Adaptive Reputation‐Based PBFT Algorithm With VRF‐Driven View Changes

ABSTRACT Recent studies have integrated reputation mechanisms into practical byzantine fault tolerance (PBFT) by evaluating nodes based on their historical performance‐nodes with superior performance receive higher reputations, while those with poorer performance receive lower reputations. Typically, the node with the highest reputation is selected as the primary to reduce the frequency of time‐consuming view changes—a protocol employed to rectify issues arising from a faulty primary. However, such reputation‐based approaches face two significant challenges: maintaining real‐time reputation accuracy requires updating node reputations after each consensus round, leading to considerable computational overhead, and selecting the highest‐reputation node renders it a prime target for malicious attacks. To address these challenges, we propose the adaptive reputation‐based PBFT algorithm with verifiable random function (VRF)‐driven view changes (ARVPBFT). ARVPBFT incorporates an adaptive reputation mechanism that dynamically adjusts the frequency of reputation updates based on view changes, substantially reducing computational overhead. Moreover, by integrating VRFs into the view‐change protocol, ARVPBFT ensures an unpredictable and fair selection among high‐reputation nodes, thereby further enhancing system security. Theoretical analysis and simulation results demonstrate that ARVPBFT significantly outperforms existing algorithms, ultimately achieving a more stable and efficient consensus mechanism.