Search Results (69)

This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape $k=0.5$, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks. Full article

Practical Byzantine Fault Tolerance (PBFT) has been widely used in consortium blockchain systems; however, it suffers from performance degradation and susceptibility to Byzantine faults in complex environments. To overcome these limitations, this paper proposes RE-BPFT, an enhanced consensus algorithm that integrates a nuanced node credibility model considering direct interactions, indirect reputations, and historical behavior. Additionally, we adopt an optimized ID3 decision-tree method for node classification, dynamically identifying high-performing, trustworthy, ordinary, and malicious nodes based on real-time data. To address issues related to centralization risk in leader selection, we introduce a weighted random primary node election mechanism. We implemented a prototype of the RE-BPFT algorithm in Python and conducted extensive evaluations across diverse network scales and transaction scenarios. Experimental results indicate that RE-BPFT markedly reduces consensus latency and communication costs while achieving higher throughput and better scalability than classical PBFT, RBFT, and PPoR algorithms. Thus, RE-BPFT demonstrates significant advantages for large-scale and high-demand consortium blockchain use cases, particularly in areas like digital traceability and forensic data management. The insights gained from this study offer valuable improvements for ensuring node reliability, consensus performance, and overall system resilience. Full article

The rapid growth of computationally intensive tasks in the Internet of Vehicles (IoV) poses a triple challenge to the efficiency, security, and stability of Mobile Edge Computing (MEC). Aiming at the problems that traditional optimization algorithms tend to fall into, where local optimum in task offloading and edge computing nodes are exposed to the risk of data tampering, this paper proposes a secure offloading strategy that integrates the Improved Polar Lights Optimization algorithm (IPLO) and blockchain. First, the truncation operation when a particle crosses the boundary is improved to dynamic rebound by introducing a rebound boundary processing mechanism, which enhances the global search capability of the algorithm; second, the blockchain framework based on the Delegated Byzantine Fault Tolerance (dBFT) consensus is designed to ensure data tampering and cross-node trustworthy sharing in the offloading process. Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle’s power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV. Full article

Federated learning (FL), as a distributed machine learning framework, enables multiple participants to jointly train models without sharing data, thereby ensuring data privacy and security. However, FL systems still struggle to escape the typical poisoning threat launched by Byzantine nodes. The current defence measures almost all rely on the anomaly detection of local gradients in a plaintext state, which not only weakens privacy protection but also allows malicious clients to upload malicious ciphertext gradients once they are encrypted, which thus easily evade existing screenings. At the same time, mainstream aggregation algorithms are generally based on the premise that “each client’s data satisfy an independent and identically distributed (IID)”, which is obviously difficult to achieve in real scenarios where large-scale terminal devices hold their own data. Symmetry in data distribution and model updates across clients is crucial for achieving robust and fair aggregation, yet non-IID data and adversarial attacks disrupt this balance. To address these challenges, we propose FedOPCS, an optimized poisoning countermeasure for non-IID FL algorithms with privacy-preserving stability by introducing three key innovations: Conditional Generative Adversarial Network (CGAN)-based data augmentation with conditional variables to simulate global distribution, a dynamic weight adjustment mechanism with homomorphic encryption, and two-stage anomaly detection combining gradient analysis and model performance evaluation. Extensive experiments on MNIST and CIFAR-10 show that, in the model poisoning and mixed poisoning environments, FedOPCS outperforms the baseline methods by 11.4% and 4.7%, respectively, while maintaining the same efficiency as FedAvg. FedOPCS therefore offers a privacy-preserving, Byzantine-robust, and communication-efficient solution for future heterogeneous FL deployments. Full article

With the deep integration of distributed network technology and intelligent medical care, how to achieve efficient collaboration under the premise of safeguarding data security and system efficiency has become an important challenge for intelligent medical diagnosis systems. The traditional practical Byzantine fault tolerance (PBFT) algorithm has difficulty meeting the demands of large-scale distributed medical scenarios due to high communication overhead and poor scalability. In addition, the existing improvement schemes are still deficient in dynamic node management and complex attack defence. To this end, this paper proposes the VS-PBFT consensus algorithm, which fuses a verifiable random function (VRF) and reputation mechanism, and designs a distributed intelligent medical diagnosis collaboration system based on this algorithm. Firstly, we introduce the VRF technique to achieve random and unpredictable selection of master nodes, which reduces the risk of fixed verification nodes being attacked. Secondly, we construct a dynamic reputation evaluation model to quantitatively score the nodes’ historical behaviors and then adjust their participation priority in the consensus process, thus reducing malicious node interference and redundant communication overhead. In the application of an intelligent medical diagnosis collaboration system, the VS-PBFT algorithm effectively improves the security and efficiency of diagnostic data sharing while safeguarding patient privacy. The experimental results show that in a 40-node network environment, the transaction throughput of VS-PBFT is 21.05% higher than that of PBFT, the delay is reduced by 33.62%, the communication overhead is reduced by 8.63%, and the average number of message copies is reduced by about 7.90%, which demonstrates stronger consensus efficiency and anti-attack capability, providing the smart medical diagnosis collaboration system with the first VS-PBFT algorithm-based technical support. Full article

