A Reputation Awareness Randomization Consensus Mechanism in Blockchain Systems

Blockchain, as an emerging technology, has gained widespread research in academia and industry due to its decentralization and traceability. As an important form of blockchain, consortium chains are often applied in the Internet of Things (IoT) to ensure the authenticity and reliability of data. Within consortium chains, the Practical Byzantine Fault Tolerance (PBFT) method is a key technology for ensuring data consistency. It plays a central role in enhancing system performance, security, and scalability. However, with the increase in the number of user nodes and the diversification of application scenarios, PBFT faces significant challenges in maintaining performance and security, particularly due to increased communication overhead, longer consensus latency, and risks of malicious attacks on leader node. To overcome these challenges, this paper proposes a new blockchain consensus mechanism, namely the reputation awareness randomization consensus mechanism in blockchain systems (RARC). This mechanism first builds an evaluation model for nodes, dividing them into ordinary nodes and candidate nodes through reputation assessment. Secondly, it constructs a consensus node selection strategy to select high-quality consensus nodes from the candidate nodes. Finally, RARC establishes a leader node randomization selection mechanism, increasing the unpredictability of leader node and reducing the probability of malicious attacks. Through theoretical analysis and simulation experiments, we demonstrate that RARC can significantly reduce the consensus latency, enhance the throughput, and increase the unpredictability of leader node, thereby improving the performance and security of blockchain systems.