A consensus-based solution for cryptocurrencies arbitrage bots in intelligent blockchain^☆

Abstract

Intelligent blockchain is an emerging field that integrates Artificial Intelligence (AI) techniques with blockchain networks, with a particular emphasis on improving the performance of blockchain, especially in cryptocurrencies exchanges. Meanwhile, arbitrage bots are widely deployed and increasing in intelligent blockchain. These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning, generating substantial profits at the expense of ordinary users. In this paper, we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol, which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies. Specifically, we present two signal asynchronous common subset protocols, the more optimal one with only constant time complexity. We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment. The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.

Keywords

Artificial intelligence / Blockchain / Arbitrage bots / Byzantine consensus / Asynchronous network

CRediT authorship contribution statement

Lingyue Zhang: Writing - review & editing, Writing - original draft, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Zongyang Zhang: Writing - review & editing, Validation, Supervision, Formal analysis, Data curation, Conceptualization. Tianyu Li: Software, Resources, Investigation, Data curation. Shancheng Zhang: Writing - review & editing, Resources, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by the National Key R&D Program of China under Grant (2022YFB2702702), in part by the National Natural Science Foundation of China under Grants (62372020, 72031001), in part by the Beijing Natural Science Foundation under Grants (L222050), and in part by the Fundamental Research Funds for the Central Universities under Grant (YWF-23-L-1032).

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