Analysis of the Features and Structure Behind Availability in Blockchain Using Altcoin


Because of its behavior-dependent properties on the volunteers who maintain the distributed ledger in the network, data availability is a security factor in a Blockchain that can be difficult to understand and explain. This is because it cannot be described cryptographically or mathematically due to the fact that it cannot be explained mathematically or cryptographically. In the majority of earlier approaches, successful projects with empirically effective availability, like Bitcoin and Ethereum, have frequently been used to analyze more active Blockchain networks. In light of this, the majority of the altcoins that split off from Bitcoin, even if they had nodes in the network (which seems counterintuitive given that they were unsuccessful projects and relatively unknown at the time), did not receive a great deal of focus in the analysis. Having said that, it is interesting to take into consideration that both can keep their availability. When taking into account the network structure that ensures availability, it is possible to investigate the features of Altcoin as a minimal component due to the fact that it is a failed project. On the other hand, it is difficult to do so with Bitcoin and Ethereum due to the complex components that make up those cryptocurrencies. As a result, we purposefully chose an altcoin that is falling in value based on the profile of our ideal user. Focusing on factors associated with continuity, such as initial node survivability, interest in software updates, and nodes with security risks, we investigated the aspects of the network that maintain availability and the user structure that underlies Altcoin. Specifically, we looked at how the network handles nodes with security risks. We made the discovery that the actions of early users and a small number of users who are community-friendly provide greater availability when compared to an organization that operates on its own. We also discuss the motivations of these users based on the costs associated with installing the nodes in the network.

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