Practical Byzantine Fault Tolerance Consensus Research Articles

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

Improvement of PBFT Consensus Algorithm Based on Affinity Propagation Clustering in Intellectual Property Transaction Scenarios

In response to the problems of random selection of primary nodes, high communication complexity, and low consensus efficiency in the current consensus mechanism for intellectual property transactions, a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on the Affinity-Propagation (AP) clustering algorithm, termed AP-PBFT, is proposed. Firstly, the election strategy of the leader node is constructed based on the reputation mechanism; the reward and punishment mechanism is designed to achieve the dynamic adjustment of the reputation value of the nodes in the PBFT consensus process, and the number of votes among the nodes is introduced to determine the node’s reputation value in collaboration with the reward and punishment mechanism to guarantee the precise ordering of the nodes. Secondly, nodes with high reputation values are selected as cluster centers to run the AP clustering algorithm, and clustering groups of knowledge property transaction nodes are constructed based on responsibility and availability. Finally, the three-stage consensus process of the PBFT consensus algorithm is optimized, and the consensus task is decomposed into two layers: the intra-consensus group and the inter-leader node group, reducing the communication complexity of transaction data in the blockchain. Experimental findings indicate a significant performance improvement of the algorithm over the PBFT consensus algorithm in communication complexity, throughput, and consensus efficiency in the simulation environment of multiple types of transactions in intellectual property transactions, including different types of large-scale transaction scenarios, such as purchases, sales, licenses, and transfers.

Multi-Task Learning for PBFT Optimisation in permissioned Blockchains

Finance, supply chain, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT) which tolerates up to one-third Byzantine nodes, performs within partially synchronous systems and has a superior throughput compared to other protocols. It has, however, an important bandwidth consumption: 2N(N-1) messages are exchanged in a system composed of N nodes to validate only one block.It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security, rapidity, and availability. In this paper, we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus. It reflects their level of security, rapidity and availability throughout the consensus. We first investigate different Single-Task Learning techniques to classify the nodes within our dataset. Then, using Multi-Task learning techniques, the results are way more interesting with classification accuracies over 98%. Integrating nodes classification as a preliminary step to the PBFT protocol optimizes the consensus. In the best cases, it is able to reduce the latency by up to 94% and the communication traffic by up to 99%.

Performance of PBFT Consensus under Voting by Groups

Practical Byzantine Fault Tolerance (PBFT) is the protocol of choice for many applications that require distributed consensus between a number of participant nodes. While PBFT assumes a single voting committee, many applications recognize different groups of participants that need to reach a consensus separately before accepting a proposal. To this end, we propose to count the votes by separate groups or committees of participating nodes, instead of all together as in the original PBFT. We then investigate the performance impact of this approach on the mean time to accept a data block and the number of nodes involved in making the final decision. Our results indicate that the proposed solutions impose a slight performance penalty which may be countermanded by reducing the quorum numbers needed in different subsets of the original committee.

Research on evolutionary game of digital twin data information sharing based on blockchain technology

Due to the significant characteristics of decentralization and tamper resistance of blockchain technology, it helps to solve the problems of timeliness and slow permission control in traditional information sharing. However, most researchers are committed to exploring how to improve the efficiency, reliability, and privacy security of digital twin data. There is a lack of research on issues such as blockchain participants free-riding and not actively participating in information sharing, and how to promote information sharing through incentives. Therefore, this paper designs a distance grouping based practical byzantine fault tolerance consensus algorithm to address the credibility issue of transmitted data, thereby improving the efficiency of negotiation. The feasibility of the algorithm is verified through cases analysis. In addition, for the problem of low subjective willingness of information sharing, this paper designs an information sharing incentive strategy based on blockchain intelligent contracts, establishes digital twin data sharing evolutionary game models with reputation incentives and non-reputation incentives, so as to explore the impact of reputation incentive on evolutionary stability strategies. Finally, simulation analysis is conducted on the evolutionary game model of digital twin data sharing. The impact of several key indicators such as data complementarity, trust, positive incentive coefficient, and data sharing cost on the evolution process of information sharing are considered, and corresponding management insights are obtained.

Improvement of Practical Byzantine Fault Tolerance Consensus Algorithm Based on DIANA in Intellectual Property Environment Transactions

In response to the shortcomings of the consensus algorithm for intellectual property transactions, such as high communication overhead, random primary node selection, and prolonged consensus time, a Practical Byzantine Fault Tolerance (PBFT) improvement algorithm based on Divisive Analysis (DIANA) D-PBFT algorithm is proposed. Firstly, the algorithm adopts the hierarchical clustering mechanism of DIANA to cluster nodes based on similarity, enhancing node partition accuracy and reducing the number of participating consensus nodes. Secondly, it designs a reward and punishment system based on node ranking, to achieve consistency between node status and permissions, timely evaluation, and feedback on node behaviours, thereby enhancing node enthusiasm. Then, the election method of the primary node is improved by constructing proxy and alternate nodes and adopting a majority voting strategy to achieve the selection and reliability of the primary node. Finally, the consistency protocol is optimised to perform consensus once within the cluster and once between all primary nodes, to ensure the accuracy of the consensus results. Experimental results demonstrate that the D-PBFT algorithm shows a better performance, in terms of communication complexity, throughput, and latency.

