Practical Byzantine Fault Tolerance Research Articles

Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment

In recent years, federated learning has been able to provide an effective solution for data privacy protection, so it has been widely used in financial, medical, and other fields. However, traditional federated learning still suffers from single-point server failure, which is a frequent issue from the centralized server for global model aggregation. Additionally, it also lacks an incentive mechanism, which leads to the insufficient contribution of local devices to global model training. In this paper, we propose a blockchain-based decentralized federated learning method, named BD-FL, to solve these problems. BD-FL combines blockchain and edge computing techniques to build a decentralized federated learning system. An incentive mechanism is introduced to motivate local devices to actively participate in federated learning model training. In order to minimize the cost of model training, BD-FL designs a preference-based stable matching algorithm to bind local devices with appropriate edge servers, which can reduce communication overhead. In addition, we propose a reputation-based practical Byzantine fault tolerance (R-PBFT) algorithm to optimize the consensus process of global model training in the blockchain. Experiment results show that BD-FL effectively reduces the model training time by up to 34.9% compared with several baseline federated learning methods. The R-PBFT algorithm can improve the training efficiency of BD-FL by 12.2%.

Improved PBFT Algorithm Based on Comprehensive Evaluation Model

Blockchain technology is well known due to the advent of Bitcoin. With the development of recent years, blockchain technology has been widely used in medicine, digital currency, energy, etc. The practical Byzantine fault-tolerant (PBFT) algorithm is a consensus algorithm widely used in consortium blockchains. Aiming to address the problems of the PBFT algorithm, low consensus efficiency due to high communication complexity, and malicious behavior of the primary node leading to consensus failure, an improved PBFT algorithm based on a comprehensive evaluation model (TB-PBFT) is proposed. First, nodes are divided into several groups based on the multi-formation control strategy of an unmanned aerial vehicle (UAV) cluster, which significantly reduces the communication complexity. Second, a comprehensive evaluation model combining the entropy method, TOPSIS method, and Borda count is proposed, which uses the behavior of nodes as an evaluation index, and the comprehensive score of nodes is obtained according to the preferences of other nodes. Finally, the highest ranking node is selected as the primary node through the comprehensive evaluation model to ensure the security and stability of the blockchain network. We analyze TB-PBFT algorithms and compare them with other Byzantine fault tolerance algorithms. Theoretical analysis and simulation results show that the TB-PBFT algorithm can improve node scalability and fault tolerance and reduce communication complexity and view switching probability. We also prove that the comprehensive evaluation model can improve the consensus success rate of the algorithm, and the feasibility and effectiveness of the improved consensus algorithm are verified. Hence, it can be applied to the consortium blockchain system effectively and efficiently.

ST-PBFT: An Optimized PBFT Consensus Algorithm for Intellectual Property Transaction Scenarios

For the current Intellectual Property (IP) transaction scenario, consensus nodes need to simultaneously consensus transactions of the same transaction type, resulting in low consensus efficiency, accuracy, and reliability, which seriously hinders the development of intellectual property. Based on the consortium chain, this paper proposes a secure and efficient blockchain-distributed consensus algorithm, ST-PBFT (Shard Transaction Practical Byzantine Fault Tolerance), applied to the IP transaction scenario. The main contributions of ST-PBFT include the following: first, a grouping method based on the principle of consistent hashing is proposed to group consensus nodes, and nodes group consensus, which reduces the complexity of communication. Second, the transaction consensus group can process IP transactions in parallel, which improves the throughput of the algorithm. Third, a node reputation evaluation model is proposed, which can prevent byzantine nodes from being repeatedly elected as primary nodes. The experimental results show that ST-PBFT can significantly improve the consensus efficiency and reliability and reduce consensus latency.

