RON‐based cross‐chain routing optimization strategy in metaverse

IET Blockchain Pub Date : 2024-06-10 DOI:10.1049/blc2.12078
Junjie Huang, Liang Tan, Jianmei Xiao
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引用次数: 0

Abstract

Metaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross‐chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross‐chain communication mode is dominated by direct‐connect routing, leading to network congestion and high propagation delay once the direct‐connect link fails and cannot be recovered quickly. To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross‐Chain_RON outperforms the direct‐connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.
基于 RON 的元宇宙跨链路由优化策略
元宇宙是数字世界与物理世界融合的新生态,基于数字孪生技术生成现实世界的镜像,并通过区块链的可信机制保证虚拟世界的公平性。跨链通信技术是实现区块链之间数据流通和协同处理的关键,它为元宇宙中区块链的大规模连接提供了通信基础。目前的跨链通信模式以直连式路由为主,一旦直连链路出现故障,无法快速恢复,就会导致网络拥塞和高传播延迟。为了优化直连路由,本文提出了一种基于 RON(Resilient Overlay Network)的跨链路由优化策略,即 Cross-Chain_RON,它首先应用 RON 重构直连路由模型,然后通过最短路径算法和策略路由选择最优链路,并结合 RON 性能数据库提高数据传输效率。最后,利用仿真工具对两种通信模式进行了仿真。实验结果表明,在网络环境较差的情况下,Cross-Chain_RON在延迟、数据传输成功率和吞吐量等方面都优于直连路由,但要以一定的系统开销为代价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.80
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0.00%
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