Double‐layer Byzantine fault‐tolerant grouping consensus algorithm based on raft

AbstractAddressing the scalability issues, excessive communication overhead, and challenges in adapting to large‐scale network node environments faced by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm currently employed in consortium blockchains, this paper proposes a Double Layer Consensus Algorithm Based on RAFT and PBFT Consensus Algorithms (DLCA_R_P). The nodes in the blockchain are initially divided into several groups to form the lower‐layer consensus network. Subsequently, the leaders of these groups constitute the upper‐layer consensus network, creating a dual‐layer consensus network structure. Within the lower‐layer consensus network, the PBFT consensus algorithm is employed for consensus among the groups, while the primary accountants form the upper‐layer RAFT consensus network. The algorithm incorporates a supervision mechanism and a reputation mechanism to enhance the security of the consensus network. Additionally, a grouping mechanism is introduced to transform the consensus network into a dynamic structure. Experimental results analysis demonstrates that compared to traditional PBFT consensus algorithms, DLCA_R_P reduces consensus latency by two orders of magnitude and improves throughput by one order of magnitude in a scenario with 100 nodes. Furthermore, it exhibits significant advantages over other improved algorithms. Thus, the DLCA_R_P consensus algorithm exhibits excellent scalability and can be widely applied in various scenarios within consortium blockchains.

Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm.

A Dynamic Adaptive Framework for Practical Byzantine Fault Tolerance Consensus Protocol in the Internet of Things

A Dynamic Adaptive Framework for Practical Byzantine Fault Tolerance Consensus Protocol in the Internet of Things

An improved PBFT consensus mechanism with trust value evaluation application in the agricultural product trusted traceability system

Blockchain has the advantages of tamper proof, high reliability and traceability, so it can better support the agricultural product traceability system. However, the agricultural product supply chain has the characteristics of long chain and decentralized production, which makes the application of the combination of blockchain technology and agricultural product traceability have many nodes, large system overhead and other problems. In order to solve the above problems, this paper proposes a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism based on trust value evaluation, and uses this optimization algorithm to propose an architecture model to meet the demand for trusted traceability of agricultural product supply chain, so as to realize the distributed storage and security protection of supply chain data. In order to ensure the effective operation of the blockchain system, this paper studies the consensus mechanism in the blockchain network, designs a trust value evaluation model to calculate the trust value of nodes, and selects trusted nodes to join the consensus group based on the trust value, which ensures the credibility of the consensus nodes. At the same time, the consistency protocol is optimized to reduce the communication overhead in the consensus process. The experimental results show that the consensus mechanism improved in this paper has certain advantages in communication overhead, transaction delay and throughput. Compared with the existing storage model, the model described in this paper has higher security and throughput efficiency, effectively ensures the reliable traceability of data, and can be better applied to the traceability scenario of agricultural product supply chain.

An Efficient and Secure Blockchain Consensus Protocol for Internet of Vehicles

Conventional blockchain consensus protocols tailored for the Internet of Vehicles (IoV) usually face low transaction throughput, high latency, and elevated communication overhead issues. To address these issues, in this paper, we propose ESBCP, an efficient and secure blockchain consensus protocol for the IoV environment. Firstly, considering the significant performance differences among nodes in the IoV, we designed a blockchain consensus model for the IoV. Roadside units execute a trust evaluation mechanism to select high-quality vehicle nodes for the consensus process, thereby reducing the likelihood of malicious nodes in the consensus cluster. Secondly, we designed a node partition strategy to adapt to the dynamic feature of the IoV. Finally, addressing the mobility of nodes in the IoV, we introduced a dynamic unique node list. Vehicle nodes can promptly select nodes with high reliability from the list of communicable nodes to join their unique node list, while also promptly removing nodes with low reliability from their unique node list. Combining these strategies, we propose DK-PBFT, an improved Practical Byzantine Fault Tolerance consensus algorithm. The algorithm meets the efficiency and mobility requirements of vehicular networks. Through theoretical analysis, ESBCP could prevent external and internal security risks while reducing communication overhead. Experimental verification demonstrated that ESBCP effectively reduces consensus latency and improves transaction throughput. Our proposed ESBCP can be used in other application scenarios that require high consensus efficiency.

