## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Virtualization of Networks  
The procedure of establishing numerous conceptual networks on the foundation of actual network facilities using network splitting is known as network virtualization. This makes it possible for numerous networks to exist together while utilizing identical physical assets, where each conceptual network shows up as an independent system with unique network strategies and setups. A virtualized network enables either traffic flow level or network-specific slicing, which divides the real network into many simulated networks according to various flows [364].  
To safeguard proprietors of wireless equipment from recurrent expenditure attacks, which assign the same radio frequency chunk to several simulated wireless networks, researchers have recently proposed adding apps with policies to incorporate Blockchain Technology for Network Virtualization (BTNV), where the blockchain serves to prevent recurrent expenditure attacks using reputation while machine learning is utilized to predict QoS requirements to optimally allocate wireless resources [365]. Furthermore, Deep Q Learning (DQL) is utilized in a Knowledge-Defined Vehicular Network (KDVN) to solve an optimization problem of allocating computation and networking resources for virtualizing resources by reaching consensus using a permissioned blockchain [366]. Moreover, to enable a service-focused blockchain system with network function virtualization, Distributed Ledger Technology (DLT) was initially put out as a platform for QoS-based service delivery along with decoupled management and control functions realized using smart contracts, where the consensus on the virtualization management and orchestration is modeled as an optimization problem solved through deep reinforcement learning [367].  
For slicing an autonomous radio active network, a Consortium Blockchain-based Decentralized Spectrum Trading (CBDST) platform for buying and selling among spectrum providers and buyers, where the Stackleberg game framework is used for incentive maximization among the infrastructure providers, has been studied in [368]. Moreover, SliceBlock is a system designed for network slicing in sixth-generation mobile network environments utilizing KDN, where network slicing has been realized using Generative Adversarial Networks (GANs), in which a Directed Acyclic Graph-based blockchain along with a Proof-of-Space consensus algorithm is utilized for security, while Markov decision-making is used for authentication and handover [369]. Similarly, the Blockchain Network Slicing Broker (BNSB) system uses blockchain for network slicing and is an education-driven technique for allocating network resources. It allows resource vendors to fluidly contract assets to ensure improved performance of the network services using primary and secondary interactions among users, where deep reinforcement learning is utilized for the resource allocation problem [370]. Alternatively, a framework known as Skunk enables distributed network slicing, which uses a blockchain-based bidding system for dynamic resource assignment where resource providers lease services for better performance of the services and blockchain-based federated learning is utilized to preserve data privacy [371].  
A consortium blockchain that supports hyper-ledger smart contracts has been utilized for Secure Resource Trading (SRT) among mobile network operators, where a Dueling Deep Q (DDQ) network has been utilized for optimal pricing and demand policies in order to achieve Stackelberg equilibrium [372]. Likewise, in [373], a Two-Tier Resource Allocation Scheme (TTRAS) to obtain network segmentation that models trading between mobile virtual network providers and end appliances as a two-phase Stackelberg contest has been assessed. The upper tier uses federated deep reinforcement learning based on the Markov decision process for assigning assets.