# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Private Blockchains
#### 3.2.2 Raft
Raft is a leader-based consensus algorithm that has become a focal point for innovation. Researchers have adjusted its core principles to address challenges in real-time systems, adversarial environments, and cross-industry use cases. [PERSON] et al. [52] redesigned Raft to optimize its performance in high real-time and adversarial settings, demonstrating an enhanced fault tolerance through dynamic leader reconfiguration. This work laid the foundation for platform-specific adaptations, such as [PERSON] et al. [53] integrating Raft into Hyperledger Fabric. By improving the log replication protocol and leader election mechanism, measurable improvements in blockchain transaction throughput were achieved, highlighting Raft's versatility in permissioned blockchain architectures. Industry applications have driven the protocol's new implementations. In supply chain management, [PERSON] et al. [54] leveraged Raft's transparency guarantees to design a halal industry blockchain network, ensuring traceability across multi-channel transactions. Meanwhile, [PERSON] et al. [55] validated the applicability of Raft in healthcare through a Hyperledger-based patient data management system, emphasizing compliance with regulatory privacy standards. For enterprise financial systems, [PERSON] et al. [56] extended Raft's scalability through a Multi-Raft variant optimized for supply chain finance, introducing a weighted node election mechanism to balance load distribution in large consortium blockchains. The Internet of Things (IoT) paradigm has given rise to specialized Raft derivatives. [PERSON] et al. [57] combined Raft with multi-access edge computing (MEC) to develop a smart transaction migration framework that reduces latency in private blockchain networks while maintaining Byzantine fault tolerance. Building upon this, [PERSON] et al. [55] introduced a weighted leader selection algorithm that prioritizes gateway functionality, addressing resource heterogeneity in IoT deployments and mitigating Sybil attack risks. As decentralized systems expand into 6G infrastructure, the behavior of Raft in wireless environments has been rigorously analyzed. [PERSON] et al. [58] conducted a comparative study of Raft and PBFT under non-ideal THz/mmWave channel conditions, quantifying the performance degradation caused by Rayleigh fading and path loss. Additionally, [PERSON] et al. [59] established probabilistic reliability metrics for Raft in wireless mesh networks, enabling data-driven deployment strategies that consider node mobility and intermittent connectivity. [PERSON] et al. [60] systematically evaluated the protocol's versatility in the blockchain ecosystem, benchmarking Raft's efficiency in Hyperledger Fabric and Ethereum cloud deployments. Their findings revealed a context-dependent trade-off between consensus latency and resource utilization, providing architectural guidance for platform selection in enterprise environments. Overall, these studies demonstrate that Raft has evolved from a general consensus protocol into a modular framework adaptable to specific industry requirements. While successful in vertical applications, horizontal challenges remain, particularly in balancing the simplicity of the algorithm with the complexity of cross-domain interoperability. Emerging research directions include integrating machine learning into adaptive leader election and developing hybrid consensus models that combine the advantages of Raft with complementary protocols such as PBFT. These innovations are expected to further solidify Raft's role in next-generation decentralized systems, from IoT edge networks to smart infrastructures supporting 6G. As shown in Table 6, existing research on improvements to the Raft algorithm can be systematically categorized into three main directions: performance optimization, scenario-specific adaptation, and network environment adaptability enhancement.