AI-Driven Optimization of Proof-of-Stake Blockchain Validators

Authors

  • Rahul Arulkumaran Independent Researcher, Vishnu Splendor Apartments, Srinagar Colony, Hyderabad, 500073,
  • Dignesh Kumar Khatri Independent Researcher, Ahmedabad , Gujarat, India,
  • Viharika Bhimanapati Independent Researcher,Almasguda, Hyderabad, Telangana ,
  • Anshika Aggarwal Independent Researcher, Maharaja Agrasen Himalayan Garhwal University, Dhaid Gaon, Block Pokhra , Uttarakhand, India ,
  • Vikhyat Gupta Independent Researcher, Chandigarh University, Punjab ,

DOI:

https://doi.org/10.36676/irt.v9.i5.1490

Keywords:

AI, Proof-of-Stake, Blockchain, Validators, Machine Learning, Optimization, Reinforcement Learning, Predictive Analytics

Abstract

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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Published

2023-12-30
CITATION
DOI: 10.36676/irt.v9.i5.1490
Published: 2023-12-30

How to Cite

Rahul Arulkumaran, Dignesh Kumar Khatri, Viharika Bhimanapati, Anshika Aggarwal, & Vikhyat Gupta. (2023). AI-Driven Optimization of Proof-of-Stake Blockchain Validators. Innovative Research Thoughts, 9(5), 315–333. https://doi.org/10.36676/irt.v9.i5.1490