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.
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.
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 largescale 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.
Night is an inevitable scene for surveillance video. Due to the high image resolution, complex background, uneven illumination, and similarity between the target and the background of hawk-eye surveillance video, it is difficult for previous trackers to apply the tracking of a tiny object in such scenes. In this regard, this paper proposes to combine an online automatically and adaptively learning spatio-temporal regularized tracking algorithm with an efficient and effective low-light image enhancement algorithm to improve tracker performance. We constructed a new benchmark that includes 41 night surveillance sequences captured by Hawk-Eye cameras at night. Exhausted experiments have been conducted on this dataset, and the results show that by combining the two methods, the original algorithm can obtain better results in this dataset, and can meet the real-time object tracking, which contributes to the application of tiny object tracking in eagle-eye surveillance video at night.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.