develop solutions that act locally but influence the networkperformance globally. More specifically, we look at snapshots from highway and urban VANETs of different sizes and vehicle densities, and study parameters such as the node degree distribution, the clustering coefficient and the average shortest path length, in order to better understand the networks' structure and compare it to structures commonly found in large real world networks such as small-world and scale-free networks. We then show how to use this information to improve existing VANETprotocols. As an illustrative example, it is shown that, by adding new mechanisms that make use of this information, the overhead of the urban vehicular broadcasting (UV-CAST) protocol can be reduced substantially with no significant performance degradation.

Keywords

Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, 90699 Electrical and Electronic Engineering not elsewhere classified, FOS: Electrical engineering, electronic engineering, information engineering, Computer Engineering

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    32
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
32
Average
Top 10%
Top 10%
Green