A Review of Recent Trends in Blockchain Consensus Algorithms: Artificial Intelligence-Based Approaches

Abstract

Blockchain has emerged as an important technology, offering safe, decentralized, and transparent platforms for recording and validating transactions. Blockchain technology consist of several main components, i.e., consensus algorithm. The consensus algorithm guarantees that all participating nodes in a blockchain network agree over the data control. Traditional consensus methods, such as Proof of Work (PoW) and Proof of Stake (PoS), present issues in terms of energy consumption and attack vulnerability. To overcome these constraints, there has been a rising interest in incorporating Artificial Intelligence (AI) techniques, especially deep learning, into blockchain consensus algorithms. We highlight blockchain fundamentals in this article, while also stressing the importance of the consensus algorithm. Furthermore, we address the most recent advancements in blockchain consensus methods in both performance-based and reputation-based paradigm, emphasizing the use of AI inside these decentralized systems. The use of AI, especially deep learning, in consensus algorithms has the potential to overcome the limitations of previous techniques. Blockchain networks may improve its performance by employing AI capabilities. However, incorporating AI into blockchain consensus algorithms is having its own challenges. Therefore, we also highlight several issues related with AI-based techniques in blockchain consensus algorithms, such as dealing with the quality and variety of data utilized by consensus algorithms and maintaining the openness of the AI models. In addition to those challenges, we propose a future direction for the AI-based approach in blockchain, which includes merging the mechanisms of performance-based and reputation-based consensus algorithms to incorporate the merits of both methods.