As the Bitcoin keeps increasing in value compared to other cryptocurrencies, more attention has given to Blockchain Technology (BT), which is the infrastructure behind the Bitcoin, especially on its role in addressing the problems of the classical centralized system. As a digital currency, Bitcoin is dependent on the decentralized cryptographic tools and peer-to-peer system. The digital currency implements a distributed ledger using Blockchain when verifying any type of transaction. In this paper, the aim is to describe how digital currency networks such as Bitcoin provides a "trust-less" platform for users to embark on money transfers without necessarily depending on any central trusted establishments such as payment services or financial institutions. Furthermore, this work comprehensively overviewed the basic principle that underly BT, such as transaction, consensus algorithms, and hashing. This study also provided a novel classification for blockchain types according to their system architecture and consensus strategy. For each type, our contribution was provided with an example, which clearly describes the blockchain features and the transaction steps. Our classification intended to help researchers understand and choose the blockchain for their application. The paper ends with the discussion of the differences between each type

<p>Congestion is one of the most important issues in communication networks which has attracted much research attention. To ensure a stable TCP network, we can use active queue management (AQM for early congestion detection and router queue length regulation. In this study, it was proposed to use the Grey Wolf Optimizer (GWO) algorithm in designing a fuzzy proportional integral (fuzzy-PI) controller as a novel AQM for internet routers congestion control and for achieving a low steady-state error and fast response. The suggested Fuzzy logic-based network traffic control strategy permit us to deploy linguistic knowledge for depicting the dynamics of probability marking functions and ensures a more accurate use of multiple inputs to depict the   the network’s state. The possibility of incorporating human knowledge into such a control strategy using Fuzzy logic control methodology was demonstrated. The postulated controller was compared to proportion integral (PI) through several MATLAB simulation scenarios. The results indicated the stability of the postulated controller and its ability to attain a faster response in a dynamic network with varying network load and target queue length.</p>

No abstract

No abstract