Instant resonance: Dual strategy enhances the data consensus success rate of blockchain threshold signature oracles

Abstract

With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an AI-driven dual optimization strategy to enhance the data consensus success rate of blockchain threshold signature oracles. Firstly, we introduce the Representative-Enhanced Aggregation Strategy (REP-AG), which leverages a Bayesian game model to improve the representativeness of node-submitted data, ensuring consistency with other nodes and thereby enhancing the availability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes’ access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the consensus success rate by approximately 56.6% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9% in consensus success rates across all scenarios.