Enhancing infotainment applications quality of service in vehicular ad hoc networks

Les réseaux ad hoc de véhicules accueillent une multitude d’applications intéressantes. Parmi celles-ci, les applications d’info-divertissement visent à améliorer l’expérience des passagers. Ces applications ont des exigences rigides en termes de délai de livraison et de débit. De nombreuses approches ont été proposées pour assurer la qualité du service des dites applications. Elles sont réparties en deux couches : réseau et contrôle d’accès. Toutefois, ces méthodes présentent plusieurs lacunes. Cette thèse a trois volets. Le premier aborde la question du routage dans le milieu urbain. A cet égard, un nouveau protocole, appelé SCRP, a été proposé. Il exploite l’information sur la circulation des véhicules en temps réel pour créer des épines dorsales sur les routes et les connecter aux intersections à l’aide des nœuds de pont. Ces derniers collectent des informations concernant la connectivité et le délai, utilisées pour choisir les chemins de routage ayant un délai de bout-en-bout faible. Le deuxième s’attaque au problème d’affectation des canaux de services afin d’augmenter le débit. A cet effet, un nouveau mécanisme, appelé ASSCH, a été conçu. ASSCH collecte des informations sur les canaux en temps réel et les donne à un modèle stochastique afin de prédire leurs états dans l’avenir. Les canaux les moins encombrés sont sélectionnés pour être utilisés. Le dernier volet vise à proposer un modèle analytique pour examiner la performance du mécanisme EDCA de la norme IEEE 802.11p. Ce modèle tient en compte plusieurs facteurs, dont l’opportunité de transmission, non exploitée dans IEEE 802.11p.The fact that vehicular ad hoc network accommodates two types of communications, Vehicle-to-Vehicle and Vehicle-to-Infrastructure, has opened the door for a plethora of interesting applications to thrive. Some of these applications, known as infotainment applications, focus on enhancing the passengers' experience. They have rigid requirements in terms of delivery delay and throughput. Numerous approaches have been proposed, at medium access control and routing layers, to enhance the quality of service of such applications. However, existing schemes have several shortcomings. Subsequently, the design of new and efficient approaches is vital for the proper functioning of infotainment applications. This work proposes three schemes. The first is a novel routing protocol, labeled SCRP. It leverages real-time vehicular traffic information to create backbones over road segments and connect them at intersections using bridge nodes. These nodes are responsible for collecting connectivity and delay information, which are used to select routing paths with low end-to-end delay. The second is an altruistic service channel selection scheme, labeled ASSCH. It first collects real-time service channels information and feeds it to a stochastic model that predicts the state of these channels in the near future. The least congested channels are then selected to be used. The third is an analytical model for the performance of the IEEE 802.11p Enhanced Distributed Channel Access mechanism that considers various factors, including the transmission opportunity (TXOP), unexploited by IEEE 802.11p

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution