AI-Driven Optimization of Proof-of-Stake Blockchain Validators
DOI:
https://doi.org/10.36676/irt.v9.i5.1490Keywords:
AI, Proof-of-Stake, Blockchain, Validators, Machine Learning, Optimization, Reinforcement Learning, Predictive AnalyticsAbstract
A number of different industries have been completely transformed as a result of the introduction of blockchain technology, which offers decentralised, secure, and transparent platforms. A number of other consensus techniques have arisen, but Proof-of-Stake (PoS) has become a popular alternative to Proof-of-Work (PoW) owing to the fact that it is both scalable and efficient with regard to energy consumption. While validators are responsible for verifying transactions and establishing new blocks in a proof-of-stake blockchain, they also play an important role in safeguarding the integrity of the network.
We begin by doing a review of the present status of proof-of-stake (PoS) consensus mechanisms, focussing on the benefits that these mechanisms provide in comparison to proof-of-work (PoW) techniques, and outlining important problems such as validator selection, stake distribution, and attack resistance. In the next step, we provide AI-driven optimisation strategies that are capable of addressing these difficulties, with a particular emphasis on machine learning algorithms and predictive analytics. One example is the use of reinforcement learning to design techniques for optimum stake distribution among validators. On the other hand, supervised learning models may be used to forecast validator performance as well as possible dangers
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