Analytically modelling the performance of piggybacking on beacons in VANETs

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    Abstract

    Piggybacking on beacons is a forwarding technique in vehic- ular ad-hoc networks (VANET) as a means to disseminate data. With this technique data is attached to and transmit- ted along with scheduled beacons. Nodes are assumed to beacon asynchronously. In this paper we present a first version of an analytical model that is able to accurately capture the performance of a piggybacking protocol inside a VANET in a number of closed-form expressions, assuming some simplifications. For a given forwarding distance, transmission range, node den- sity, and beacon frequency, the model is able to give the stochastic distribution of the end-to-end delay. The model also provides the distribution of the per-hop delay, the hop length, and the position of the ith forwarder. We have veri- fied our analytical model using a simulation study. The most relevant assumptions in our model are a fixed inter-node distance and a fixed deterministic transmission range. Having completed this stage of our work, our next goal is to extend our model so that we can drop these as- sumptions.
    Original languageUndefined
    Title of host publicationProceedings of the ninth ACM international workshop on Vehicular inter-networking, systems, and applications, VANET '12
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Pages43-52
    Number of pages10
    ISBN (Print)978-1-4503-1317-9
    DOIs
    Publication statusPublished - Jun 2012

    Publication series

    Name
    PublisherACM

    Keywords

    • IR-80858
    • METIS-287933
    • VANET
    • Vehicular Ad-Hoc Network
    • EWI-22054
    • Piggybacking
    • Beaconing

    Cite this

    Klein Wolterink, W., Heijenk, G., & van den Berg, H. L. (2012). Analytically modelling the performance of piggybacking on beacons in VANETs. In Proceedings of the ninth ACM international workshop on Vehicular inter-networking, systems, and applications, VANET '12 (pp. 43-52). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2307888.2307898