Abstract

Intelligent Transportation Systems (ITS) embrace smart vehicular environments through a fully connected paradigm known as vehicular networks. Vehicular networks allow automobiles to stay online and connected with their surroundings while travelling. In that sense, vehicular networks enable various activities; for example, autonomous driving, road surveillance, data collection, content delivery, and many others. This leads to more efficient, safer, and comfort driving experiences and opens up new opportunities for many business sectors. As such, the networking industry and academia have shown great interests in advancing vehicular networks and leveraging relevant services.

In this dissertation, several vehicular network problems are addressed along with proposing novel ideas and utilizing effective solutions. As opposed to stationary or slow moving communications, vehicular networks experience more challenging environment as a result of vehicle mobility. Consequently, vehicular networks suffer from ever-changing topology, short contact times, and intractable propagation environments. In particular, this dissertation presents six works that participate in supplementing the literature as follows. First, a content delivery framework in the context of vehicular network is studied where digital contents are generated by different content providers (CP) and have distinct values. To this end, a prefetching technique along with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications are used to enable fast content delivery. Furthermore, a pricing model is proposed to deal with contents' values to attain a satisfactory Quality of Experience (QoE). Second, a more advanced system model is discussed to cache contents with the assistance of vehicles and to enable a disconnected and fixed Road-Side Unit (RSU) to participate in providing content delivery services. The changing popularity of contents is investigated besides accounting for the limited RSU cache capabilities. Third, the stationary RSU proposed in the second work is replaced by a more flexible infrastructure, namely an aerial RSU mounted on an unmanned aerial vehicle (UAV). The mobility of the UAV and its constrained energy capacity are analyzed and Deep Reinforcement Learning is incorporated to aid in solving the challenges in leveraging UAVs. Fourth, the previous two studies are integrated by investigating the collaboration between a UAV and terrestrial RSUs in delivering large-size contents. A strategy to fill up the UAV cache is also suggested via mulling contents over vehicles. Fifth, the complexity of vehicular urban environments is addressed. In particular, the problem of disconnected areas in vehicular environments due to the appearance of high-rise buildings and other obstacles is studied. In details, a Reconfigurable Intelligent Surface (RIS) is exploited to provide indirect links between the RSU and vehicles travelling through such areas. Our sixth and final contribution deals with time-constrained Internet of Things (IoT) devices (IoTD) supporting ITS networks. In this regard, a UAV is dispatched to collect their data timely and fully while being assisted by a RIS to improve the wireless channel quality. In the end, this dissertation provides discussions that highlight open research directions worth of further investigations.