Design Models for Trusted Communications in Vehicle-to-Everything (V2X) Networks

Intelligent transportation system is one of the main systems which has been developed to achieve safe traffic and efficient transportation. It enables the road entities to establish connections with other road entities and infrastructure units using Vehicle-to-Everything (V2X) communications. To improve the driving experience, various applications are implemented to allow for road entities to share the information among each other. Then, based on the received information, the road entity can make its own decision regarding road safety and guide the driver. However, when these packets are dropped for any reason, it could lead to inaccurate decisions due to lack of enough information. Therefore, the packets should be sent through a trusted communication. The trusted communication includes a trusted link and trusted road entity. Before sending packets, the road entity should assess the link quality and choose the trusted link to ensure the packet delivery. Also, evaluating the neighboring node behavior is essential to obtain trusted communications because some misbehavior nodes may drop the received packets. As a consequence, two main models are designed to achieve trusted V2X communications. First, a multi-metric Quality of Service (QoS)-balancing relay selection algorithm is proposed to elect the trusted link. Analytic Hierarchy Process (AHP) is applied to evaluate the link based on three metrics, which are channel capacity, link stability and end-to-end delay. Second, a recommendation-based trust model is designed for V2X communication to exclude misbehavior nodes. Based on a comparison between trust-based methods, weighted-sum is chosen in the proposed model. The proposed methods ensure trusted communications by reducing the Packet Dropping Rate (PDR) and increasing the end-to-end delivery packet ratio. In addition, the proposed trust model achieves a very low False Negative Rate (FNR) in comparison with an existing model

Quality of service aware data dissemination in vehicular Ad Hoc networks

Des systèmes de transport intelligents (STI) seront éventuellement fournis dans un proche avenir pour la sécurité et le confort des personnes lors de leurs déplacements sur les routes. Les réseaux ad-hoc véhiculaires (VANETs) représentent l'élément clé des STI. Les VANETs sont formés par des véhicules qui communiquent entre eux et avec l'infrastructure. En effet, les véhicules pourront échanger des messages qui comprennent, par exemple, des informations sur la circulation routière, les situations d'urgence et les divertissements. En particulier, les messages d'urgence sont diffusés par des véhicules en cas d'urgence (p.ex. un accident de voiture); afin de permettre aux conducteurs de réagir à temps (p.ex., ralentir), les messages d'urgence doivent être diffusés de manière fiable dans un délai très court. Dans les VANETs, il existe plusieurs facteurs, tels que le canal à pertes, les terminaux cachés, les interférences et la bande passante limitée, qui compliquent énormément la satisfaction des exigences de fiabilité et de délai des messages d'urgence. Dans cette thèse, en guise de première contribution, nous proposons un schéma de diffusion efficace à plusieurs sauts, appelé Dynamic Partitioning Scheme (DPS), pour diffuser les messages d'urgence. DPS calcule les tailles de partitions dynamiques et le calendrier de transmission pour chaque partition; à l'intérieur de la zone arrière de l'expéditeur, les partitions sont calculées de sorte qu'en moyenne chaque partition contient au moins un seul véhicule; l'objectif est de s'assurer que seul un véhicule dans la partition la plus éloignée (de l'expéditeur) est utilisé pour diffuser le message, jusqu'au saut suivant; ceci donne lieu à un délai d'un saut plus court. DPS assure une diffusion rapide des messages d'urgence. En outre, un nouveau mécanisme d'établissement de liaison, qui utilise des tonalités occupées, est proposé pour résoudre le problème du problème de terminal caché. Dans les VANETs, la Multidiffusion, c'est-à-dire la transmission d'un message d'une source à un nombre limité de véhicules connus en tant que destinations, est très importante. Par rapport à la diffusion unique, avec Multidiffusion, la source peut simultanément prendre en charge plusieurs destinations, via une arborescence de multidiffusion, ce qui permet d'économiser de la bande passante et de réduire la congestion du réseau. Cependant, puisque les VANETs ont une topologie dynamique, le maintien de la connectivité de l'arbre de multidiffusion est un problème majeur. Comme deuxième contribution, nous proposons deux approches pour modéliser l'utilisation totale de bande passante d'une arborescence de multidiffusion: (i) la première approche considère le nombre de segments de route impliqués dans l'arbre de multidiffusion et (ii) la seconde approche considère le nombre d'intersections relais dans l'arbre de multidiffusion. Une heuristique est proposée pour chaque approche. Pour assurer la qualité de service de l'arbre de multidiffusion, des procédures efficaces sont proposées pour le suivi des destinations et la surveillance de la qualité de service des segments de route. Comme troisième contribution, nous étudions le problème de la congestion causée par le routage du trafic de données dans les VANETs. Nous proposons (1) une approche de routage basée sur l’infonuagique qui, contrairement aux approches existantes, prend en compte les chemins de routage existants qui relaient déjà les données dans les VANETs. Les nouvelles demandes de routage sont traitées de sorte qu'aucun segment de route ne soit surchargé par plusieurs chemins de routage croisés. Au lieu d'acheminer les données en utilisant des chemins de routage sur un nombre limité de segments de route, notre approche équilibre la charge des données en utilisant des chemins de routage sur l'ensemble des tronçons routiers urbains, dans le but d'empêcher, dans la mesure du possible, les congestions locales dans les VANETs; et (2) une approche basée sur le réseau défini par logiciel (SDN) pour surveiller la connectivité VANET en temps réel et les délais de transmission sur chaque segment de route. Les données de surveillance sont utilisées en entrée de l'approche de routage.Intelligent Transportation Systems (ITS) will be eventually provided in the near future for both safety and comfort of people during their travel on the roads. Vehicular ad-hoc Networks (VANETs), represent the key component of ITS. VANETs consist of vehicles that communicate with each other and with the infrastructure. Indeed, vehicles will be able to exchange messages that include, for example, information about road traffic, emergency situations, and entertainment. Particularly, emergency messages are broadcasted by vehicles in case of an emergency (e.g., car accident); in order to allow drivers to react in time (e.g., slow down), emergency messages must be reliably disseminated with very short delay. In VANETs, there are several factors, such as lossy channel, hidden terminals, interferences and scarce bandwidth, which make satisfying reliability and delay requirements of emergency messages very challenging. In this thesis, as the first contribution, we propose a reliable time-efficient and multi-hop broadcasting scheme, called Dynamic Partitioning Scheme (DPS), to disseminate emergency messages. DPS computes dynamic partition sizes and the transmission schedule for each partition; inside the back area of the sender, the partitions are computed such that in average each partition contains at least a single vehicle; the objective is to ensure that only a vehicle in the farthest partition (from the sender) is used to disseminate the message, to next hop, resulting in shorter one hop delay. DPS ensures fast dissemination of emergency messages. Moreover, a new handshaking mechanism, that uses busy tones, is proposed to solve the problem of hidden terminal problem. In VANETs, Multicasting, i.e. delivering a message from a source to a limited known number of vehicles as destinations, is very important. Compared to Unicasting, with Multicasting, the source can simultaneously support multiple destinations, via a multicast tree, saving bandwidth and reducing overall communication congestion. However, since VANETs have a dynamic topology, maintaining the connectivity of the multicast tree is a major issue. As the second contribution, we propose two approaches to model total bandwidth usage of a multicast tree: (i) the first approach considers the number of road segments involved in the multicast tree and (ii) the second approach considers the number of relaying intersections involved in the multicast tree. A heuristic is proposed for each approach. To ensure QoS of the multicasting tree, efficient procedures are proposed for tracking destinations and monitoring QoS of road segments. As the third contribution, we study the problem of network congestion in routing data traffic in VANETs. We propose (1) a Cloud-based routing approach that, in opposition to existing approaches, takes into account existing routing paths which are already relaying data in VANETs. New routing requests are processed such that no road segment gets overloaded by multiple crossing routing paths. Instead of routing over a limited set of road segments, our approach balances the load of communication paths over the whole urban road segments, with the objective to prevent, whenever possible, local congestions in VANETs; and (2) a Software Defined Networking (SDN) based approach to monitor real-time VANETs connectivity and transmission delays on each road segment. The monitoring data is used as input to the routing approach

