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Blockchain-Based Cross-Domain Authentication With Dynamic Domain Participation in IoT

IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5385-5395页

作者： Luo, Deyu Sun, Gang Yu, Hongfang Guizani, Mohsen Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Dept Machine Learning Abu Dhabi U Arab Emirates

In the Internet of Things (IoT), blockchain-based cross-domain authentication schemes can effectively establish trust and share data across different administrative domains. However, current blockchain-based cross-domain authentication solutions often overlook dynamic node participation challenges, which is crucial for the flexible IoT environment. In this study, we propose a blockchain-based cross-domain authentication scheme that supports dynamic node participation, allowing administrative domainss to freely join or leave under legal conditions. First, we introduce an efficient blockchain-based cross-domain authentication framework. Second, we propose a blockchain consensus algorithm that supports dynamic node participation to serve the aforementioned framework. Specifically, inspired by the transmission control protocol (TCP) protocol's piggybacking strategy, this algorithm integrates the process of node joining and leaving into the regular consensus flow to enhance efficiency. To further improve the algorithm's efficiency, we designed compressed block and parallel chain structures to increase bandwidth utilization and throughput. Detailed correctness proofs demonstrate the algorithm's security. Extensive experiments have been conducted to show that our scheme increases throughput by approximately 8x compared to existing approaches.

关键词： Authentication Internet of Things Heuristic algorithms Security Servers Public key consensus algorithm Information sharing Protocols Blockchain cross-domain authentication dynamic efficiency improvement Internet of Things (IoT) parallelization parallelization

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Improved algorithms for optimal k sink location on path networks

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THEORETICAL COMPUTER SCIENCE 2025年 1039卷

作者： Bhattacharya, Binay Golin, Mordecai J. Higashikawa, Yuya Kameda, Tsunehiko Katoh, Naoki Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Univ Massachusetts Manning Coll Informat & Comp Sci Amherst MA 01002 USA Univ Hyogo Grad Sch Informat Sci Kobe Japan

We address the problem of placing k sinks on dynamic-flow path networks with n vertices so as to minimize their maximum evacuation completion time. We develop two different algorithms that, when all edges have the same capacity, run respectively in O(n+k2log2 n) and O(n log n) time. When the edge capacities can be different, i.e., are general, they run respectively in O(n log n+k2log4 n) and O(n log3 n) time. These algorithms improve upon the previously most efficient algorithms, which had time complexities O(kn) and O(knlog2n), respectively, for the uniform and general edge capacity models. The improvements are achieved by moving from a dynamic programming based approach to a parametric-search based one.

关键词： consensus algorithm

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Balanced scheduling method of ideological and political teaching resources based on cluster analysis algorithm

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 1-8页

作者： Liu, Wenbin Department of Management Hunan City University Yiyang Hunan413000 China

In order to overcome the problems of poor scheduling accuracy, low recall rate and long scheduling time in the traditional scheduling method of ideological and political resources, the paper proposes a balanced scheduling method of resources based on clustering analysis algorithm. First, the load time series of the teaching resource server is determined and pre-processed. Secondly, cluster analysis is used to classify the data. Finally, according to the classification results, the balanced scheduling function of resources is constructed, and the particle swarm optimisation algorithm is used to solve the scheduling function to obtain the final scheduling strategy. The results show that the scheduling accuracy of the proposed method is 99.12%, the recall rate is up to 95%, the scheduling time is controlled within 7 s, and the resource balance scheduling effect is good. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： consensus algorithm

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Tree Proof-of-Position algorithms

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IEEE Internet of Things Journal 2025年第11期12卷 16393-16409页

作者： Manzano Kharman, Aida Ferraro, Pietro Hamedmoghadam, Homayoun Shorten, Robert Imperial College London Dyson School of Design Engineering LondonSW7 2DB United Kingdom

