###### Abstract
Centralized control is best suitedto be deployed when there is a requirement for simplified network control, in data center networks where global optimizations are very important, when the network size is small, and when the network environment is relatively stable. This approach, however, is less extensible, has a propensity to be a source of collapse, and has an extended communication latency for the control layer [21]. The central point of malfunction and challenges with scaling observed in the single controller paradigm are successfully avoided in distributed control architecture, where the control is conceptually and/or physically dispersed among numerous synchronized controllers. Distributed control architecture is well suited for large networks having thousands of pieces of user equipment, when fault tolerance is crucial, and when low-latency local decision-making is necessary. However, because there are several controllers, distributed control models have trouble maintaining consistency and typically take longer to optimize globally [22]. On the other hand, the term \"hybrid (combined) control model\" refers to a combination of completely centralized control and completely distributed control, where the conceptually centralized controller may modify the control on the data plane elements from complete (completely centralized control) to null (completely distributed control), considering the network circumstances [23]. Thus, hybrid control is best suited when the size of the network is variable and the network conditions can change over time from stable to highly dynamic. Moreover, blockchain has been employed to improve different aspects of SDN. The most dominant application is the security and privacy that blockchains enable due to their immutable properties [24]. Like every paradigm, SDN has a number of difficulties. The original conceptually centralized SDN approach has a variety of problems, including that it has a single physically and conceptually centralized controller, which makes it less trustworthy due to one source of breakdown [25]. Furthermore, SDN has security risks in heavily mobile networks like automotive networks [26], and, because of the changeable network layout, routing is problematic [27, 28]. Other obstacles that SDN encounters include expansion, difficulty connecting with historical networks, and the absence of a few standards for the northbound interface [29]. Data/information are utilized to create knowledge with the aid of a knowledge creation technique in Knowledge-Defined Networking (KDN), which is used in updating application policies and aids in making decisions in control and management planes [30]. As an example, through conflicts between automotive actions, the idea of KDN was employed to investigate knowledge of hazard perception [31]. Furthermore, the significance of an area's centralization has been determined via automobile mobility analysis, and nodes in this knowledge-defined vehicular network are entitled to this perception [32]. Moreover, knowledge generated using data in the management information base can be deployed to aid in decision-making regarding network monitoring and network configuration in the management plane by jointly considering application policies. For example, when the knowledge plane detects that the bandwidth consumption of the network devices is high, if there is an application policy specifying that the network bandwidth consumption must be lower than a given threshold, then the network configuration module can dynamically reconfigure network devices such that bandwidth consumption is reduced. Likewise, in the control plane, based on data collected on security events, routing protocols, QoS data, traffic statistics, etc., the knowledge plane can generate knowledge that can be used to make decisions in the control plane.