Reinforcement Learning based Gateway Selection in VANETs

In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways\u27 bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time

VANETs Signal Quality-based Route Selection in Smart Cities

In road networks, the most common metrics to determine the optimal route relaying two points are either the path length or the travel time. However, as autonomous smart cars are expected to emerge in future smart cities and lead to an unprecedented growth of mobile applications spectrum for both drivers and passengers, we argue that other metrics can be also considered for route selection. Therefore, the best route for a driver will be also determined by specific in-vehicle users' requirements such as, ensuring sufficient quality for wireless connection with minimum cost, and selecting preferred access networks, etc. To this end, we propose an original algorithm that enables route selection based on maximum expected signal quality with respect to the drivers' maximum tolerated prolongation of journey. Our ultimate objective is to select the route that ensures sufficient signal quality for users on board by maximizing the usage of VANETs resources, through RSUs, while minimizing the usage of the costly LTE-A resources. The obtained simulation results, under several scenarios, have proven the high efficiency of the proposed algorithm

Clustering and 5G-enabled smart cities: a survey of clustering schemes in VANETs

This chapter highlights the importance of Vehicular Ad-hoc Networks (VANETs) in the context of the 5Genabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies, therefore clustering has the potential to play an important role in the 5G-VANET enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms

New Gateway Selection Algorithm Based on Multi-Objective Integer Programming and Reinforcement Learning

Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requires finding suitable gateways to connect the source vehicles to the infrastructure. The major feature of using gateways is to decrease the load of the network infrastructure resources so that each gateway is responsible for a group of vehicles. Unfortunately, the implementation of this goal is facing many challenges, including the highly dynamic topology of VANETs, which causes network instability, and the deployment of applications with high bandwidth demand that can cause network congestion, particularly in urban areas with a high-density vehicle. This work introduces a novel gateway selection algorithm for vehicular networks in urban areas, consisting of two phases. The first phase identifies the best gateways among the deployed vehicles using multi-objective integer programming. While in the second phase, reinforcement learning is employed to select a suitable gateway for any vehicular node in need to access the VANET infrastructure. The proposed model is evaluated and compared to other existing solutions. The obtained results show the efficiency of the proposed system in identifying and selecting the gateways