International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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GNN-Enhanced State Channel Optimization for Efficient Blockchain Micropayments

Author(s) Chidananda Singh Ningthoujam, Basanta Singh Thoudam, Bidyarani Devi Mutum
Country India
Abstract Blockchain networks face significant scalability challenges, particularly in handling micropayments efficiently. Current Layer-2 solutions, while promising, suffer from high channel opening costs and suboptimal routing efficiency, limiting their practical adoption for micropayment scenarios. This paper presents a novel Layer-2 scaling solution that leverages graph neural networks (GNNs) for optimal payment routing combined with predictive channel balancing mechanisms. Our approach ad- dresses the critical research gap in optimization algorithms for state channel routing and liquidity management. We introduce a comprehensive framework that integrates advanced machine learning techniques with blockchain technology to achieve substantial improvements in transaction throughput and cost reduction. Through extensive simulation and real-world testing, our solution demonstrates a 10-fold reduction in micropayment costs while maintaining instant settlement capabilities. The pro- posed system achieves an average transaction confirmation time of 0.2 seconds with fees reduced to 0.01% of transaction value. Our contribution includes a novel GNN-based routing algorithm, a predictive channel rebalancing mechanism, and a comprehensive evaluation framework that validates the effectiveness of our approach across various network topologies and transaction patterns.
Keywords Blockchain scalability, Layer-2 solutions, State channels, Graph neural networks, Micro- payments, Payment routing, Channel optimization
Field Computer Applications
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-09-20
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.56209
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E-ISSN 2582-2160
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