In the recent years we have seen a continuous integration of technology with the urban environment. This fusion aims to improve the efficiency and the quality of living in big urban agglomerates, while reducing the costs for their management. Cities are getting “smarter and smarter”, with a plethora of IoT devices and sensors deployed all over the urban areas. Among those intelligent objects, an important role may be played by cars. Modern vehicles are (or will be) indeed equipped with multiple network interfaces, they have (or will have) computational capabilities and devices able to sense the environment. However, smart and connected cars do not represent only an opportunity, but also a challenge. Computation capabilities are limited, mobility and the diversity of network interfaces are obstacles when providing connectivity to the Internet and to other vehicles. When addressing the networking aspect, we believe that a shift in the Internet model is needed, from a host oriented architecture (IP) to a more content focused paradigm, the Information Centric Networking (ICN) architectures. This thesis thus analyzes the benefits and the challenges of the ICN paradigm, in particular of Named Data Networking (NDN), in the VANET domain, presenting the first implementation running on real cars of NDN for VANET (V-NDN). It then proposes Navigo, an NDN based forwarding mechanism for content retrieval over V2V and V2I communications, with the goal of efficiently discovering and retrieving data while reducing the network overhead. Networking mobility is not only a challenge for vehicles, but for any connected mobile device. For this reason, this thesis extends its initial area of interest — VANET — and addresses the network mobility problem for generic mobile nodes, proposing a NDN-based solution, dubbed MAP–Me. MAP-Me tackles the intra-AS content provider mobility problem without relying on any fixed node in the network. It exploits notifications messages at the time of a handover and the forwarding plane to maintain the data provider “always” reachable.Finally, the “connected car” concept is not the only novel element in modern vehicles. Cars indeed won’t be only connected, but also smart, able to locally process data produced by in-car sensors. Vehicles are the perfect candidates to play an important role in the recently proposed Fog Computing architecture. Such an architecture moves computational tasks typical of the cloud away from it and brings them to the edge, closer to where the data is produced. To prove that such a model, with the car as computing edge node, is already feasible with the current technology and not only a vision for the future, this thesis presents ParkMaster. Parkmaster is a fully deployed edge-based system that combines vision and machine learning techniques, the edge (driver’s smartphone) and the cloud to sense the environment and tackle the parking availability problem.