8 research outputs found

    Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

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    Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V

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

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    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

    Resource sharing in vehicular cloud

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    Au cours des dernières années, on a observé l'intérêt croissant envers l'accessibilité à l'information et, en particulier, envers des approches innovantes utilisant les services à distance accessibles depuis les appareils mobiles à travers le monde. Parallèlement, la communication des véhicules, utilisant des capteurs embarqués et des dispositifs de communication sans fil, a été introduite pour améliorer la sécurité routière et l'expérience de conduite à travers ce qui est communément appelé réseaux véhiculaires (VANET). L'accès sans fil à l’Internet à partir des véhicules a déclenché l'émergence de nouveaux services pouvant être disponibles à partir ceux-ci. Par ailleurs, une extension du paradigme des réseaux véhiculaires a été récemment promue à un autre niveau. Le nuage véhiculaire (Vehicular Cloud) (VC) est la convergence ultime entre le concept de l’infonuagique (cloud computing) et les réseaux véhiculaires dans le but de l’approvisionnement et la gestion des services. Avec cette approche, les véhicules peuvent être connectés au nuage, où une multitude de services sont disponibles, ou ils peuvent aussi être des fournisseurs de services. Cela est possible en raison de la variété des ressources disponibles dans les véhicules: informatique, bande passante, stockage et capteurs. Dans cette thèse, on propose des méthodes innovantes et efficaces pour permettre la délivrance de services par des véhicules dans le VC. Plusieurs schémas, notamment la formation de grappes ou nuages de véhicules, la planification de transmission, l'annulation des interférences et l'affectation des fréquences à l'aide de réseaux définis par logiciel (SDN), ont été développés et leurs performances ont été analysées. Les schémas de formation de grappes proposés sont DHCV (un algorithme de clustering D-hop distribué pour VANET) et DCEV (une formation de grappes distribuée pour VANET basée sur la mobilité relative de bout en bout). Ces schémas de regroupement sont utilisés pour former dynamiquement des nuages de véhicules. Les systèmes regroupent les véhicules dans des nuages qui ne se chevauchent pas et qui ont des tailles adaptées à leurs mobilités. Les VC sont créés de telle sorte que chaque véhicule soit au plus D sauts plus loin d'un coordonnateur de nuage. La planification de transmission proposée implémente un contrôle d'accès moyen basé sur la contention où les conditions physiques du canal sont entièrement analysées. Le système d'annulation d'interférence permet d'éliminer les interférences les plus importantes; cela améliore les performances de planification d’utilisation de la bande passante et le partage des ressources dans les nuages construits. Enfin, on a proposé une solution à l'aide de réseaux définis par logiciel, SDN, où différentes bandes de fréquences sont affectées aux différentes liens de transmission de chaque VC afin d’améliorer les performances du réseau.Abstract : In recent years, we have observed a growing interest in information accessibility and especially innovative approaches for making distant services accessible from mobile devices across the world. In tandem with this growth of interest, there was the introduction of vehicular communication, also known as vehicular ad hoc networks (VANET), leveraging onboard sensors and wireless communication devices to enhance road safety and driving experience. Vehicles wireless accessibility to the internet has triggered the emergence of service packages that can be available to or from vehicles. Recently, an extension of the vehicular networks paradigm has been promoted to a new level. Vehicular cloud (VC) is the ultimate convergence between the cloud computing concept and vehicular networks for the purpose of service provisioning and management. Vehicles can get connected to the cloud, where a multitude of services are available to them. Also vehicles can offer services and act as service providers rather than service consumers. This is possible because of the variety of resources available in vehicles: computing, bandwidth, storage and sensors. In this thesis, we propose novel and efficient methods to enable vehicle service delivery in VC. Several schemes including cluster/cloud formation, transmission scheduling, interference cancellation, and frequency assignment using software defined networking (SDN) have been developed and their performances have been analysed. The proposed cluster formation schemes are DHCV (a distributed D-hop clustering algorithm for VANET) and DCEV (a distributed cluster formation for VANET based on end-to-end relative mobility). These clustering schemes are used to dynamically form vehicle clouds. The schemes group vehicles into non-overlapping clouds, which have adaptive sizes according to their mobility. VCs are created in such a way that each vehicle is at most D-hops away from a cloud coordinator. The proposed transmission scheduling implements a contention-free-based medium access control where physical conditions of the channel are fully analyzed. The interference cancellation scheme makes it possible to remove the strongest interferences; this improves the scheduling performance and resource sharing inside the constructed clouds. Finally, we proposed an SDN based vehicular cloud solution where different frequency bands are assigned to different transmission links to improve the network performance

    Stable dynamic feedback-based predictive clustering protocol for vehicular ad hoc networks

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    Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Existing clustering algorithms are optimized for either cluster head or member, and for highways or intersections. The lack of intelligent use of mobility parameters like velocity, acceleration, direction, position, distance, degree of vehicles, and movement at intersections, also contributes to cluster stability problems. A dynamic clustering algorithm that efficiently utilizes all mobility parameters can resolve these issues in VANETs. To provide higher stability in VANET clustering, a novel robust and dynamic mobility-based clustering algorithm called junction-based clustering protocol for VANET (JCV) is proposed in this dissertation. Unlike previous studies, JCV takes into account position, distance, movement at the junction, degree of a vehicle, and time spent on the road to select the cluster head (CH). JCV considers transmission range, the moving direction of the vehicle at the next junction, and vehicle density in the creation of a cluster. JCV's performance is compared with two existing VANET clustering protocols in terms of the average cluster head duration, the average cluster member (CM) duration, the average number of cluster head changes, and the percentage of vehicles participating in the clustering process, etc. To evaluate the performance of JCV, we developed a new cloud-based VANET simulator (CVANETSIM). The simulation results show that JCV outperforms the existing algorithms and achieves better stability in terms of the average CH duration (4%), the average CM duration (8%), the number of CM (6%), the ratio of CM (22%), the average CH change rate (14%), the number of CH (10%), the number of non-cluster vehicles (7%), and clustering overhead (35%). The dissertation also introduced a stable dynamic feedback-based predictive clustering (SDPC) protocol for VANET, which ensures cluster stability in both highway and intersection scenarios, irrespective of the road topology. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future construction of the road, considering relative distance, movement at the intersection, degree of vehicles, majority-vehicle, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering algorithms in various road topologies, in terms of the average cluster head change rate, duration of the cluster head, duration of the cluster member, and the clustering overhead. The simulation results show that SDPC outperforms existing algorithms, achieving better clustering stability in terms of the average CH change rate (50%), the average CH duration (15%), the average CM duration (6%), and the clustering overhead (35%)
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