## 3 Mobility Methodology  
KITE, the PMSS, and hybrid NeMo were selected due to the mobility support offered and the need for producer mobility technology. KITE uses the indirection method to transmit content using simple NDN communication; the PMSS improves producer operation by reducing handover latency; and hybrid NeMO provides a BIT to replace the FIB function by using the same cluster for indirection mobility. Thus, these three methods are comparable in measuring network performance for handover latency and signaling cost. On the basis of the NDN architecture, the ndnSIM simulator can be implemented on the network protocol stack framework. The simulator works with any link layer protocol, including point-to-point, CSMA, and wireless, as well as with the deployment of IPv4 and IPv6. The advantage of the ndnSIM simulator is that it can implement heterogeneous scenarios, such as NDN over IP. The ndnSIM simulator uses the C++ programming language to enable several modules in the NDN to operate, including PIT, FIB, content storage, network and application surface, and forwarding strategies. The use of this structured module enables any component to be modified. NS3 has been patented, using the GNU GPLv2 license to reduce the writing of model procedures in simulations. The NS3 simulator uses the C++ library as its main program, as no graphics version is provided. NS3 can be used on Windows operating systems by adding MinGW modules. To capture the traffic log, the Wireshark app can be combined with the NS3 simulator. Several methods are running for the NDN testbed, especially on mobility perspectives, including KITE with C-IoT, PMSS with C-IoT, and hybrid NeMO with C-IoT, which is the latest technology. Figure 4 shows the setup for these three methods, which include a home router and a destination router; an AP that maintains the connection \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Method** & **Description** \\ \hline
[49] & ERDOS & Integration of edge-native data flow and edge computing. \\
[50] & NDN-based aVC framework (NVCF) & Improve aVC data gathering success rates and lower the \\  & & cost of aVC data retrieval. \\  & & With in-network caching of widely used datasets, NDN for \\
[51] & NDN Genomics & genomic information operations improves data insights, \\  & & accelerates extraction leveraging ubiquitous resources, and \\  & & enables society to create architecture. \\
[52] & VC NDN & By using the NDN’s benefits, it provides cost-effective and \\  & & value-based data retrieval. \\
[53] & NDN Routing overlay & In addition to reducing network and cache burden, NDN \\  & & CSs that offer data aggregation and transformation also \\  & & enforce privacy naturally. \\
[54] & VCCN & Vehicle networks have the potential to spread new data \\  & & via multi-hop. \\
[55] & NDN IoT Edge & Cipher texts and signatures are used to ensure the security \\  & & of medical data transport and the advantages of NDN. \\
[56] & VSN on NDN & Routing data between virtual sensors is a solution to the \\  & & current paradigm. \\  & & Based on NDN, an AR/VR computational architecture that \\
[57] & NDN Mixed Reality Real-Time & potentially addresses these issues is developed by utilizing \\  & & a hybrid edge-cloud model. \\
[58] & NDN SGX-Based & Data access control keys are distributed and maintained \\  & & efficiently and flexibly. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NDN cloud application. for mobile producers; and interaction between consumers with FIB to receive I_packets and D_packets. Simulation is applied by researchers to implement new solution methods before testing them in real situations. Simulation methods can improve performance and meet performance expectations. Simulation combines elements of hardware and software. The combination of these elements is important, because running the simulation process can identify how some elements can communicate with each other and also identify the effect of the use of certain elements on the simulation process [59].