Adaptive Restructuring of Merkle and Verkle Trees for Enhanced Blockchain Scalability
<|reference_start|>Adaptive Restructuring of Merkle and Verkle Trees for Enhanced Blockchain Scalability: The scalability of blockchain technology remains a pivotal challenge, impeding its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of Merkle and Verkle trees, fundamental components of blockchain architecture responsible for ensuring data integrity and facilitating efficient verification processes. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns, significantly reducing the average path length required for verification and, consequently, the computational overhead associated with these processes. Through a comprehensive conceptual framework, we delineate the methodology for adaptive restructuring, encompassing both binary and non-binary tree configurations. This framework is validated through a series of detailed examples, demonstrating the practical feasibility and the efficiency gains achievable with our approach. Moreover, we present a comparative analysis with existing scalability solutions, highlighting the unique advantages of adaptive restructuring in terms of simplicity, security, and efficiency enhancement without introducing additional complexities or dependencies. This study's implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification that could facilitate broader adoption of blockchain technology in finance, supply chain management, and beyond. As the blockchain ecosystem continues to evolve, the principles and methodologies outlined herein are poised to contribute significantly to its growth and maturity.<|reference_end|>
@article{kuznetsov2024adaptive,
    title={Adaptive Restructuring of Merkle and Verkle Trees for Enhanced
  Blockchain Scalability},
    author={Oleksandr Kuznetsov, Dzianis Kanonik, Alex Rusnak, Anton Yezhov,
  Oleksandr Domin},
    journal={Internet of Things, vol. 27, p. 101315, Oct. 2024},
    year={2024},
    doi={10.1016/j.iot.2024.101315},
    archivePrefix={arXiv},
    eprint={2403.00406},
    primaryClass={cs.CR}
}