Enhancing Consensus Mechanisms in Consortium Blockchains: A Novel Approach with Backpropagation Neural Network, Segmented DAG, and Genetic Node Selection

• Author(s): Bhaumik Tyagi ; Anshika Bansal ; Sarthak Pasricha ; Rahul Kumar ; Daamini Batra
• Paper ID: 1705173
• Page: 1-14
• Published Date: 02-11-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 5 November-2023

Abstract

Blockchain technology has garnered significant attention for its potential to transform various industries, with a pivotal role played by consensus algorithms in ensuring the security and reliability of blockchain networks. This paper introduces a novel approach to consensus algorithm design, presenting a solution rooted in the Segmented Directed Acyclic Graph (DAG) and Backpropagation (BP) Neural Network for Consortium Blockchain. In contrast to conventional consensus mechanisms, the proposed algorithm harnesses the power of Segmented DAG to structure transaction data, enhancing scalability and reducing latency. The integration of the BP Neural Network further enhances consensus decision-making by incorporating machine learning principles, adapting to changing network dynamics and optimizing transaction validation. Comprehensive experimentation and performance evaluation demonstrate the algorithm's superior throughput and fault tolerance when compared to conventional consensus methods. To enhance the performance of the consortium blockchain consensus, the Practical Byzantine Fault Tolerance (PBFT) consensus, widely used in consortium blockchains, is employed to reduce the number of consensus nodes, and optimize performance. Utilizing the PBFT consensus, high-performance nodes are screened to obtain a reliable and limited set of consensus nodes. A genetic algorithm-based blockchain consensus enhancement scheme is proposed, outlining the fitness function of blockchain nodes and employing the genetic algorithm to iteratively select consensus node groups with outstanding indicators, ultimately forming the nodes participating in consensus.

Keywords

Consortium Blockchain, Genetic algorithm, Backpropagation Neural Network, PBFT, Consensus algorithm, Segmented DAG.

Citations

IRE Journals:
Bhaumik Tyagi , Anshika Bansal , Sarthak Pasricha , Rahul Kumar , Daamini Batra "Enhancing Consensus Mechanisms in Consortium Blockchains: A Novel Approach with Backpropagation Neural Network, Segmented DAG, and Genetic Node Selection" Iconic Research And Engineering Journals Volume 7 Issue 5 2023 Page 1-14

IEEE:
Bhaumik Tyagi , Anshika Bansal , Sarthak Pasricha , Rahul Kumar , Daamini Batra "Enhancing Consensus Mechanisms in Consortium Blockchains: A Novel Approach with Backpropagation Neural Network, Segmented DAG, and Genetic Node Selection" Iconic Research And Engineering Journals, 7(5)