Emerging blockchain solutions in the mobility ecosystem: Associated risks and areas for applications

This study aims to identify the most influential blockchain types for potential implementation areas in the transforming mobility ecosystem, considering application area-specific needs such as transparency, transaction speed, scalability, energy usage, security, and operating cost. The study demonstrated the hybrid blockchain suitability for most of the 13 distinguished mobility applications, while private and consortium blockchains are found applicable based on the needs of specific use cases. A public blockchain is only found suitable for two of the use cases. Proof of Authority and Proof of Stake matches well with most use cases, while Practical Byzantine Fault Tolerance and Proof of Work could be suitable in particular.

LRBFT: Improvement of practical Byzantine fault tolerance consensus protocol for blockchains based on Lagrange interpolation

LRBFT: Improvement of practical Byzantine fault tolerance consensus protocol for blockchains based on Lagrange interpolation

An efficient sharding consensus algorithm for consortium chains

The consensus algorithm is very critical in any blockchain system, because it directly affects the performance and security of the blockchain system. At present, the classic Practical Byzantine Fault Tolerance Algorithm (PBFT), which is mainly used in the consortium chain, will lead to system communication congestion and reduced throughput when the number of nodes increases, so the PBFT algorithm is not suitable for large-scale consortium chains. In response to the above problems, this paper proposes a new clustering-based sharding consensus algorithm (KBFT), which aims to ensure that the consortium chain takes into account decentralization, security and scalability. The KBFT algorithm first uses the K-prototype clustering algorithm to shard the nodes in the network according to mixed attributes, and second, disjoint transactions are used to reach consensus in parallel in different shards. Concurrently, the KBFT algorithm introduces a supervision mechanism and a node credit mechanism, which is used to supervise and score the behavior of the nodes and select the proxy nodes, which improves security. We discuss the choice of shard size with the help of the binomial probability distribution and analyze the probability that the system can successfully form a global block under different node failure probabilities. Finally, the proposed algorithm is evaluated through theoretical analysis and simulation experiments. Results show that the proposed algorithm achieves a marked improvement in scalability and throughput along with a marked reduction in communication complexity compared with the classic baseline algorithm PBFT in this field of study, which improves the operating efficiency of the system and simultaneously guarantees the security and robustness of the system.

Improved PBFT Consensus Algorithm Based on Node Role Division

The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.

PBFT optimization algorithm based on community contributions.

Community governance is the basic unit of social governance, and it is also an important direction for building a social governance pattern of co-construction, co-governance and sharing. Previous studies have solved the problems of data security, information traceability and participant enthusiasm in the process of community digital governance by building a community governance system based on blockchain technology and incentive mechanisms. The application of blockchain technology can solve the problems of low data security, difficulty in sharing and tracing and low enthusiasm on the part of multiple subjects regarding participation in community governance. The process of community governance involves the cooperation of multiple government departments and multiple social subjects. Under the blockchain architecture, the number of alliance chain nodes will reach 1000 with the expansion of community governance. The existing consensus algorithms for coalition chains are difficult to meet the high concurrent processing requirements under such large-scale nodes. An optimization algorithm has improved the consensus performance to a certain extent, but the existing systems still cannot meet the data needs of the community and are not suitable for community governance scenarios. Since the community governance process only involves the participation of relevant departments in users, all nodes in the network are not required to participate in the consensus under the blockchain architecture. Therefore, a practical Byzantine fault tolerance (PBFT) optimization algorithm based on community contribution (CSPBFT) is proposed here. First, consensus nodes are set according to different roles of participants in community activities, and participants are given different consensus permissions. Second, the consensus process is divided into different stages, and the amount of data processed by each consensus step is reduced. Finally, a two-level consensus network is designed to perform different consensus tasks, and reduce unnecessary communication between nodes to reduce the communication complexity of consensus among nodes. Compared with the PBFT algorithm, CSPBFT reduces the communication complexity from O(N2) to O(N2/C3). Finally, the simulation results show that, through rights management, network level setting and consensus phase division, when the number of nodes in the CSPBFT network is 100-400, the consensus throughput can reach 2000 TPS. When the node in the network is 1000, the instantaneous concurrency is guaranteed to be above 1000 TPS, which can meet the concurrent needs of the community governance scenario.

