PBFT Consensus Algorithm Based on Communication Delay Clustering and Node Reputation

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PBFT Consensus Algorithm Based on Communication Delay Clustering and Node Reputation

Introduction

Blockchain technology has emerged as a decentralized database that integrates peer-to-peer (P2P) networks, consensus algorithms, and asymmetric encryption. It has found applications in various fields, including education, healthcare, finance, and the Internet of Things (IoT). The consensus algorithm, a core component of blockchain, ensures the stability and security of distributed systems. Different blockchain frameworks employ different consensus mechanisms depending on their application scenarios.

In public blockchains, delegated proof-of-stake (DPoS) addresses the energy inefficiency of proof-of-work (PoW) but introduces centralization risks by electing a limited number of representative nodes. In consortium blockchains, practical Byzantine fault tolerance (PBFT) improves efficiency by reducing the algorithm’s complexity from exponential to polynomial. However, PBFT still suffers from high communication latency and an unstable primary node selection mechanism.

Existing improvements to PBFT primarily focus on two approaches:

  1. Group-based consensus – Reducing communication overhead by partitioning nodes into smaller groups.
  2. Reputation models – Enhancing security by selecting high-reputation nodes as primary nodes.

Despite these improvements, most group-based PBFT variants do not account for communication delays between nodes, leading to suboptimal performance. Additionally, reputation-based approaches often ignore latency factors, making them susceptible to predictable attacks and frequent view changes.

To address these limitations, this paper proposes CD-PBFT, a PBFT consensus algorithm based on communication delay clustering and node reputation. The key contributions include:
• A novel CD_K-means clustering algorithm that optimizes node grouping by incorporating communication delays.

• A comprehensive reputation model that evaluates nodes based on delay index, consensus behavior, and historical reputation.

• An optimized primary node selection mechanism that prioritizes nodes with high reputation and low variance.

Experimental results demonstrate that CD-PBFT significantly improves throughput and reduces latency compared to traditional PBFT and other clustering-based variants.

Problem Analysis

Limitations of PBFT

PBFT operates in five phases: request, pre-prepare, prepare, commit, and reply. While it ensures Byzantine fault tolerance, its communication complexity grows quadratically with the number of nodes, leading to scalability issues.

Two primary methods are used to select the primary node in PBFT:

  1. Round-robin view change – Nodes take turns being the primary node, which allows malicious nodes to disrupt consensus.
  2. Reputation-based selection – High-reputation nodes are prioritized, but this makes them predictable targets for attacks, leading to frequent view changes.

Issues with K-means Clustering

K-means clustering is commonly used to partition nodes into groups, but it relies solely on spatial distance, ignoring communication delays. Since PBFT requires frequent inter-node communication, latency significantly impacts performance. Traditional K-means clustering fails to optimize for this factor, resulting in inefficient group formations.

CD-PBFT Algorithm

Algorithm Overview

CD-PBFT consists of three main steps:

  1. CD_K-means clustering – Nodes are grouped based on a hybrid distance metric combining Euclidean distance and communication delay.
  2. Hybrid consensus mechanism – A combination of PBFT (for inter-group consensus) and an improved Raft algorithm (for intra-group consensus) is used.
  3. Reputation model – Nodes are evaluated based on delay index, consensus behavior, and historical reputation. The primary node is selected based on high reputation and low variance.

CD_K-means Clustering

The CD_K-means algorithm enhances traditional K-means by incorporating communication delay as a clustering feature. The key steps are:

  1. Communication Delay Calculation – The total delay between nodes includes transmission, propagation, processing, and queuing delays.
  2. Hybrid Distance Metric – Combines Euclidean distance and communication delay with adjustable weights.
  3. Optimal Grouping – Nodes are assigned to clusters such that the sum of intra-cluster delays is minimized.

This approach ensures that nodes within the same group have minimal communication delays, reducing overall consensus time.

Node Types and Reputation Model

Nodes are categorized into three types:

  1. Primary Node – Coordinates consensus across groups.
  2. Consensus Node – Participates in intra-group consensus.
  3. Supervisory Node – Monitors group behavior and ensures Byzantine fault tolerance in Raft consensus.

The reputation model evaluates nodes based on:
• Delay Index – Measures relative communication efficiency.

• Consensus Behavior – Rewards honest behavior and penalizes malicious actions.

• Historical Reputation – Considers past performance to ensure stability.

Nodes with high reputation and low variance are prioritized for primary node selection, reducing the risk of malicious attacks.

Hybrid Consensus Mechanism

After clustering, CD-PBFT employs a two-tier consensus process:

  1. Inter-group PBFT – A committee of group leaders (primary nodes) uses PBFT to reach global consensus.
  2. Intra-group Raft – Within each group, an improved Raft algorithm ensures efficient consensus under Byzantine conditions.

Supervisory nodes enhance Raft’s security by:
• Comparing messages to detect inconsistencies.

• Validating leader behavior through cross-group verification.

• Triggering leader re-election if malicious activity is detected.

This hybrid approach reduces communication overhead while maintaining Byzantine fault tolerance.

Performance Analysis

Security

CD-PBFT ensures security through:
• PBFT’s Byzantine fault tolerance in inter-group consensus.

• Supervisory nodes detecting and mitigating malicious leaders in Raft.

• Reputation-based primary node selection, reducing the likelihood of attacks.

Communication Overhead

Compared to PBFT and other clustering-based variants, CD-PBFT significantly reduces communication complexity. Experiments show:
• 90.2% lower overhead than PBFT.

• 58.1% lower overhead than K-means-based PBFT.

Experimental Results

  1. Primary Node Stability – CD-PBFT exhibits fewer view changes than reputation-only selection methods.
  2. Consensus Latency – CD-PBFT reduces average latency by 68.3% compared to PBFT.
  3. Throughput – CD-PBFT achieves 126.8% higher throughput than PBFT, making it suitable for high-performance consortium chains.

Conclusion

CD-PBFT addresses the limitations of traditional PBFT by optimizing node grouping, enhancing reputation evaluation, and improving primary node selection. Its hybrid consensus mechanism ensures efficiency and security, making it well-suited for large-scale consortium blockchain applications. Future work will explore dynamic node management and real-world deployment in scenarios like autonomous vehicle fleets.

doi.org/10.19734/j.issn.1001-3695.2024.07.0219

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