Poster: Task Difficulty Adjustment in the Energy-Recycling Consensus Mechanism
Hao Zeng, Man Li, Helei Cui, Yuefeng Du, Zhiwen Yu, Bin Guo · 2023
An increasing number of energy-recycling consensus mechanisms are being employed to address the drawback of proof of work (PoW) wasting computation and energy. For instance, the computing power wasted in solving difficult but meaningless PoW puzzles is used to conduct practical federated learning tasks and train deep learning models. However, there remains a neglected issue of task difficulty adjustment. To address this problem, we propose a method for measuring task difficulty and an algorithm for adjustment to achieve controlled minting and stable transaction processing capacity for cryptocurrency based on energy-recycling consensus mechanisms. Our research evaluates the effectiveness of this algorithm and highlights the potential benefits of this approach.