# Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review
## 3 Recent Mobility Techniques
### Anchorless Technique
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Technique** & **Simulation Tools** & **Parametric** & **Advantages on Performance** & **Weakness on Performance** \\ \hline
[84] & & ndnSIM & Queuing transmission, content load & The performance from the simulation results shows an increase in transmission and effective load sharing. & The simulation shows queueing transmission and increased latency. \\ \hline
[85] & & mini-NDN & Routing and scalability & The simulation highlights NDN routing and scalability & The result shows a lack of highlights tracing and data rendezvous \\ \hline
[86] & & OMNet++ & Network overhead and scalability & The simulation results indicate improved network overhead transmission and are more scalable. & The simulation does not cover communication between two moving vehicles. \\ \hline
[87] & & ndnSIM & scalability & The results exhibit enhanced NDN network performance and scalability & The results show it’s not practical in load balancing and failure recovery \\ \hline
[88] & & C++ and MATLAB & Network overhead and throughput & The simulation presents reducing network overhead with high throughput & This method does not cover scalability with the rendezvous server \\ \hline
[89] & & ndnSIM & scalability & The simulation shows the improvement in scalability and availability during transmission. & The results show handoff latency is still high. \\ \hline
[67] & & ndnSIM & Forwarding and content locator & The simulation demonstrates the improvement of the stateful forwarding plane and location freenes. \\ \hline
[90] & & MATLAB & Content security & The simulation portrays the trustworthy and updated request-respond transmission. & The results generate less secure privacy for vehicular communication. \\ \hline
[91] & & MATLAB & Content encryption & The simulation results establish the reduced encryption interest time & This experiment does not apply to cluster-based anchor \\ \hline
[76] & & ndnSIM & Handoff and delay & The simulation result determines the reduced handoff and delay & This experiment is not compatible with the local mobile node \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mathematical simulation for NDN producer mobility techniques. ## 4 Findings and Future Trends of NDN Mobility

A recent study indicates that NDN mobility can decrease latency and enhance data retrieval performance by enabling data to be obtained from any node within the network rather than a designated place [92]. Furthermore, implementing caching and content retrieval algorithms in NDN mobility can enhance data accessibility and minimize duplication in mobile networks [93]. Note that NDN mobility enhances security and privacy in mobile networks by utilizing encryption and name-based access control techniques, which provide secure and detailed data access [94]. NDN in-network mobility management has garnered interest, with recent studies focusing on developing a scalable and adaptable virtual network overlay. NDN mobility research also incorporates other emerging technologies like edge computing and blockchain. Other than that, it entails relocating computing resources near devices, enhancing performance and reducing communication overhead. Blockchain technology can guarantee the genuineness and uncorrupted nature of the content, which is essential in a mobile setting where devices may lack reliability. Moreover, this technique is thoroughly implemented and tested using the ns3 and ndnSIM simulation tools. Table 7 displays the characteristics of NDN hybrid mobility and its associated benefits. NDN mobility research is expected to focus on incorporating IoT devices, which typically have restricted resources and sporadic connectivity. Researchers are also investigating cross-layer optimization techniques to enhance the performance of NDN mobile