A General Difficulty Control Algorithm for Proof-of-Work Based Blockchains
This addresses a key challenge for blockchain developers and users by improving difficulty adjustment, though it appears incremental as it builds on existing methods.
The paper tackles the problem of efficiently controlling block difficulty in Proof-of-Work blockchains by proposing a two-layer neural network algorithm, which shows better performance in simulations using real Ethereum data.
Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.