## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Trustworthiness
#### 4.2.2 Data Sharing  
In instances of case analysis, such as the investigation of the causes of accidents in an intelligent transportation system, untampered data are required to identify the root causes of the accidents. In these situations, the blockchain can come in handy by making data immutable so that it can be considered digital proof, whose credibility is further enhanced through the adoption of self-executing contracts to gather data in ambiguous situations [250].  
According to a study by [251], a system for safe data exchange in a commercial IoT network through the KDN approach, permissioned blockchain, and Federated Learning (FL) for safeguarded device interactions, where self-executing contracts operate for seeking and modifying archives in the blockchain, has recently been developed. A similar work points out the one potential site of breakdown and DDoS attacks in the centralized KDN architecture and proposes blockchain and artificial intelligence (federated learning) for safe data exchange in fifth-generation drone networks [252]. Furthermore, blockchain is implemented on a hyper-ledger fabric platform integrated with deep learning for the sharing of data in a distributed and automatic manner, eliminating the necessity for every demand for the exchange of data to be approved by the data propriator in an industrial healthcare KDN system known as Permissioned Blockchain Deep Learning (PBDL) [253]. Moreover, a foundation for private Medical Data Sharing (MDS) has become possible with the incorporation of collective authorization and encryption, driven by attributes for restricting access, self-executing contracts to support organization rationality, and blockchain for linking personal databases for safe data supply, where an intelligent artificial intelligence-driven IoT-based KDN is utilized to establish the sharing regulations [254]. Similarly, softwarized unmanned aerial vehicles make use of the KDN principle, which divorces components and control reasoning, while blockchain has been proposed to register, verify, and validate the communication using Proof of Concept consensus and smartcontracts, and deep learning has been utilized to generate knowledge regarding illegitimate transactions by analyzing the data [255].  
Blockchains can be used in sensitive data transmission systems to perform transactions without leaking the sensitivity of the data with the aid of smart contracts [256]. In healthcare systems to share sensitive health data, a system based on self-executing contracts has been investigated for precise authentication and sharing of medical data while protecting privacy by introducing anonymity noise into federated learning, known as the Medical Privacy Blockchain (MPBC) [257]. Moreover, in order to share data among different network operators, mutual trust is developed using distributed blockchain, which implements the creation of a platform for data exchange in artificial intelligence-driven cognitive networks utilizing self-executing contracts and fine access management [258].  
In order to analyze the trustworthiness of data, a fuzzy logic engine has been utilized, where IoT data gathering is carried out using blockchain while additional encryption is used to secure the data [259]. Moreover, ANFPB is a blockchain-based adaptive neuro-fuzzy-based payment for safe data exchanging in an Internet of Vehicles (IoV) network where a neuro-fuzzy system is used to evaluate rewards based on different automobile parameters while the blockchain preserves privacy [260].