# Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review
## 2 NDN Architecture and Mobility Component
Machine Learning-Powered Mobility Forecasting (MLBMP) applies Machine Learning (ML) methods to forecast producer actions and enhance routing for more efficient content retrieval [60]. This proposes an intelligent method for improving the movement of producers, utilizing predictive analytics to optimize efficiency in accessing material. The study [61] examines techniques for producer mobility that prioritize energy efficiency, considering the effects on content retrieval and network resource usage. The authors acknowledge the importance of sustainability in NDN by connecting producer mobility strategies with energy-conscious concerns. As illustrated in Figure 4, two mobility phases on NDN, for red arrow involve of producer mobility on NDN while for green arrow refer as consumer mobility on NDN. Figure 4: Producer and consumer mobility in the NDN network. Producer mobility in NDN networks refers to moving producers from their previous positions to new places. During this process, the consumer initiates a direct request for interest from the manufacturer. Next, the producer handles the request for interest and verifies the availability of the FIB. Subsequently, the manufacturer relocates to a different site, updating the existing location and reprocessing the interest request. Upon reprocessing the request, the producer transmits the requested interest to the consumer. Concerning consumer mobility in the NDN network, the consumer transitions from one location to another remain unchanged. However, the notable distinction is that the producer handles the consumer interest request while the consumer is in motion. NDN enables users to access the NDN network without needing specific approaches through utilizing in-network caching to deliver mobility functions. When a customer moves, the requested NDN duplicates the cache on each NDN router [62]. A study by Guo et al. [63] proposed two forwarding systems, namely flooding and smart flooding, which entail disseminating communications by sending interest packets to all active interfaces throughout the flooding process. Flooding is a direct method in which content is disseminated to all nodes in the network. This simplicity facilitates effortless implementation and reduces the need for extensive setting. Note that the decentralized character of flooding confers advantages to dynamic networks, as it prevents the need for a centralized infrastructure. Flooding inherently generates duplication, enhancing the accessibility and dependability of data. Nevertheless, this duplication could result in elevated network congestion and utilization of resources, hence affecting overall effectiveness. The primary disadvantage of flooding is the possibility of significant network congestion caused by the broadcast nature of the method. These consequences include higher latency, lower quality of service, and excessive resource usage, which diminishes its suitability for large-scale networks. Nonetheless, flooding is vulnerable to security concerns such as content contamination and unauthorized entry. Without adequate security measures, the network is susceptible to evil actions, which can jeopardize the integrity and confidentiality of data. Therefore, smart flooding frequently utilizes adaptive algorithms to react to dynamic network conditions effectively. The network's adaptability enhances its resilience, enabling it to function effectively in dynamic contexts and under diverse workloads. As smart flooding utilizes sophisticated algorithms to distribute content intelligently, the heightened intricacy may present difficulties regarding execution, upkeep, and potential compromises with computing efficacy. Other than that, smart flooding employs adaptive decision-making mechanisms to optimize content delivery. The efficacy of bright flooding relies on the precision of these decisions, and any mistakes could impact the overall performance. A Cluster Base Routing (CBR) model [64] has also created a method applicable to traffic situations with a high delay rate.