## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Network Administration
#### 4.4.2 Mobility Administration  
Programs for mobility administration are employed to control how network participants and equipment travel across various network regions. By monitoring the position of the gadget in order to smoothly switch links between various network fields, such as mobile networks, wireless local area networks, automobile networks, etc., it ensures that operations are maintained while users travel across various zones. Generic mobility management--Blockchain and Multi-Agent Deep Reinforcement Learning (MADRL) have been suggested as a way for resource and mobility management where virtualized resource allocation is modeled as a Stackelberg game in KDNs using 5G in unmanned aerial vehicular networks [286]. Alternatively, another work uses blockchain and deep reinforcement learning for secure, optimized handover and service offloading in an ultra-dense edge computing network environment, which has shown reduced handover latency with low packet loss rates during handover [287]. Furthermore, with the use of blockchain technology and enquiry response methods, a Blockchain Integrated Network Function Management Scheme (BINFMS) is deployed for handling movement and network address translation while simultaneously obtaining the necessary mobility-related measurements [288]. Moreover, driven by existing security flaws in the distributed mobility management of hierarchical and flat distributed control architectures of ultra-dense 5G KDNs, a secure and intelligent Distributed Mobility Management (DMM) framework hasbeen proposed by employing blockchain and federated deep reinforcement learning, which has proven to work independently from the network layout while at the same time fulfilling distributed security needs [289]. Authentication handover--Authentication handover involves transferring authentication credentials from one network entity to another whenever a portable gadget switches between networks. Blockchain and artificial intelligence have been proposed to reduce re-authentication and repeated handover using public and private keys in IoT networks' Authentication Handover (IoTAH), which has resulted in low latency and overhead for handover [290]. Furthermore, blockchain has been utilized to create immutable ledgers, while deep learning has been used to classify users as legitimate or not by learning mobility patterns using channel state information to prevent impersonation attacks during handover authentication [291]. Moreover, by excluding re-authentication with less delay, work in [292] performs efficient authentication handover by utilizing credit-based blockchain consensus, where credit is assigned to the secondary peers upon successful involvement in consensus verified by a local service center using Reinforcement Learning with Actor Critic-based Fuzzy Neural Network (RLAC-FNN). Additionally, for knowledge-defined heterogeneous 5G IoT wireless body area networks, authentication handover by storing user credentials in the hierarchical blockchain while using a bio-signature validation authentication mechanism and by using an Artificial Electric Field Optimization (AEFO) algorithm and edge intelligent agents for handover using the State Action Reward State Action (SARSA) algorithm, considering access network constraints and matching theory, has been studied in [293]. Likewise, in [294], a secure Multi-Factor Authentication (MFA) technique for handover in industrial IoT networks has been investigated. This scheme, known as Authentication Transfer Learning Blockchain (ATLB), provides the usage of blockchain to confirm the open keys of network gadgets and log the geographical locations of individuals with the goal of protecting the key agreement system and handover procedure, along with transfer learning to improve the authentication process. Channel scheduling--Channel scheduling is the process of allocating and organizing communication channels or time slots to transmit data among multiple devices.