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Algorithmes de Big Data adaptés aux réseaux véhiculaires pour modélisation de comportement de conducteur

Abstract : Big Data is gaining lots of attentions from various research communities as massive data are becoming real issues and processing such data is now possible thanks to available high-computation capacity of today’s equipment. In the meanwhile, it is also the beginning of Vehicular Ad-hoc Networks (VANET) era. Connected vehicles are being manufactured and will become an important part of vehicle market. Topology in this type of network is in constant evolution accompanied by massive data coming from increasing volume of connected vehicles in the network. In this thesis, we handle this interesting topic by providing our first contribution on discussing different aspects of Big Data in VANET. Thus, for each key step of Big Data, we raise VANET issues. The second contribution is the extraction of VANET characteristics in order to collect data. To do that, we discuss how to establish tests scenarios, and to how emulate an environment for these tests. First we conduct an implementation in a controlled environment, before performing tests on real environment in order to obtain real VANET data. For the third contribution, we propose an original approach for driver’s behavior modeling. This approach is based on an algorithm permitting extraction of representatives population, called samples, using a local density in a neighborhood concept.
Keywords : Test Big Data VANET
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Submitted on : Friday, January 11, 2019 - 2:24:14 PM
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  • HAL Id : tel-01978351, version 1

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Emilien Bourdy. Algorithmes de Big Data adaptés aux réseaux véhiculaires pour modélisation de comportement de conducteur. Informatique [cs]. Université de Reims Champagne Ardenne URCA, 2018. Français. ⟨tel-01978351⟩

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