Hybrid Smart Contracts for Securing IoMT Data

Data management becomes essential component of patient healthcare. Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being. Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician. The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient’s health care data. To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage. Patients health data is collected from physician, smart devices etc. The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner. In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW). The implementation is done using cloud technology setup with smart contracts (PBFT-PoW). The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%.

An Intelligent Optimization Control Method for Enterprise Cost Under Blockchain Environment

This paper uses blockchain technology to conduct in-depth research and analysis on enterprise cost optimization control. Based on the analysis of the cost control status in enterprises, the concept of target cost optimization control and specific control ideas are proposed. And the study of optimization control under target cost based on genetic algorithm is carried out in combination with the current three major controls of quality, schedule, and cost. It provides the technical basis for the realization of the target profit of the enterprise. The Optimized Scalable Byzantine Fault Tolerance (OSBFT) algorithm, which is suitable for spectrum sharing, is proposed based on PBFT (Practical Byzantine Fault Tolerance) algorithm. So, in this paper, an improved consensus algorithm OSBFT (Optimized Scalable Byzantine Fault Tolerance) is proposed based on it. The improved genetic algorithm is used to solve the objective function and verify the validity, reasonableness and applicability of the model and algorithm. It is shown that the introduction of delay cost in the multilevel inventory model reduces the total cost of the optimized model by 16.87% compared to previous studies. The algorithm reduces the consensus steps, incorporates a data synchronization mechanism, and enables nodes to join and exit consensus.

A trusted peer-to-peer market of joint energy and reserve based on blockchain

• A joint energy-reserve prosumer-centric market is proposed. • The trading mechanism not only enables peer-to-peer (P2P) energy trading but also quantifies the reserve cost and the value of flexibility. • A blockchain-based trading algorithm is proposed to implement an autonomous and trustworthy prosumer-centric market. • A pipelined delegated Byzantine fault tolerance (PDBFT) consensus algorithm is proposed to improve the efficiency of blockchain-based energy trading. With the increasing penetration of distributed energy resources, the traditional producer-centric electricity market is moving to a prosumer-centric market, where prosumers can trade with each other in an autonomous pattern. However, there remain research gaps on the pricing and allocation of joint energy and reserves in an autonomous prosumer-centric market. This paper firstly designs a joint energy-reserve peer-to-peer (P2P) trading mechanism. The mechanism not only enables P2P energy trading but also quantifies the reserve cost and the value of flexibility. Then, a blockchain-based trading algorithm is proposed to implement a trustworthy prosumer-centric market. A pipelined delegated Byzantine fault tolerance (PDBFT) consensus algorithm is proposed to ensure robustness and improve the efficiency of the autonomous trading process. Numerical results show the effectiveness of the trading mechanism and the performance of the blockchain-based trading algorithm. Compared with only considering energy trading, the proposed mechanism reduces the total cost of the market by 16.03%. Compared with using the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, the computational time of market clearing on blockchain is decreased by 45.90%.

A Scalable Byzantine Fault Tolerance Algorithm Based on a Tree Topology Network

The consortium chain is the main form of application of blockchain technology in the actual industry, and its consensus mechanism mostly adopts the practical Byzantine fault tolerance (PBFT) algorithm. The traditional PBFT algorithm is only suitable for small-scale local area networks, but in large-scale wide-area network environments, its scalability bottleneck has a serious impact on the performance of the system. Therefore, in this paper, a scalable Byzantine fault tolerance algorithm based on a tree topology network is proposed (STBFT), which can take different steps to reach consensus according to the abnormal situation of the system. First, the STBFT algorithm divides the consensus nodes into different layers and groups based on the tree topology network structure, which transforms from global consensus to local consensus and drastically reduces communication consumption. Then, the division method of the group is based on a verifiable random function (VRF), with the purpose of preventing targeted attacks and colluding Byzantine nodes from affecting the normal consensus of the system. Finally, a feedback mechanism is proposed for the first time to reduce the influence of Byzantine failure on hierarchical network systems. The simulation results show that the proposed algorithm reduces the communication complexity and improves the fault tolerance of the system, and the scalability of the tree topology network structure can be better applied in large-scale scenarios such as IoT and health care.

Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.

Trustworthy Federated Learning via Blockchain

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this article, we shall propose a decentralized blockchain-based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying a practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning (DRL)-based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by the DRL-based algorithm compared with the baseline algorithms.

ABC-GSPBFT: PBFT with grouping score mechanism and optimized consensus process for flight operation data-sharing

The flight operation data sharing not only brings huge benefits to all participants, but also puts forward higher requirements for data security. Consortium blockchain provides a new approach to safely sharing flight operation data in an untrusted environment. Practical Byzantine Fault-Tolerant (PBFT) algorithm is one of the most widely used consensus algorithms in consortium blockchain, but it exists some problems, such as static structure, low consensus efficiency and large communication overhead. To solve these problems, a PBFT with grouping score mechanism and artificial bee colony-optimized consensus process, namely ABC-GSPBFT, is designed to construct the consortium blockchain for sharing flight operation data. Firstly, the artificial bee colony algorithm is used to pre-select a collection of reliable nodes to narrow the scope of consensus. Then, the grouping score mechanism is employed to dynamically update the consensus nodes to further narrow the scope of consensus and enhance the reliability of consensus nodes. Finally, the commit stage of the PBFT algorithm is reasonably simplified to improve consensus efficiency. The actual flight operation data is selected to test and verify the effectiveness of the ABC-GSPBFT algorithm. The analysis and experimental results show that the ABC-GSPBFT algorithm can greatly reduce consensus delay and communication overhead, and improve dynamic performance and consensus efficiency of the flight data sharing. It is an effective method for safely, reliably and efficiently sharing flight data.

Enhanced PBFT Blockchain based on a Combination of Ripple and PBFT (R-PBFT) to Cryptospatial Coordinate

In this research, we introduce the combination of two Blockchain methods. Ripple Protocol Consensus Algorithm (RPCA) and Practical Byzantine Fault Tolerance (PBFT) are applied to cryptospatial coordinates to support cultural heritage tourism. The PBFT process is still used until the preparation process to ensure a maximum error of 33%, and every node would add a new chain in all nodes, so PBFT has a slower processing speed than other methods. This research cuts the PBFT process. After the preparation process in PBFT, the data was entered into the RPCA node and was calculated using an equation to minimize errors with a maximum limit of 20%. After this process, the was were sent to the commit process to store the data in all connected nodes in the Blockchain network; we call this combination of two methods R-PBFT. Combining the two methods can enhance data processing security and speed because it still uses the PBFT work combined with the speed of RPCA. Furthermore, this method uses a fault tolerance value from the RPCA of 20% to enhance data processing security and speed.

Improvement of the PBFT Algorithm Based on Grouping and Reputation Value Voting

An improved practical Byzantine fault tolerance (Practical Byzantine Fault Tolerant consensus algorithm based on reputation, RPBFT) algorithm based on grouping and reputation value voting is proposed for the problems of high communication complexity, poor scalability, and random selection of master nodes of the practical Byzantine fault tolerance (PBFT) consensus algorithm of the consortium chain. First, the consistency process is optimized to take the response speed of nodes to each group leader as the basis of grouping, and the intragroup consensus is performed. The group leader then takes the result of intragroup consensus and participates in extra-group consensus to reduce the frequency and time of internode communication. Second, the reputation model and voting mechanism are proposed, and the group leader is generated by node reputation value voting, which enhances the initiative and reliability of trusted nodes and reduces the abnormal nodes as group leader. Finally, a simulation and performance testing system based on this improved scheme is built to prove the effectiveness as well as the usability of the scheme through simulation experiments. The experimental results show that when the number of network nodes is 36, the throughput of the RPBFT algorithm is six times that of PBFT. Therefore, the consensus delay is reduced by 91.7%, and the communication overhead is reduced by 37.8%.

