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An improved HotStuff consensus algorithm based on a double reputation evaluate model
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DOI：10.1007/s12083-026-02221-x.png)
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Abstract
En 中文
In order to solve the situation that the Leader node in the blockchain consensus algorithm HotStuff is randomly selected and the consensus success rate decreases significantly when facing the Byzantine node attack, this paper designs a consensus algorithm DR-HotStuff(Double Reputation evaluate- HotStuff) with double reputation evaluation model applicable to this complex environment. HotStuff), which maintains the advantages of low communication complexity of the HotStuff algorithm, combines with the dual reputation model to quickly screen out the Byzantine nodes in the consensus nodes, and assigns each node a corresponding identity based on different reputation values to reduce the probability that the Leader node is a malicious node, which further improves the stability and work efficiency of the blockchain. The experimental results show that the consensus algorithm has high throughput and low consensus delay, and can still eliminate most of the Byzantine nodes when the number of Byzantine nodes is greater than 30% and less than 50%, so that the efficiency of the blockchain can be restored to the optimal state through up to four rounds of consensus.
Keywords:
Blockchain
Consensus algorithms
Reputation models
Byzantine problem
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