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Link State Estimator for VANETs Using Neural Networks

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Abstract

In Vehicular Ad-hoc NETworks (VANETs), it is important to consider the quality of the path used to forward data packets. Because of the fluctuating conditions of VANETs, stringent requirements have been imposed on routing protocols and thus complicating the entire process of packet delivery. To determine which path is the best, a routing protocol relies on a path assessment mechanism. In this paper, the problem of link quality estimation in VANET networks is addressed. Based on the information gathered from the packet decoding errors at the physical layer, a novel link quality estimator is proposed. The proposed link quality estimator named LSENN for Link State estimation based on Neural Networks, has been tested under realistic physical layer and mobility models for reactivity, accuracy and stability evaluation.

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Data Availability

Data available on request from the authors.

Code Availability

Code available on request from the authors.

Notes

  1. Hello packets in case of routing protocols like AODV.

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Authors and Affiliations

  1. LaRIA Laboratory, Computer Science, University of Jijel, Jijel, Algeria

    Hamida Ikhlef, Soumia Bourebia & Ali Melit

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  1. Hamida Ikhlef
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  2. Soumia Bourebia
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  3. Ali Melit
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Contributions

HI: Conceived of the presented idea, performed the experiments, analyse of the results, writing and original draft preparation. SB: Supervised the findings of this work. AM: Supervision and approved the final manuscript.

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Correspondence to Hamida Ikhlef.

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This material is the authors’ own original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere. The paper reflects the authors’ own research and analysis in a truthful and complete manner. All sources used are properly disclosed (correct citation).

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Ikhlef, H., Bourebia, S. & Melit, A. Link State Estimator for VANETs Using Neural Networks. J Netw Syst Manage 32, 10 (2024). https://doi.org/10.1007/s10922-023-09786-5

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  • DOI: https://doi.org/10.1007/s10922-023-09786-5

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