Data has become an increasingly significant component of the Internet as it has increased in popularity. Where people care more about the data than the location of data. The Named Data Network (NDN) took this concept and paired it with the idea of making data a core component rather than host addresses. Because of its in-network caching capability, NDN will become a better contender than current Transmission Control Protocol and Internet Protocol (TCP/IP) based networks as data traffic grows exponentially. NDNrelated challenges are available for investigation as NDN becomes more important. Routing in NDN is another essential domain that needs to be addressed, and several approaches have been presented to address routing concerns in NDN. In this study, we discuss and highlight NDN and its routing strategies comprehensively. In addition, this research illustrates a comparison of important routing paradigms to emphasis the breadth of routing research in NDN. Also, we investigate the routing attributes of NDN and expose the latest literature on this critical topic. Finally, this study provides useful insights into the emerging areas of guidance in the NDN to assist future studies in addressing challenges and open research issues. INDEX TERMSNamed Data Networks, Open Research issues in NDN, Routing in NDN I.

The Internet is evolving, and data is a critical component of today’s Internet. People are more interested in data than data location. An information-centric network (ICN) uses this idea and makes data, instead of host addresses, an integral component. Another essential topic in the contemporary period is cloud or edge computing, as well as the Internet of Things (IoT) and Artificial Intelligence (AI), which becomes even more critical when combined with ICN. We initially rate the configuration of ICN with cloud or edge IoT and AI (ICN-CIoT-AI) in this study so that readers may learn about the latest trends and merging of ICN-CIoT-AI. As data rates rise and the Internet becomes a requirement for any technology, we require IoT settings in which data can be cached locally, which is possible when ICN collaborates with cloud or edge computing. To make this arrangement more intelligent, we require AI, and machine learning algorithms can help to overcome many obstacles. In this paper, we first discuss ICN, its deployment, and its unique features that distinguish it from its archrival TCP/IP. We then present the most recent research on ICN-CIoT-AI and provide a comprehensive analysis of this domain in terms of technology, AI/ML domain, IoT, and cloud technology. The study framework, simulation software, and results achieved by the researchers are also listed. Finally, we explore three broad categories of open issues and challenges raised by the researchers: security, performance, and in-network caching. We also exhibit the technologies that were employed in the study.

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