Blockchain technology uses a consensus mechanism to create and finalize blocks. The consensus mechanism affects the total performance parameters of the blockchain network, such as throughput. In this paper, we present “Nazfast”, a simplified proof of stake—Byzantine fault tolerance based consensus mechanism to create and finalize blocks. The presented consensus is completed in multiple folds. For block producer and validation committee selection, we used a secure and speeded-up election mechanism, S&Sem, in Nazfast. The consensus is designed for fast block finalization in a malicious environment. The simulation result shows that we approximately achieved three block finalizations in 1 s with almost similar latency. We reduced and fixed the number of validators in the consensus to improve the throughput. We achieved a higher throughput among other consensus of the same family. Because we reduced the number of validators, the safety parameters of the consensus are at risk, so we used Sea Shield to improve the overall consensus safety. This is another blockchain to save nodes’ details when they join/unjoin the network as validators. By using all three parts together, our system is protected from 28-plus different attacks, and we maintain a high decentralization by using S&Sem. Finally, we also enhance the incentive mechanism of consensus to improve the liveness of the network. Full article

The consensus protocol is a fundamental building block in distributed computing and has been widely used in blockchain systems in recent years. Paxos, introduced by Lamport, stands out as one of the most widely adopted consensus protocols and has found application in renowned distributed systems, including Google’s Spanner system. Abraham et al. analyzed the FaB Paxos protocol, a Byzantine version of Paxos. They abstracted the single-shot FaB Paxos into a Byzantine broadcast protocol and further gave an enhanced protocol known as Abraham et al.’s BB. Abraham et al.’s BB protocol achieved optimal two-round message interaction under good conditions, satisfying the optimal fault tolerance threshold of $n=5t-1$ where n represents total number of nodes in the system and t denotes the tolerable number of Byzantine nodes. This paper delves into scenarios wherein the actual number of Byzantine nodes surpasses the fault tolerance threshold during the operation of Abraham et al.’s BB protocol. To address this, we propose a forensic protocol designed to offer forensic support in cases of agreement violations. The forensic protocol aims to label Byzantine nodes through irrefutable evidence. We analyze the forensic protocol, elucidating the number of Byzantine nodes that the forensic protocol can label under different circumstances, along with the corresponding number of required messages. Additionally, we present an impossibility result, indicating that forensic support for Abraham et al.’s BB is impossible when the number of Byzantine nodes exceeds $2t-2$. Full article

Traditional intellectual property authentication relies on centralized intermediaries, which makes it difficult to address issues such as forgery, lack of trust, and opaque information. Combined with the characteristics of blockchain, such as decentralization, tampering, and traceability, these challenges can be effectively dealt with. Aiming at the shortcomings of traditional consensus algorithms in intellectual property authentication, such as high communication overhead and low efficiency, the improved PBFT (Practical Byzantine Fault Tolerance) algorithm (MBFT algorithm) is proposed and combined with the distributed database IPFS (Inter Planetary File System) to alleviate the pressure of blockchain data storage and enhance operational efficiency. The algorithm first adopts the evaluation system in the hierarchical mechanism, invokes the Fibonacci series incremental law to update the Score value of the nodes and sort them, and divides the nodes into the classification consensus layer, the consensus confirmation layer, and the supervision layer. Secondly, the Maglev algorithm is used to generate a lookup table and design a classification consensus strategy, which is divided into four consensus groups based on the characteristics of intellectual property categories, namely, the patent authentication consensus group, the trademark authentication consensus group, the copyright authentication consensus group, and the other types of authentication consensus group. Then, the algorithm optimizes the consistency protocol, carries out PBFT consensus once in each of the classification consensus layers and consensus confirmation layers, according to the consensus situation, and realizes the nodes’ dynamic update to ensure the consensus’s accuracy and reliability. The experiments show that the MBFT algorithm performs better in terms of communication complexity and throughput. As the number and size of files increase, the execution time of IPFS progressively lengthens. However, the overall performance still meets the actual demand. Compared with the traditional PBFT, MBFT improves the communication complexity by about 50% or more, the throughput is about 3 times the traditional PBFT, and the scalability and response speed of the system are significantly improved when the number of nodes increases. Full article

