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Transaction transmission model for blockchain channels based on non-cooperative games

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Abstract

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds (also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game theory. Channel balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: SilentWhispers and SpeedyMurmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay, and effectively decreases transmission overhead in comparison with its two competitive peers.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61872006), Scientific Research Activities Foundation of Academic and Technical Leaders and Reserve Candidates in Anhui Province (Grant No. 2020H233), Top-notch Discipline (specialty) Talents Foundation in Colleges and Universities of Anhui Province (Grant No. gxbj2020057), Startup Foundation for New Talents of NUIST, and in part by Deanship of Scientific Research (DSR) at King Abdulaziz University (Grant No. RG-30-135-42).

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Correspondence to Peiyun Zhang or Mengchu Zhou.

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Zhang, P., Li, C., Zhou, M. et al. Transaction transmission model for blockchain channels based on non-cooperative games. Sci. China Inf. Sci. 66, 112105 (2023). https://doi.org/10.1007/s11432-021-3362-9

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  • DOI: https://doi.org/10.1007/s11432-021-3362-9

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