# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 4 Analysis of the Improved PBFT Consensus Algorithm
### Select High-Quality Nodes for Participation in Consensus  
With the expansion of blockchain nodes, large-scale network situations experience a rapid increase in node count, which results in a notable decline in the effectiveness of the PBFT consensus. In various research efforts focused on enhancing PBFT consensus algorithms, numerous researchers have proposed implementing reward and penalty systems to identify honest or high-performing nodes for participation in the consensus, while other nodes serve as candidates and do not engage in the process. This strategy effectively reduces the number of participating nodes (e.g., selecting half or a third of the total nodes), and communication overhead between nodes can be reduced, thereby improving the throughput of consensus transactions. The process of selecting high-quality nodes for consensus participation is illustrated in Figure 11.  
[PERSON] et al. [98] proposed a QoS-aware trust service trust evaluation mechanism to achieve service reliability ranking. By integrating both static and dynamic QoS metrics to compute a global evaluation value, they select more reliable and secure nodes to participate in consensus, reducing the number of participating nodes and, in turn, lowering communication costs while enhancing consensus efficiency. [PERSON] et al. [99] introduced a secure and efficient PBFT consensus algorithm for vehicular networks based on a distributed blockchain structure. This algorithm incorporates a reputation model, selecting the top half of nodes by reputation to participate in consensus. After each round of consensus, node reputations are updated, which helps reduce the number of nodes and lower communication costs, improving system throughput. [PERSON] et al. [100] proposed an innovative, optimized Practical Byzantine Fault Tolerance consensus algorithm (T-PBFT) based on the EigenTrust model. This multi-stage consensus algorithm evaluates the trustworthiness of nodes through transactions interactions, selecting high-quality nodes to form the consensus \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Grouping Method** & **Fault Tolerance** & **Network Model** & **Reward/Penalty Mechanism** \\ \hline P-PBFT [91] & Grouping based on response speed between nodes & N/2 & Semi-Synchronous Network & No explicitly defined incentive mechanism. \\ \hline GPBFT [92] & Grouping based on EigenTrust values & N/2 & Semi-Synchronous Network & \
\begin{tabular}{c} May incentivize high-trust nodes by \\ prioritizing their participation. \\ \end{tabular} \\ \hline CBFT [93] & Grouping based on node response speed within a grid structure & N/2 & Semi-Synchronous Network & No explicitly defined incentive mechanism. \\ \hline CE-PBFT [94] & Grouping using K-means clustering & N/2 & Semi-Synchronous Network & No explicitly defined incentive mechanism. \\ \hline DLBFT [95] & Based on the Consistent Hashing Principle & N & Semi-Synchronous Network &
\begin{tabular}{c} Uses load-balancing rewards for \\ efficient participation. \\ \end{tabular} \\ \hline ST-PBFT [96] & Performance of BIM Information Exchange & N & Semi-Synchronous Network & No explicitly defined incentive mechanism. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A comparison of grouping methods for each algorithm. group. Only members of the consensus group can participate in the network consensus, significantly enhancing consensus efficiency. [PERSON] et al. [101] proposed 5G-PBFT, which introduces a reputation model based on node performance and behavior. This model dynamically selects consensus nodes based on their reputation scores to ensure the reliability of node selection. Subsequently, the algorithm leverages the reputation model along with a verifiable random function (VRF) to elect the primary node, considering both its reliability and the randomness of the selection process. Finally, by utilizing the low-latency characteristics of 5G networks, the commit phase is omitted, effectively reducing communication complexity. [PERSON] et al.