# A Statistical Performance Analysis of Named Data Ultra Dense Networks
## 1 Introduction  
The proliferation of Internet of Things (IoT) devices along with advances in wireless communication technologies, such as multi-access edge computing (MEC) [1], multiple-input and multiple-output (MIMO) [2], millimeter-wave (MMW) [3], and Fifth Generation (5G) and beyond, offer novel opportunities to support applications (e.g., smart cities [4], intelligent health-care systems [5], autonomous vehicles [6], and the industrial IoT [7]) that are becoming an integral part of our modern life. These smart applications further aid the transformation of IoT from concept to reality. The fourth industrial revolution, which is helping to build smart infrastructures, would not be able to happen without the help of the IoT [8]. The IoT can be a collection of diverse types of device, such as smartphones, daily household electronic equipment, radio frequency identification (RFID) tags, smart gadgets, sensors, and actuators. Connecting these devices to the Internet and human users may result in the production of massive amounts of useful data [9]. Currently, all of these devices employ the location-based transmission control protocol/Internet protocol (TCP/IP) networking model for communication, and they face many issues, as these devices could be numberedin the billions, producing zettabytes of data [10]. Moreover, IoT application users are always more interested in getting updated content rather than the location of that content. The conventional Internet was designed to share resources and to enable communication among end-to-end hosts, which is called host-oriented communication. However, with the passage of time and technological development, the notion of the Internet has changed. In today's Internet, a substantial amount of content is uploaded on the Internet from content-oriented applications such as Instagram, Facebook, Twitter, and YouTube. Hence, the content on the Internet is increasing exponentially, and such content needs efficient delivery and retrieval. Therefore, various solutions are provided such as content delivery networks (CDNs) and peer-to-peer (P2P) networks. However, such solutions are based on an end-to-end (host-oriented) architecture, which has many issues and is not a good fit for the contemporary requirements of the Internet such as mobility, security, and latency. This paradigm shift in user demand and the limitations of the existing Internet architecture motivated researchers to look for an alternative for the future Internet. Named data networking (NDN) [11] is one of the future Internet architectures proposed under the umbrella of information-centric networking (ICN) [12]. It aims to provide an efficient content distribution mechanism in highly dense or congested network environments through its intrinsic property of dealing with content instead of hosts. NDN adopts a pull-based communication model and directly forwards an application layer name at the network layer along the best path to fetch the desired content. In NDN, each node maintains three types of data structure: (1) pending interest table (PIT), (2) content store (CS), and (3) forwarding information base (FIB). A consumer inserts the name of the desired content into an interest packet and forwards it to the network. When an interest arrives at the content router (CR) on its incoming face (the term face/interface is interchangeable and may refer to the physical interface of a router or an application face), the CR first checks the PIT entries to check whether a request for the same name is already in the queue. A single PIT entry maintains in and out records (sub-collections) of each incoming and outgoing interest that has arrived at the router, but has not yet been satisfied. The in-records maintains the incoming face id of an interest packet, whereas the out-records maintain the outgoing face id. If the router has a matching PIT entry, it merely updates the in-records and stores the incoming face information of the current request.