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Conference Papers Year : 2018

Predicting transmission success with Machine-Learning and Support Vector Machine in VANETs

Abstract

In this article we study the use of the Support Vector Machine technique to estimate the probability of the reception of a given transmission in a Vehicular Ad hoc NETwork (VANET). The transmission takes place between a vehicle and a RoadSide Unit (RSU) at a given distance and with a given transmission rate. The RSU computes the statistics of the receptions and is able to compute the percentage of successful transmissions versus the distance between the vehicle and the RSU and the transmission rate. Starting from this statistic, a Support Vector Machine (SVM) scheme can produce a model. Then, given a transmission rate and a distance between the vehicle and the RSU, the SVM technique can estimate the probability of a succcessful reception. This probability can be used to build an adaptive technique which optimizes the expected throughput between the vehicle and the RSU. Instead of using transmission values of a real experiment, we use the results of an analytical model of CSMA that is customized for 1D VANETs. The model we adopt to perform this task uses a Matern selection process to mimic the transmission in a CSMA IEEE 802.11p VANET. With this model we obtain a closed formula for the probability of successful transmissions. Thus with these results we can train an SVM model and predict other values for other couples : distance, transmission rate. The numerical results we obtain show that SVM seems very suitable to predict the reception probability in a VANET.
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Dates and versions

hal-01864671 , version 1 (30-08-2018)

Identifiers

  • HAL Id : hal-01864671 , version 1

Cite

Mamoudou Sangare, Soumya Banerjee, Paul Mühlethaler, Samia Bouzefrane. Predicting transmission success with Machine-Learning and Support Vector Machine in VANETs. PEMWN 2018 - 7th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, Sep 2018, Toulouse, France. ⟨hal-01864671⟩
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