AI-Assisted Consensus Mechanisms for Scalable Blockchain Networks
DOI:
https://doi.org/10.63345/sjaibt.v2.i2.301Keywords:
Blockchain, Consensus Mechanisms, Artificial Intelligence, Reinforcement Learning, Scalability, Decentralized Systems, Proof of Stake, Federated Consensus, Machine Learning, Predictive OptimizationAbstract
Blockchain technology has emerged as a transformative paradigm for secure, decentralized, and transparent data management. However, the rapid growth of decentralized applications (dApps), global transaction demands, and multi-chain ecosystems has exposed scalability bottlenecks in existing consensus mechanisms. Traditional models such as Proof of Work (PoW) and Proof of Stake (PoS), while effective in maintaining security, struggle with throughput, latency, and energy efficiency. Recent research highlights the potential of artificial intelligence (AI) to augment blockchain consensus by improving leader selection, optimizing validator participation, dynamically adjusting difficulty, and predicting network anomalies. This manuscript explores AI-assisted consensus mechanisms as a scalable alternative for next-generation blockchain systems. The paper conducts a comprehensive literature review of blockchain scalability challenges, outlines a methodology for integrating reinforcement learning (RL), deep learning, and predictive analytics into consensus protocols, and presents simulation-based results. Findings suggest that AI-enhanced consensus can achieve up to 70% improved throughput, reduce energy costs by 50%, and enhance fault tolerance by predicting malicious node behavior in advance. The study concludes that AI-assisted consensus mechanisms provide a sustainable path toward highly scalable, adaptive, and secure blockchain networks, with implications for finance, supply chains, IoT, and government applications.
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