基于激励相容的权益分散共识算法

Equity decentralized consensus algorithm based on incentive compatibility

PoW共识算法被证明是激励不相容的,存在高奖励差异下的算力中心化和极端情况下的分叉收敛速度较慢等问题。基于此,提出了一种基于激励相容的SSPoW共识算法。通过引入局部解来计算区块链的聚合算力,利用算力的显性量化加快分叉收敛速度,从而满足区块链的一致性。通过改进奖励方案实现激励相容,减少因高奖励差异导致的算力中心化问题。仿真结果证明,所提算法能有效削减奖励差异,并且效率高于传统Po W共识算法,对提高系统安全性和共识效率有积极意义。

The PoW consensus algorithm has been proved to be incentive incompatible, existing computing centralization under high reward differences and slow convergence of forks in extreme cases. Based on this, an incentive-compatiblebased consensus algorithm SSPoW was proposed. By introducing local solutions to calculate the computing power aggregated on the block chain, the explicit quantification of computing power was used to speed up the convergence of the fork, thus satisfying the consistency of the blockchain. Incentive compatibility was achieved by improving the reward scheme, which reduced the problem of computing centralization caused by high reward differences. Simulation results prove that the proposed algorithm could effectively reduce the reward differences and is more efficient than the traditional PoW consensus algorithm, which has positive implications for improving system security and consensus efficiency.