Optimizing Blockchain Scalability: Enhancing Consensus Mechanisms with Nodetovector Algorithms

Abstract

Blockchain technology has revolutionized industries with its decentralized and secure data management and transaction capabilities. However, scalability remains a critical challenge as blockchain networks expand. This study investigates the integration of TensorFlow with Node2Vec embeddings to optimize consensus mechanisms, focusing on enhancing blockchain scalability. Using the Hyperledger Fabric framework, experiments simulated and analyzed system performance metrics including block size, timeout, arrival rate, and probability of timeout. Data analysis revealed significant variability and trends critical for machine learning modeling. The study shows that using the model with embeddings and Principal Component Analysis (PCA) for visualization, for feature reduction performed better than traditional Linear Regression. The Mean Squared Error (MSE) was 0.0341 compared to 0.0658 highlighting the effectiveness of AI techniques in improving abilities and addressing scalability challenges in networks. This research aims to advance analytics in technology by showcasing how integrating TensorFlow with Node2Vec embeddings can enhance network efficiency and scalability bridging the gap between theory and practice to drive innovation, in decentralized data management and secure transaction processing.