## 2 Literature Review
The SDN-based IoT network increases network efficiency by lowering network overheads caused by frequent communication between the nodes and the controllers, according to the latency, delay, and throughput performance findings. [PERSON] et al. [26] proposed a digital twin-based user-centric resource management plan to lower resource use while increasing user satisfaction. First, the study creates a framework for resource reservations facilitated by user data transfer. Second, the study formulated a joint non-convex issue for reserving bandwidth and computing resources. To further increase user happiness, the study will look at the joint optimization of resource allocation and segment-level cache order based on distilled data from user data transfer. [PERSON] et al. [27] presented a novel software-defined user-centric radio access technology solution that divides power-hungry access points into virtual cells and offers dependable, all-around connectivity to mobile-connected vehicles. Due to the heterogeneous preferences of the connected vehicles, the study introduced a preference-popularity trade-off into each content request, but also used content prefetching at the edge server with multiple classes in the content catalog to ensure fast decision making for mission-critical operations and uninterrupted onboard entertainment. The suggested method outperforms the current starting points regarding content delivery latency, deadline violation, and cache hit ratio. [PERSON] et al. [28] proposed a thorough analysis of software-defined unmanned aerial vehicle networks and architecture, emphasizing their needs, opportunities, and uses. In the context of 6G networking, it highlights how crucial it is to integrate the SDN and unmanned aerial vehicle-enabled networks since they provide superior flexibility, scalability, dependability, and efficient connectivity compared to traditional methods. The study also addresses practical issues with proposed networks, including security, flexibility, interference control, interoperability, and lack of standards. [PERSON], [PERSON] and [PERSON], [PERSON] [29] a novel mobility management feature that decreases the number of handovers in the vehicle-to-network service, hence improving the performance of 5G for high-speed road users, such as connected autonomous vehicles, in small cells. Additionally, the paper suggested a novel cell reselection process for high-speed users under our mobility management function's control using the vehicular frequency reuse scheme and in the RRC_Connected (Radio Resource Control) state. Compared to the conventional scheme, computer simulations demonstrate that the suggested vehicular frequency reuse approach can minimize the total amount of handovers (handover rate) for users by more than 99%. [PERSON] [30] proposed several methods for enhancing connection stability through effective decision-making during handover. First, M-FiVH, a modified probabilistic technique, was developed to improve network stability and minimize 5G handovers. Later, an adaptive learning strategy used connectivity-oriented SARSA reinforcement learning for user-centric virtual cell management to enable effective handover decisions. The study presented an analysis and comparison of several methods and showed that our suggested methods outperform the others regarding network connection. [PERSON] et al. [31] suggested the Proactive Manufacturing Resources Assignment (PMRA) method for the Smart Factory to Production Performance Prediction. First, a smart factory's resources are equipped with distributed control capacity and made smart using modern IIoT and CPS technologies. In this scenario, resources at the cloud center and those at the edge may work together dynamically. A second technique is suggested for reliably predicting production status in the future by extracting real-time production information and using a real-time colored Petri net (RCPN) to analyze key production performance indicators (KPPIs).