DPNPBFT, a dual primary node derivative of the PBFT blockchain consensus algorithm, is based on the separation of powers.

PROJECT TITLE :

A derivative PBFT Blockchain consensus algorithm with dual primary nodes based on separation of powers-DPNPBFT

ABSTRACT:

The Practical Byzantine Fault Tolerant (PBFT) consensus algorithm has many benefits, which has led to its widespread adoption. PBFT is an acronym for "Practical Byzantine Fault Tolerant." PBFT, on the other hand, is not appropriate for large-scale node scenarios due to the high Communication complexity it entails. Additionally, it has the obvious disadvantage of inadequate fault tolerance. The typical focus of PBFT algorithms, which are typically derived from them, is on simplifying Communication while simultaneously lowering the system's fault tolerance and security. In order to achieve the best possible balance of the aforementioned three performances, the Dual-Primary-Node derived Practical Byzantine Fault Tolerance (DPNPBFT) algorithm is proposed in this paper. In the first step of the DPNPBFT process, dual master nodes are chosen based on the principle of power isolation. In order to prevent the excessive centralization that would result from using a single master node, the two master nodes check each other's balances and supervise one another. Because the replica node only communicates with the master node, this not only saves time but also makes the Communication process simpler. In addition, we designed the architecture of DPNPBFT to achieve a fault tolerance rate of a practical 49%, and it is very similar to the algorithms that are currently used in the mainstream Proof of Work and Proof of Stake systems. The results of the experiments show that DPNPBFT has a Communication complexity of O(N) level and has outstanding performance against host node malicious activity. The number of Transactions Per Second that DPNPBFT processes remains constant at 1700. This demonstrates that DPNPBFT possesses the best performance balance and excellent comprehensive performance for large-scale application scenarios involving the Internet of Things.

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