BW-PBFT: Practical byzantine fault tolerance consensus algorithm based on credit bidirectionally waning

BW-PBFT: Practical byzantine fault tolerance consensus algorithm based on credit bidirectionally waning

Fibonacci Group Consensus Algorithm Based on Node Evaluation Mechanisms

In response to challenges posed by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, where all nodes are involved in the consensus process, issues such as high communication overhead in the three-phase procedure, random selection of master nodes, and the absence of reward and penalty mechanisms are addressed. This leads to the proposal of a Speculative Practical Byzantine Fault Tolerance (SP-PBFT) consensus algorithm based on a node evaluation mechanism for speculative grouping. Firstly, the consensus protocol was optimized, and a timeout mechanism was proposed to divide the consensus process into an optimistic mode and a pessimistic mode, which reduced the communication overhead under the premise of resisting Byzantine node attacks. Secondly, a node evaluation mechanism was proposed to calculate the node reputation value according to the node’s historical behavior and give the corresponding reward or punishment, which reduced the possibility of malicious nodes participating in the consensus process. Finally, the Fibonacci grouping mechanism was used to reduce the number of nodes participating in the consensus process, fundamentally improving the consensus efficiency, avoiding the problem of centralization of the consensus process caused by the cumulative reputation value of nodes, and improving the enthusiasm of consensus nodes. Simulation experiments using Docker containers to simulate multiple nodes show that the SP-PBFT consensus algorithm proposed in this paper has better performance than the PBFT consensus algorithm and other improved algorithms in terms of consensus delay, throughput, fault tolerance and communication complexity.

An improved PBFT consensus algorithm based on grouping and credit grading

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.

Multi‐stage energy‐risk adjustments using practical byzantine fault tolerance consensus for blockchain‐powered peer‐to‐peer transactive markets

AbstractThe energy risk associated with distributed energy resources (DERs) is inevitable in Peer‐to‐Peer (P2P) transactive energy markets owing to mismatches between energy commitments and metered measurements. However, adjusting these possible mismatches by progressive revision of the energy commitments in the rolling time horizon mitigates the energy risk, and thereby mitigates the financial risk for prosumers. In this study, the conditional value at risk (CVaR) is used to estimate the risk value for each prosumer. The energy offers that are riskier than CVaR‐based threshold values are reduced in an “adjustment bid”. A new pricing mechanism for these adjustment bids is introduced, which varies with historical deviations of a prosumer from energy commitments. This market framework and pricing mechanism are simulated through a blockchain network hosted on a Python Django server using the practical Byzantine fault tolerance consensus algorithm to guarantee network immutability and data privacy. Efforts to mitigate such mismatches between ex‐ante and ex‐post energy values incentivise risk‐aware participation in P2P markets. In addition, the welfare of both prosumers and consumers improves with their participation in the proposed market framework. Furthermore, implementing a network using blockchain technology guarantees the privacy of bidding data and provides a secure transaction platform.

ULS-PBFT: An ultra-low storage overhead PBFT consensus for blockchain

Since the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism can avoid the performance bottleneck of blockchain systems caused by Proof of Work (PoW), it has been widely used in many scenarios. However, in the blockchain system, each node is required to back up all transactions and block data of the system, which will waste a lot of storage resources. It is difficult to apply to scenarios with limited storage resources such as unmanned aerial vehicle networks and smart security protection; thus, it is often used in small-scale networks. In order to deploy PBFT-based blockchain systems in large-scale network scenarios, we propose an ultra-low storage overhead PBFT consensus (ULS-PBFT), which groups nodes hierarchically to limit the storage overhead within the group. In this paper, we first propose an optimal double-layer PBFT consensus from the perspective of minimizing the storage overhead, and prove that this consensus can significantly reduce the storage overhead. In addition, we also investigate the superiority of ULS-PBFT in terms of communication overhead while setting the security threshold in the presence of the possibility of Byzantine nodes. The simulation results demonstrate the advantages of ULS-PBFT. Then, we extend such grouping idea to the blockchain system with X-layer PBFT and analyze its storage and communication overhead. Finally, the node grouping strategy of double-layer PBFT is studied for four application scenarios when the performance of storage overhead, communication overhead, and security are considered comprehensively.