A growing issue across multiple fields involves verifying that an individual or object is truly in the location it claims to be and, despite the significance of this problem, the scientific community has not extensively explored how to provide proof for such claims. Accordingly, this article presents a novel class of proof-of-position algorithms: tree-proof-of-position (T-PoP). These algorithms are decentralized, collaborative and can be computed in a privacy preserving manner, such that agents do not need to reveal their position publicly. We make no assumptions of honest behavior in the system, and consider varying ways in which agents may misbehave. T-PoP is therefore resilient to adversarial scenarios, which makes it suitable for a wide class of applications, namely those where trust in a centralized infrastructure may not be assumed, or high security risk scenarios. Our algorithm has a worst case quadratic runtime, making it suitable for hardware constrained IoT applications. We also provide a mathematical model that summarizes T-PoP’s performance for varying operating conditions. Using a large number of agent-based simulations, we verify the agreement between TPoP’s performance and our mathematical predictions. T-PoP can achieve high levels of reliability and security by tuning its operating conditions, both in high and low density environments. Finally, we also present a mathematical model to probabilistically detect platooning attacks. © 2014 IEEE.

关键词： consensus algorithm

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A two-population artificial tree algorithm based on adaptive updating strategy for dominant populations

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Soft Computing 2025年第4期29卷 2075-2106页

作者： Xiao, Yaping Niu, Linfeng Li, Qiqi School of Economics Central South University of Forestry and Technology Hunan Province Changsha City410004 China College of Mechanical and Vehicle Engineering Changsha University of Science & Technology Hunan Province Changsha City410114 China

An artificial tree (AT) algorithm has been proposed recently, and the performance of AT has been enhanced because of the introduction of improved AT algorithm with two-population (IATTP). However, the branch update operators of IATTP cannot effectively balance exploration and exploitation, which limits the optimization accuracy and efficiency of IATTP. To further improve the performance of IATTP, this work proposes a two-population artificial tree algorithm based on adaptive updating strategy for dominant populations (TATAD). In TATAD, six operators named self-evolution operator 2, crossover operator 2, improved self-evolution operator, gradient descent update operator, Gauss and Cauchy variational operator, and random traceless Sigma variational operator are applied to form an operator library. A dominant operator dynamic following mechanism is proposed to assign these six operators to the two populations in an optimal pairing scheme. Both populations and operators compete with each other, and the advantages of all operators are fully utilized. Moreover, the combination of diverse operators and dominant operator dynamic following mechanism can effectively balance the exploration and exploitation of TATAD. The performance of TATAD is compared with four AT algorithms and six efficient algorithms through typical test functions, and their results are bested by Wilcoxon rank sum test (WRST) and Friedman ranking test. It is found that TATAD is the most competitive algorithm among these algorithms for solving these optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： consensus algorithm

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A multi-objective social-based algorithm for community detection

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Multimedia Tools and Applications 2025年 1-30页

作者： Farrokhpour Dizaj, Yousef Lotfi, Shahriar Hajipour, Javad Department of Computer Science Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

The detection of community structures in complex networks has garnered significant attention in recent years. Given its NP-hardness, numerous evolutionary optimization-based approaches have been proposed. However, there is still a need for improvement, since no previous method has been able to find the true community structure on all benchmark networks. In this paper, we propose a multi-objective approach based on the social-based algorithm (SBA). SBA is a hybrid of the imperialist competitive algorithm and the standard evolutionary algorithm. SBA has demonstrated remarkable performance in finding optimal solutions and can effectively identify correct community structures. In our method, instead of a single solution, a set of non-dominated solutions is produced, each representing a different compromise between the objective functions. To evaluate the performance of the proposed algorithm, various tests were conducted on synthetic and real-world datasets. The results of experiments on several social network benchmarks show up to a 57% improvement on benchmark networks compared to previous works. This indicates that the proposed algorithm performs well in community detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： consensus algorithm

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Fuzzy vision image denoising algorithm based on Bayesian estimation

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International Journal of Reasoning-based Intelligent Systems 2025年第1期17卷 24-31页

作者： Ye, Cong Department of Computer Science and Technology Suzhou College of Information Technology Jiangsu215200 China

A fuzzy visual image denoising algorithm based on Bayesian estimation is proposed to address the problems of poor denoising performance and long denoising time in traditional image denoising algorithms. First, analyse the noise reduction requirements of fuzzy visual images, construct a visual image degradation model, and obtain fuzzy visual images. Then, analyse the spectrum of the acquired fuzzy visual image, preprocess the spectrum signal by adding a window function, and detect the edge of the fuzzy visual image according to the preprocess result to supplement the fuzzy edge. Based on the supplemented blurred visual image edges, Bayesian estimation is used for image denoising to improve the image denoising effect. The experimental results show that the visual image obtained by the proposed method is relatively clear and has high image quality, indicating that the denoising effect of the algorithm is good and the denoising time is short. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： consensus algorithm

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Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks

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ICT EXPRESS 2025年第2期11卷 258-263页

作者： Kim, Dong-Seong Syamsul, Rizal Kumoh Natl Inst Technol ICT CRC Gumi South Korea NSLab Co Ltd Gumi South Korea Telkom Univ Sch Elect Engn Bandung Indonesia

Integrating machine learning (ML) into blockchain consensus mechanisms enhances efficiency, scalability, and resilience. This study introduces the PoA2 algorithm, an ML-enhanced Proof of Authority mechanism that optimizes signer selection for improved transaction processing. Simulations with models including Random Forest, Logistic Regression, SVM, K-Nearest Neighbors, Decision Tree, and Gradient Boosting showed significant gains. Random Forest reduced latency tenfold, achieving nearly 1000 transactions per second, with 93.33% accuracy, 100% precision, 86.67% recall, and a 92.86% F1-score. These results demonstrate ML's potential to enhance blockchain performance, making hybrid blockchain-ML solutions a promising research direction.

关键词： Blockchain consensus algorithm Machine learning

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Cabinet: Dynamically Weighted consensus Made Fast 51st

Cabinet: Dynamically Weighted Consensus Made Fast

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Gengrui Zhang, Shiquan Bachras, Michail Jacobsen, Hans-Arno Zhang, Yuqiu Concordia University Canada University of Toronto Canada

Conventional consensus algorithms, such as Paxos and Raft, en counter in efficiencies when applied to large-scale distributed systems due to the requirement of waiting for replies from a majority of nodes. To address these challenges, we propose Cabinet, a novel consensus algorithm that introduces dynamically weighted consensus, allocating distinct weights to nodes based on any given failure thresholds. Cabinet dynamically adjusts nodes’ weights according to their responsiveness, assigning higher weights to faster nodes. The dynamic weight assignment maintains an optimal system performance, especially in large-scale and heterogeneous systems where node responsiveness varies. We evaluate Cabinet against Raft with distributed MongoDB and PostgreSQL databases using YCSB and TPC-C workloads. The evaluation results show that Cabinet outperforms Raft in throughput and latency under increasing system scales, complex networks, and failures in both homogeneous and heterogeneous clusters, offering a promising high-performance consensus solution. © 2025, VLDB Endowment. All rights reserved.

关键词： consensus algorithm

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Speeding up Grover's algorithm

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European Journal of Operational Research 2025年第1期326卷 13-27页

作者： Creemers, Stefan Voie du Roman Pays 34 Louvain-la-NeuveB-1348 Belgium

To find one of the M target entries in an unstructured database that has N entries, Grover's algorithm performs [Formula presented][Formula presented] iterations. In each iteration, a function is called that evaluates whether an entry is a target entry. Compared to a classical procedure, that requires [Formula presented] evaluations, Grover's algorithm achieves a quadratic speedup. This quadratic speedup, combined with its general applicability as a search algorithm, have made Grover's algorithms one of the most important (quantum) algorithms available today. A study to improve its runtime is more than justified. For this purpose, we investigate two speedup strategies: (1) reducing the number of iterations that are performed in each run of Grover's algorithm and (2) partitioning the database. For each of these strategies, we not only execute Grover's algorithm in parallel, but also explore the possibility to execute Grover's algorithm in series on a single Quantum Processing Unit (QPU) as well as on multiple QPUs. For each combination of execution mode (serial, parallel, and serial/parallel) and speedup strategy, we show how to obtain optimal policies using closed-form expressions and a gradient-descent procedure. For a single QPU we obtain a speedup factor of 1.1382 when compared to a textbook implementation of Grover's algorithm. If multiple QPUs are at our disposal, we obtain a speedup factor of at most 1.1382Q, where Q denotes the number of QPUs. In addition, we show that the dominant policies that minimize the expected number of Grover iterations also minimize the expected number of iterations that are performed per qubit. © 2025 Elsevier B.V.

关键词： consensus algorithm

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