Efficient practical Byzantine consensus using random linear network coding

Using random linear network coding (RLNC) in asynchronous networks with one-to-many information flow has already been proven to be a valid approach to maximize the channel capacities. Message-based consensus protocols such as practical Byzantine fault tolerance (pBFT) adhere partially to said scenario. Protocol phases with many-to-many communication, however, still suffer from quadratic growth in the number of required transmissions to reach consensus. We show that an enhancement in the data transmission behavior in the quadratic phases is possible through combining RLNC with pBFT as one hybrid protocol. We present several experiments conducted on random network topologies. We conclude that using RLNC-based data transmission offers a significantly better performance under specific circumstances, which depend on the number of participating network nodes and the chosen coding parameters. Applying the same approach to other combinations of message-based consensus and network coding protocols promises not only a gain in performance, but may also improve robustness and security and open up new application scenarios for RLNC, e.g., running it on the application layer.

A secure and efficient multi-domain data sharing model on consortium chain

In a large-scale networking scenario with massive distribution of devices, data are independently generated and maintained by multiple domains. To solve the problem of isolated data islands in multi-domain, this paper designs a multi-domain data sharing model based on consortium chain. Aiming at the problems of low efficiency and lack of consideration of intra-domain consensus processes in traditional data sharing schemes, this paper optimizes the security and efficiency of data sharing from two parts: inter-domain chain and intra-domain chain. To ensure fine granularity, flexibility and security of the access control process in inter-domain chain, this model combines the Attribute-Based Access Control (ABAC) with smart contract, and a permission grading mechanism is designed to solve the problem of low retrieval efficiency in this access control model through reducing the retrieval range by grading and matching Policy set. Considering the problem of high delay of node consensus in the large-scale networking environment of intra-domain chain, a layered practical Byzantine fault tolerance optimization (LPBFT) algorithm with introducing a reputation model is designed, which greatly improves the efficiency and security of the intra-domain consensus. The consortium chain model is designed and implemented from the perspectives of inter-domain and intra-domain, which optimized the security and efficiency of the multi-domain data sharing model. Finally, a prototype system is implemented based on Hyperledger Fabric, and the validity of the model is verified from the aspects of inter-domain access control and intra-domain consensus algorithm.

Online Music Teaching Resource Sharing Algorithm Based on Blockchain Technology

Blockchain technology provides technical support for cloud computing data sharing in data storage and data access, and ensures the safe and effective transmission of data sharing. The online music teaching resource platform based on blockchain technology faces the problems of low data throughput, large delay and unstable performance. Therefore, a blockchain-based sharing algorithm (Practical Byzantine Fault Tolerance, PBFT), PBFT algorithm is proposed. Consensus allows 1/3 of cheating points in the blockchain, and authenticated transactions are confirmed through a confirmation mechanism to ensure data security. However, the traditional PBFT algorithm lacks an efficient node joining and exiting mechanism, and has problems such as low data throughput. Therefore, on the basis of the traditional PBFT algorithm, a blockchain sharing algorithm (WBFT) that improves the weighted Byzantine fault tolerance mechanism is proposed. A weighting mechanism is added in the consensus process. After the consensus, the node behavior will dynamically adjust the weights to reduce the adverse impact of malicious nodes on the data and improve the overall data sharing effect. Experimental performance tests show that the improved WBFT algorithm performs best in multi-algorithm delay performance tests. In the data average throughput test, the performance of the improved WBFT algorithm is 20.2% higher than that of the traditional PBFT algorithm. In the security test, the improved WBFT algorithm has the best performance. The proposed improved WBFT algorithm can well meet the needs of building an online music teaching resource sharing platform. The research content has important reference value for the application of blockchain technology in the education industry, and is conducive to promoting the development of modern education.