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

Big Data is gaining lots of attentions from various research communities as massive data are becoming real issues and processing such data is nowpossible 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 isin 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.Les technologies Big Data gagnent de plus en plus d’attentions de communautés de recherches variées, surtout depuis que les données deviennent si volumineuses, qu’elles posent de réels problèmes, et que leurs traitements ne sont maintenant possibles que grâce aux grandes capacités de calculs des équipements actuels. De plus, les réseaux véhiculaires, aussi appelés VANET pour Vehicular Ad-hoc Networks, se développent considérablement et ils constituent une part de plus en plus importante du marché du véhicule. La topologie de ces réseaux en constante évolution est accompagnée par des données massives venant d’un volume croissant de véhicules connectés. Dans cette thèse, nous discutons dans notre première contribution des problèmes engendrés par la croissance rapide des VANET, et nous étudions l’adaptation des technologies liées aux Big Data pour les VANET. Ainsi, pour chaque étape clé du Big Data, nous posons le problème des VANET. Notre seconde contribution est l’extraction des caractéristiques liées aux VANET afin d’obtenir des données provenant de ceux-ci. Pour ce faire, nous discutons de comment établir des scénarios de tests, et comment émuler un environnement afin, dans un premier temps, de tester une implémentation dans un environnement contrôlé, avant de pouvoir effectuer des tests dans un environnement réel, afin d’obtenir de vraies données provenant des VANET.Pour notre troisième contribution, nous proposons une approche originale de la modélisation du comportement de conducteur. Cette approche estbasée sur un algorithme permettant d’extraire des représentants d’une population, appelés exemplaires, en utilisant un concept de densité locale dans un voisinage

big data algorithms adapted to vehicular networks for driver's behavior modeling

Les technologies Big Data gagnent de plus en plus d’attentions de communautés de recherches variées, surtout depuis que les données deviennent si volumineuses, qu’elles posent de réels problèmes, et que leurs traitements ne sont maintenant possibles que grâce aux grandes capacités de calculs des équipements actuels. De plus, les réseaux véhiculaires, aussi appelés VANET pour Vehicular Ad-hoc Networks, se développent considérablement et ils constituent une part de plus en plus importante du marché du véhicule. La topologie de ces réseaux en constante évolution est accompagnée par des données massives venant d’un volume croissant de véhicules connectés.Dans cette thèse, nous discutons dans notre première contribution des problèmes engendrés par la croissance rapide des VANET, et nous étudions l’adaptation des technologies liées aux Big Data pour les VANET. Ainsi, pour chaque étape clé du Big Data, nous posons le problème des VANET.Notre seconde contribution est l’extraction des caractéristiques liées aux VANET afin d’obtenir des données provenant de ceux-ci. Pour ce faire, nous discutons de comment établir des scénarios de tests, et comment émuler un environnement afin, dans un premier temps, de tester une implémentation dans un environnement contrôlé, avant de pouvoir effectuer des tests dans un environnement réel, afin d’obtenir de vraies données provenant des VANET.Pour notre troisième contribution, nous proposons une approche originale de la modélisation du comportement de conducteur. Cette approche est basée sur un algorithme permettant d’extraire des représentants d’une population, appelés exemplaires, en utilisant un concept de densité locale dans un voisinage.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

Big Data: An incoming challenge for vehicular ad-hoc networking

International audienceBig 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. This paper focuses especially on the network community where currently huge amount of data have to be processed in real time. In the meanwhile, it is also the beginning of Vehicular Ad-hoc Networking (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. Therefore, we will handle this interesting topic by providing backgrounds on Big Data concerning vehicular networks. Key aspects of Big Data related to VANET (e.g., data generation, preprocessing, and communication) are discussed. Finally, we will also illustrate how to analyze data in VANET by presenting a real experimentation and some representatives results

New Method for Selecting Exemplars Application to Roadway Experimentation

I4CS: International Conference on Innovations for Community ServicesInnovations for Community Services18th International Conference, I4CS 2018Žilina, Slovakia, June 18-20, 2018ProceedingsInternational audienceNowadays, data are generated and collected in many domains from various sources. In most of the cases, they are handled as common data where some simple calculations are used to analyse them as measuring the average, the maximum, the deviation, etc. For instance, the average number of children in European families is 1.8 children. This kind of assessment is far away from reality: the number of children should be an integer number. For this reason, exemplars have a finer meaning since its aim, in this case, is to look of an exemplar of a common family in Europe which has 2 children (the most representative family). The aim of this paper is to propose a methodology able to extract representative exemplars from a dataset. This methodology has been experimented with dataset extracted from experimentations of connected vehicle traces. This data analysis has shown some interesting features: the vehicle connectivity guarantees that messages are not lost

New Method for Exemplar Selection and Application to VANET Experimentation

International audienceNowadays, huge amount of data are generated and collected in many domains and from various sources. Most of the time, the collected data are processed as common data where simple calculations are applied for the analysis, such as measuring the average, the maximum, the deviation, etc. Exemplar selection has a finer meaning since its aim is to study a few exemplars from common data (the most representative ones). The objective of this paper is to propose a methodology able to extract these representative exemplars from a dataset. The proposed method has been tested against well-known simulated as well as real dataset. It is then experimented on dataset extracted from experimentations of connected vehicle traces

Towards Analysing Cooperative Intelligent Transport System Security Data

International audienc