Practical Byzantine Fault Tolerance Based Robustness for Mobile Crowdsensing

Mobile crowdsensing (MCS) has become a prominent paradigm to collect and share data based on sensing devices with built-in sensors in the Internet of Things era. Nevertheless, conventional MCS confronts various security and privacy vulnerabilities in terms of decentralized, openness, and non-dedicated properties. Currently, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure, which reduces the effectiveness and robustness of the MCS system. Meanwhile, fake task attack is a serious threat, as it would drain excessive resources from the participant devices and clog the MCS servers to disrupt the services offered by the MCS. To address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. Moreover, the distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain uses a practical Byzantine fault tolerance consensus that can tolerate one-third faulty nodes, making the implemented MCS system robust and sturdy. In addition, an ensemble learning approach is deployed in the blockchain for eliminating fake tasks by malicious requesters. The evaluation test is conducted under two different datasets representing a big city and a small one to have an MCS campaign. Numerical results show that the ensemble approach eliminates most of the fake tasks with a detection accuracy of up to 0.99. Furthermore, the ensemble learning integrated system outperforms individual learner based centralized systems, and non-fault tolerant systems in terms of Ratio of Legitimate Tasks ( RoLT ) saved and Ratio of Fake Tasks ( RoFT ). RoFT is low to 0.01, and RoLT is high up to 0.913 via the proposed MCS blockchain-driven framework.

Antiquantum Privacy Protection Scheme in Advanced Metering Infrastructure of Smart Grid Based on Consortium Blockchain and RLWE

Smart meters in advanced metering infrastructure (AMI) collect fine-grained, real-time power consumption data may result in user privacy leakage. Aiming at the problem that existing privacy protection schemes cannot resist quantum attacks, an antiquantum privacy protection scheme in AMI of smart grid based on consortium blockchain and ring learning with errors (RLWE) is proposed. The scheme has the following contributions. First, the RLWE-based secure communication scheme is designed in this article, which ensures the resistance of the scheme to quantum attacks. Second, all smart meters in AMI use anonymous identity to communicate with the control center, and the fine-grained power consumption data is also encrypted and digitally signed to prevent user privacy leakage and tampering of power consumption data. And the scheme does not use bilinear pairings and exponential operations and has efficient computational performance. Third, a two-tier consortium blockchain storage system consisted of regional gateways and data aggregators are designed to eliminate the single point of failure problems of the data aggregators. The practical byzantine fault tolerance consensus mechanism is improved to reduce the communication cost in the consensus process. The scheme's security is proven theoretically, and the experimental results show that the scheme has a low computational cost.

R-PBFT: A secure and intelligent consensus algorithm for Internet of vehicles

Blockchain applications in the Internet of Vehicle (IoV) employ a secure and distributed consensus method, such as Practical Byzantine Fault Tolerance (PBFT) and Proof-of-Work (PoW), to reduce the load on the central server of IoV and the likelihood of single-node attacks. While traditional consensus algorithms based IoV improves consistency, efficiency, and eliminates single-node attacks, it has some limitations, including a delayed consensus process, restricted transaction frequency, and substantial block congestion. To address these limitations, we propose a fast and intelligent consensus mechanism called R-PBFT (Reputed PBFT). The R-PBFT improves the PBFT consensus process through the use of a reputation mechanism computed by logistic regression. We have built a testbed using the Raspberry Pi 4 and NVIDIA Jetson Nano platforms and experimented with the proposed scheme. The experiment result shows that the proposed R-PBFT outperforms state-of-the-art approaches by minimizing consensus delay by 50%, improving transaction frequency by 24% and throughput by 50%, reducing block congestion by 57%, and improves fairness by 15%. According to security analysis, R-PBFT is secure against Finney, Sybil, eclipse, routing, and impersonation attacks.

Reinforced practical Byzantine fault tolerance consensus protocol for cyber physical systems

Cyber–physical systems (CPS) are becoming an essential component of modern life. CPS services enable information to be exchanged between physical devices and virtual systems. The increasing malicious activities causing confidential data leakage and incorrect performance of devices are posing challenges for protection against cyber threats. Therefore, development of effective solutions that can protect both CPS data and data exchange networks are extremely urgent. This study makes some improvements on Practical Byzantine Fault Tolerance (PBFT), and provides a critical analysis of the feasibility of using blockchain technology to protect constrained CPS data. It also justifies the choice of the improved PBFT for implementation on such devices and simulates the main distributed ledger scenarios. The improved PBFT works as follows: first, the client broadcasts the signed transaction data to the entire network, rather than just the master node, followed by a hash value comparison verification process; second, select the master node based on the longest chain principle, and punish the malicious node via the “blacklist” mechanism; third, add the data synchronization and verification process, as well as dynamically entering and leaving nodes via state transitions. Ultimately, the PBFT commit phase is eliminated, resulting in a two-stage process. In comparison to the original PBFT protocol, the Reinforced Practical Byzantine Fault Tolerance (RPBFT) protocol may substantially enhance system throughput and minimize consensus communication time, thereby improving overall system efficiency while guaranteeing security.