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 the data consistency. It plays a central role in enhancing the 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 the increased communication overhead, longer consensus latency (CL), and risks of malicious attacks on the leader node. To overcome these challenges, this article proposes a new blockchain consensus mechanism, namely the reputation awareness randomization consensus mechanism in the blockchain systems (RARCs). This mechanism first builds an evaluation model for the nodes, dividing them into ordinary nodes and candidate nodes through the reputation assessment. Second, it constructs a consensus node selection strategy to select the high-quality consensus nodes from the candidate nodes. Finally, RARC establishes a leader node randomization selection mechanism, increasing the unpredictability of the leader node and reducing the probability of the malicious attacks. Through the theoretical analysis and simulation experiments, we demonstrate that the RARC can significantly reduce the CL, enhance the throughput, and increase the unpredictability of the leader node, thereby improving the performance and security of the blockchain systems.