Consensus In Networks Research Articles

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.

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Resonant Frequency Modelling of Microstrip Antennas by Consensus Network and Student’s-T Process

Distributed event-triggered estimation for dynamic average consensus: A perturbation-injected privacy-preservation scheme

Previous studies on the distributed estimation problem for dynamic average consensus of multi-agent networks are usually based on the assumption that each agent continuously and honestly shares information with its neighbors. To relax this assumption, this paper focuses on the distributed event-triggered private estimation problem. By injecting random perturbations into the original reference signal, an adaptive robust distributed privacy-preserving event-triggered estimation algorithm is proposed. With the proposed algorithm, the convergence of the estimation error is guaranteed under intermittent communication, while protecting the private reference signal information from disclosure. To determine the timing for communication, an adaptive distributed dynamic triggering mechanism with a dynamically updated internal triggering variable is designed. In addition, a dynamically updated adaptive gain instead of a static gain is employed in the estimation algorithm and triggering mechanism to eliminate the dependence on some global information. Finally, numerical simulation results are presented to illustrate the validity of the theoretical results.

Cross-Chain Digital Asset System for Secure Trading and Payment

Blockchain as a ledger technology is attractive without the need for central servers. There are many types of blockchains in different fields, such as digital asset trading and payment, which have business interactions. In the fields, the digital asset and payment information on their blockchains need to be securely cooperative with each other. However, some business activities are on different blockchains, which have different consensus algorithms and network architectures, thus limiting the interoperability among these activities and making each blockchain an island. Cross-chain technology can connect different blockchains and realize the interoperability and sharing of information among them. This work designs a cross-chain digital asset system for secure trading and payment. It builds two parallel chains, i.e., digital asset chain (DAC) and payment chain (PC), and their functions are analyzed and designed. The cross-chain, i.e., relay chain (RC), is used to realize cross-chain interoperability, where the cross-chain message format and authority setting are designed to endow parallel chains with the ability to recognize. The decentralized characteristic of the RC allows cross-chain messages to be safely transmitted to ensure secure trading and payment. Through testing and analysis, the proposed system can provide more secure trading and payment than its peers.

Spotting the Unseen: Reciprocal Consensus Network Guided by Visual Archetypes

Humans often require only a few visual archetypes to spot novel objects. Based on this observation, we present a strategy rooted in ``spotting the unseen" by establishing dense correspondences between potential query image regions and a visual archetype, and we propose the Consensus Network (CoNet). Our method leverages relational patterns intra and inter images via Auto-Correlation Representation (ACR) and Mutual-Correlation Representation (MCR). Within each image, the ACR module is capable of encoding both local self-similarity and global context simultaneously. Between the query and support images, the MCR module computes the cross-correlation across two image representations and introduces a reciprocal consistency constraint, which can incorporate to exclude outliers and enhance model robustness. To overcome the challenges of low-resource training data, particularly in one-shot learning scenarios, we incorporate an adaptive margin strategy to better handle diverse instances. The experimental results indicate the effectiveness of the proposed method across diverse domains such as object detection in natural scenes, and text spotting in both historical manuscripts and natural scenes, which demonstrates its sparkling generalization ability. Our code is available at: https://github.com/infinite-hwb/conet.

Performance Analysis and Comparison of Nonideal Wireless PBFT and RAFT Consensus Networks in 6G Communications

Performance Analysis and Comparison of Nonideal Wireless PBFT and RAFT Consensus Networks in 6G Communications

New consensus reaching process with minimum adjustment and feedback mechanism for large-scale group decision making problems under social trust networks

Large-scale group decision making problems in social networks environment have become an important research topic in decision making science. In this paper, we propose a new consensus reaching process with minimum adjustment and feedback mechanism considering public opinions for large-scale group decision making problems under social trust networks. The trust propagation operator based on Archimedean t-norm is used to calculate incomplete trust values first. The fast unfolding algorithm is used to divide experts into subgroups. The opinion evolution model is presented to calculate experts’ weights based on dynamic trust values and expert influence. Public opinions are used to determine consensus thresholds of attributes. A dual-path adjustment model is developed for group consensus in social network. Divide experts’ evaluation values into three different types: ‘conflict behavior’, ‘tolerant behavior’, ‘trust behavior’. Minimum cost adjustment models have been developed considering adjustment directions for the ‘conflict behavior’ and interactive path is activated for the ‘tolerant behavior’. The drugs selection from E-commerce platform for therapy of influenza at home is used to illustrate the new method and comparisons have also been conducted.

SHA 512 Enhanced Secure Hash Decentralized Response System

Today a very huge lack of confidence about the current EVM/ballot response system like voting has made voting response collection in any democratic country very condemnatory. People have watched their fundamental rights which are being violated. On the other hand the other digital voting systems are also in danger due to a minimal of transparency. Most of the traditional response systems are not clear enough so as to meet today's requirements; this crucial reason makes it very sweaty for the government to come up with voters' trust. The motive behind the downfall of the traditional and current cyber voting system is that it can be easily compromised due to centralized network. The primary objective here is to counter the issues of the traditional digital voting and such response systems, which has any type of mistrust or inequality during the process of accepting responses through response systems like voting. Blockchain technology can be used in such response systems like voting to have a fair response system and reduce wrongdoing. The current physical response systems like voting have many downs in it as well as the digital voting systems are not up to the mark to be implemented on a huge scale. Such all problems raises the need for a solution to secure the democratic rights of the people. This article put-ups a framework based on up-to-date blockchain algorithm technology that provides high clarity and reliability of the system to construct a healthy relationship between response systems and election authorities. The insisted software provides a framework that can be implemented in real world to conduct voting like procedures digitally through blockchain rather than involving any physical polling stations and centralized ledger. Our proposed framework provides an extendible hashing, by using the latest consensus algorithms. The Secure Hash Algorithms applied in the voting system along with Chain Security makes the response systems more secure. Distributed ledger contracts also provide a protected connection between the user and the consensus network while running a transaction in the chain. The security of the blockchain based response system has also been discussed. In Addition to this, encryption of responses using cryptographic hash functions and prevention of exploitation attacks about 51% on the blockchain has also been elaborated. Also, the procedure for carrying out blockchain transactions during the process of response collection has been elaborated using Blockchain. Finally, the throughput performance evaluation of the proposed system shows that such a system can be deployed in a large-scale real time population.

Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks.

One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices.

The Time Complexity of Consensus Under Oblivious Message Adversaries

We study the problem of solving consensus in synchronous directed dynamic networks, in which communication is controlled by an oblivious message adversary that picks the communication graph to be used in a round from a fixed set of graphs D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{D}$$\\end{document} arbitrarily. In this fundamental model, determining consensus solvability and designing efficient consensus algorithms is surprisingly difficult. Enabled by a decision procedure that is derived from a well-established previous consensus solvability characterization for a given set D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{D}$$\\end{document}, we study, for the first time, the time complexity of solving consensus in this model: We provide both upper and lower bounds for this time complexity, and also relate it to the number of iterations required by the decision procedure. Among other results, we find that reaching consensus under an oblivious message adversary can take exponentially longer than both deciding consensus solvability and broadcasting the input value of some unknown process to all other processes.

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Impulsive bounded consensus of two-layer multi-agent networks under deception attacks

This paper investigates the mean-square bounded consensus issue in a two-layer multi-agent network under deception attacks. The two-layer network is composed of the leader and follower layers with a switching topology. Employing an impulsive control method, the mean-square bounded consensus for the leader layer and the node-to-node mean-square bounded consensus of the two-layer network are analyzed. Based on the knowledge of graph theory, Lyapunov stability theory, and linear matrix inequalities, sufficient conditions for the mean-square bounded consensus of multi-agent systems in the two-layer network are derived. Finally, the practicability and efficacy of the theoretical outcomes are corroborated via the provided numerical simulations.

Robust Finite-Time Consensus of Coupled Markovian Logical Dynamic Networks

Robust Finite-Time Consensus of Coupled Markovian Logical Dynamic Networks

Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality

Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and Chain Quality

Causal inference of diachronic semantic maps from cross-linguistic synchronic polysemy data

Semantic maps are used in lexical typology to summarize cross-linguistic implicational universals of co-expression between meanings in a domain. They are defined as networks which, using as few links as possible, connect the meanings so that every isolectic set (i.e., set of meanings that can be expressed by the same word in some language) forms a connected component. Due to the close connection between synchronic polysemies and semantic change, semantic maps are often interpreted diachronically as encoding potential pathways of semantic extension. While semantic maps are traditionally generated by hand, there have been attempts to automate this complex and non-deterministic process. I explore the problem from a new algorithmic angle by casting it in the framework of causal discovery, a field which explores the possibility of automatically inferring causal structures from observational data. I show that a standard causal inference algorithm can be used to reduce cross-linguistic polysemy data into minimal network structures which explain the observed polysemies. If the algorithm makes its link deletion decisions on the basis of the connected component criterion, the skeleton of the resulting causal structure is a synchronic semantic map. The arrows which are added to some links in the second stage can be interpreted as expressing the main tendencies of semantic extension. Much of the existing literature on semantic maps implicitly assumes that the data from the languages under analysis is correct and complete, whereas in reality, semantic map research is riddled by data quality and sparseness problems. To quantify the uncertainty inherent in the inferred diachronic semantic maps, I rely on bootstrapping on the language level to model the uncertainty caused by the given language sample, as well as on random link processing orders to explore the space of possible semantic maps for a given input. The maps inferred from the samples are then summarized into a consensus network where every link and arrow receives a confidence value. In experiments on cross-linguistic polysemy data of varying shapes, the resulting confidence values are found to mostly agree with previously published results, though challenges in directionality inference remain.

A personalized bidirectional feedback mechanism by combining cooperation and trust to improve group consensus in social network

In the context of social network group decision making, it is an essential step to exploit the feedback mechanism for consensus reaching process (CRP) to obtain group-supported decision results. The CRP typically involves both concordant and discordant decision-makers (DMs), and the bidirectional interaction between them is regarded as a preferable approach to promote consensus. However, the existing bidirectional feedback models cannot provide personalized recommendation advice for DMs without objectively considering the DMs’ trust relationship during the interaction process. To address this issue, this paper utilizes the Uninorm propagation operator to establish the trust relationship among group DMs, which is used to generate personalized recommendation advices to modify their preference. In addition, it defines the concept of the cooperation degree index (CDI) to explore the ‘cooperative behavior’ of DMs during interaction, and then it is able to prevent over adjusting behavior. Therefore, this article establishes a novel personalized bidirectional feedback mechanism, which can reduce the opinion loss of individuals while enhancing fairness in the CRP. Finally, through case studies and discussions, the effectiveness and feasibility of the proposed approach is validated.

MiR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer’s disease

BackgroundAlzheimer’s dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.MethodsWe performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.ResultsNetwork analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.ConclusionsIntegrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion,LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

A Survey of Multi-Vehicle Consensus in Uncertain Networks for Autonomous Driving

A Survey of Multi-Vehicle Consensus in Uncertain Networks for Autonomous Driving

Consensus of Heterogeneous Multi-agent Networks Under Sparse Attacks by Integral Type Observer

Consensus of Heterogeneous Multi-agent Networks Under Sparse Attacks by Integral Type Observer