Aiming at the problems of significant communication overheads, the low reliability of primary nodes, and the insufficient dynamic adaptability of traditional consensus algorithms in intellectual property transaction scenarios, an Improved Practical Byzantine Fault Tolerant (IPBFT) algorithm based on the Chord algorithm and entropy weight method is proposed. Firstly, the Chord algorithm is employed to map nodes onto a hash ring, enabling dynamic grouping. Secondly, an entropy-based dynamic reputation model is constructed, quantifying the evaluation of node behaviors and calculating the overall reputation value. A three-level reputation classification mechanism is used to dynamically select primary and supervisory nodes, thereby reducing the probability of Byzantine nodes being elected. Then, a three-phase monitoring strategy for supervisory nodes is developed, which includes collection, review, and blackout. This improves the Raft consensus process, enhancing the detection and fault tolerance against malicious leaders. Finally, a grouped dual-layer consensus architecture is proposed. The lower layer uses an improved Raft algorithm for efficient consensus within groups, while the upper layer uses the PBFT algorithm for cross-group global consistency verification. Experimental findings demonstrate that the IPBFT algorithm is able to balance security, scalability, and consensus efficiency in a dynamic network environment, providing a better consensus solution for intellectual property transactions. Full article

With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design. Full article

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant consensus algorithm with three ways to improve PBFT. Firstly, 5G-PBFT constructed the reputation model based on node performance and behavior. The model dynamically selected consensus nodes based on the reputation value to ensure the reliability of the consensus node selection. Next, the algorithm selected the primary node using the reputation model and verifiable random function, giving consideration to the reliability of the primary node and the randomness of the selection process. Finally, we take advantage of the low latency feature of the 5G network to omit the submission stage to reduce the communication complexity from $O\left(N^2\right)$ to $O\left(N\right)$, where $N$ denotes the number of nodes. The simulation results show that 5G-PBFT achieves a 26% increase in throughput and a 63.6% reduction in transaction latency compared to the PBFT, demonstrating significant performance improvements. Full article

Aiming to address the shortcomings of traditional blockchain technologies, characterized by high storage redundancy and low transaction query efficiency, we propose a lightweight sender-based blockchain architecture (LSB). In this architecture, the linkage between blocks is associated with the user initiating the transaction, and the hash of the newly generated block is recorded in the user’s wallet, thereby facilitating transaction retrieval. Each user node must store only the blocks that pertain to it, significantly reducing storage costs. To ensure the normal operation of the system, the Delegated Proof of Stake based on Reputation and PBFT (RP-DPoS) consensus algorithm is employed, establishing a reputation model to select honest and reliable nodes for consensus participation while utilizing the Practical Byzantine Fault Tolerance (PBFT) algorithm to verify blocks. The experimental results demonstrate that LSB reduces storage overhead while enhancing the efficiency of querying and verifying transactions. Moreover, in terms of security, it decreases the likelihood of malicious nodes being designated as agent nodes, thereby increasing the chances of honest nodes being selected for consensus participation. Full article

This paper introduces RWA-BFT, a reputation-weighted asynchronous Byzantine Fault Tolerance (BFT) consensus algorithm designed to address the scalability and performance challenges of blockchain systems in large-scale IoT scenarios. Traditional centralized IoT architectures often face issues such as single points of failure and insufficient reliability, while blockchain, with its decentralized and tamper-resistant properties, offers a promising solution. However, existing blockchain consensus mechanisms struggle to meet the high throughput, low latency, and scalability demands of IoT applications. To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. This dual-layer design significantly improves performance, achieving higher throughput, lower latency, and enhanced scalability, while maintaining strong fault tolerance even in the presence of a substantial proportion of malicious nodes. Experimental results demonstrate that RWA-BFT outperforms HB-BFT and PBFT algorithms, making it a scalable and secure blockchain solution for decentralized IoT applications. Full article

Practical Byzantine Fault Tolerance (PBFT) is one of the most popular consensus mechanisms for the consortium and private blockchain technology. It has been recognized as a candidate consensus mechanism for the Internet of Things networks as it offers lower resource requirements and high performance when compared with other consensus mechanisms such as proof of work. In this paper, by considering the blockchain nodes are wirelessly connected, we model the network nodes distribution and transaction arrival rate as Poisson point process and we develop a framework for evaluating the performance of the wireless PBFT network. The framework utilizes slotted ALOHA as its multiple access technique. We derive the end-to-end success probability of the wireless PBFT network which serves as the basis for obtaining other key performance indicators namely, the optimal transmission interval, the transaction throughput and delay, and the viable area. The viable area represents the minimum PBFT coverage area that guarantees the liveness, safety, and resilience of the PBFT protocol while satisfying a predefined end-to-end success probability. Results show that the transmission interval required to make the wireless PBFT network viable can be reduced if either the end-to-end success probability requirement or the number of faulty nodes is lowered. Full article

In order to explore more spectrum resources to support sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensor paradigms is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attacks from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attacks, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on the sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. The numerical simulation results demonstrate that, compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities. Full article