Named Data Networking (NDN) is a new network architecture based on the content-based model that has fundamental differences to the legacy path-based model. The first is focused on the content itself, and the latter focuses on the location of data. Both models provide different benefits to the end-user and the network, but they often cannot inter-operate due to their significant design differences. Therefore, we propose a new network architecture and protocol, both named NDN-Fab, to allow the inter-operation between path-based and content-based network architectures. NDN-Fab is based on the Fabric model and allows the edge network to operate with content-centric architectures, such as the NDN, while the core network can operate with a high-performance path-based communication model. The Core network uses the NDN-Fab protocol that can be embedded in the programmable data plane hardware of the core network's switches, allowing for the sending of multicast packets and link-layer packet steering. Our results show that NDN-Fab can be more efficient in packet forwarding than IPv4, achieving up to 150 times lower average delay, 100 times lower network dispersion, and it managed to process 21% more packets.

Intelligent Transport Systems (ITS) can assist in the identification and reduction of vehicular traffic congestion. In this context, this paper proposes CARTIM, a proposal for collaborative identification and minimization of vehicular congestion. CARTIM uses V2V (Vehicle-to-Vehicle) communication to cooperatively measure the local level of vehicular traffic congestion. Additionally, if any infrastructure is present, the dissemination of consolidated information may occur to vehicles in other regions through V2I (Vehicle-to-Infrastructure) communication. To effectively identify a traffic congestion locally (in vehicles), CARTIM employs a fuzzy logic-based system, which is used in the treatment of qualitative information (e.g., vehicle density etc). The proposed technique also efficiently uses the collaborative communication channel, preventing overload. The simulation results showed that CARTIM can detect congestion (better than related works) and minimize it (based on a heuristic).