Named Data Networking (NDN) is the most promising paradigm recently conceived for future Internet architectures, where communications are driven by contents instead of host addresses. To realize this novel paradigm, three novel tables, namely Content Store, Pending Interest Table (PIT) and Forwarding Information Base (FIB), are utilized in NDN forwarding plane. Designing and evaluating the quick enough forwarding plane with high capacity is a major challenge within the overall NDN research area. Since NDN was proposed in 2010, there have been many efforts focusing on this challenge and a rich literature has been developed. Unfortunately, there is a lack of the comprehensive sketch about the requirements of NDN forwarding plane and the study on various schemes proposed. Focusing on the above insufficiency, this survey gives the complete requirements and compares all the schemes proposed for NDN forwarding plane based on the data structure utilized. In addition, the survey also discusses some issues, challenges and directions in future research. It is considered that designing a novel data structure to meet all requirements of the forwarding plane and studying on a better structure of Content Store play important roles, while discussing the necessity of a unified index, combining with other contents in NDN research and implementing a unified benchmark are also required in this domain.

To keep up with the rapid development of railway and meet the high demand for communication business in China, Global System for Mobile Communications-Railway (GSM-R) based on General Packet Radio Service (GPRS) ahead of Europe has been developed. However, the current data communication mode of GPRS using TCP/IP has some disadvantages, such as poor mobility, lack of security etc. For this reason, there are some problems for GSM-R, like handover, which makes communication service quality undesirable. Named Data Networking focuses on named data, adopts data-facing communication mode, and does not care where the contents are stored but the contents themselves. It can effectively solve the problems caused by TCP/IP. In this paper, a new high-speed railway communication system with Named Data Networking (NDN) named NDN-GSM-R is proposed. Two simulations, called transmission interference time and end-to-end transmission delay of NDN-GSM-R, have been conducted. The simulation results show that NDN-GSM-R has better performance in high-speed mobile communication environment and can make up the insufficiency of the current GSM-R based on TCP/IP framework.

In Named Data Networking (NDN) architecture, packets carry data names rather than source or destination addresses. To realize this paradigm, each NDN router maintains three data structures CS, PIT, FIB in the forwarding plane. Designing a quick enough forwarding plane with high capacity is a major challenge within the overall NDN research area. In this paper, we present a novel implementation of FIB called MaFIB. The evaluations indicate that the excellent data structure and the efficient lookup algorithm of MaFIB can effectively reduce the memory cost and can accelerate the lookup process. Its outstanding performance in probability of false positive also makes MaFIB meet current network requirements.

Name lookup is a key technology for the forwarding plane of content router in Named Data Networking (NDN). To realize the efficient name lookup, what counts is deploying a high-performance index in content routers. So far, the proposed indexes have shown good performance, most of which are optimized for or evaluated with URLs collected from the current Internet, as the large-scale NDN names are not available yet. Unfortunately, the performance of these indexes is always impacted in terms of lookup speed, memory consumption and false positive probability, as the distributions of URLs retrieved in memory may differ from those of real NDN names independently generated by content-centric applications online. Focusing on this gap, a smart mapping model named Pyramid-NN via neural networks is proposed to build an index called LNI for NDN forwarding plane. Through learning the distributions of the names retrieved in the static memory, LNI that will be trained by real NDN names offline and preset in content routers in the future can not only reduce the memory consumption and the probability of false positive, but also ensure the performance of real NDN name lookup. Experimental results show that LNI-based FIB can reduce the memory consumption to 58.258 MB. Moreover, as it can be deployed on SRAMs, the throughput is about 177 MSPS, which well meets the current network requirement for fast packet processing.