Consensus Strategy Research Articles

An efficient Proof-of-Authority consensus scheme against cloning attacks

Proof-of-Authorization (PoA) consensus algorithms are widely used in permissioned blockchain networks due to their high throughput, security, and efficiency. However, PoA is susceptible to cloning attacks, where attackers copy the authenticator identity and key, thereby compromising the consensus integrity. This study proposes a novel randomized authenticator within the PoA framework to mitigate cloning attacks and solve the leader selection bottleneck. The main contributions include 1) Introducing unpredictability in leader selection through Verifiable Random Functions (VRFs) to prevent identity duplication.2) Dynamic group management using a hierarchical decentralized architecture of distributed ledgers that balances security and performance.3) Using threshold signatures to avoid a single point of failure among validators.4) Comprehensively analyzing attacks, security, randomness, and availability.5) Evaluating the effectiveness of a randomized authenticator by means of OMNET++ simulations to assess efficiency. By integrating randomness into leader selection and robust consensus design, the approach enables reliable and secure dynamic group management in decentralized networks.

Teledermoscopic triage of melanoma-suspicious skin lesions is safe: A retrospective comparative diagnostic accuracy study with multiple assessors.

The rising incidence of melanoma and the high number of benign lesions excised due to diagnostic uncertainty highlight the need for effective patient triage. This study assesses the safety and accuracy of teledermoscopic triage on a high-prevalence case set with pre-triaged, challenging, melanoma-suspicious lesions. Five dermatologists independently reviewed 250 retrospectively extracted patient cases. Teledermoscopy assessments were simulated for panels of 1, 2, 3 and 5 assessors using two distinct consensus strategies, Caution Protocol and Majority Vote, and the sensitivity and specificity of the patient triages were calculated. Triage by a single teledermatologist showed a sensitivity of 92.3% and a specificity of 58.7%. Sensitivity improved with the number of assessors, particularly when using the Caution Protocol, though with a considerable drop in specificity. The Majority Vote showed a more balanced improvement in sensitivity and specificity. Safety analyses indicated that diagnostic accuracy decreased with poor image quality and increased case difficulty. Expert teledermoscopic triage of melanocytic skin lesions is highly sensitive and lowers the need for unnecessary excision procedures by half while dismissing as few as 0.4% (95% confidence interval 0-0.6%) of melanomas, even when applied to a high-prevalence pre-triaged subpopulation. Implementation of safety procedures increases accuracy. Using multiple teledermatologists increases sensitivity but at the cost of specificity unless a Majority Vote consensus strategy is applied. Future teledermoscopy guidelines should encompass safety procedures and protocols for disagreement between assessors.

Distributed adaptive secure consensus control for multiagent systems under denial-of-service attacks

This paper investigates the security consensus of multiagent systems (MASs) under denial-of-service (DoS) attacks. First, we introduce a novel dynamic consensus protocol against DoS attacks by utilising relative measurement outputs. Then, based on the edge pinning technique, the novel distributed synchronous adaptive laws are designed to tune a small fraction of coupling strengths between neighbouring agents, under which all updating of coupling strengths will stop when DoS attacks are activated. To extend the application of secure dynamic edge pinning consensus scheme, we further develop the distributed asynchronous adaptive laws, under which only the coupling strengths of the attacked edges stop updating rather than all coupling strengths when DoS attacks are activated. Note that the proposed secure dynamic edge pinning consensus protocols with distributed synchronous and asynchronous adaptive laws are fully distributed and operate independently of any global information. At last, we validate the validity of the proposed distributed adaptive secure dynamic consensus schemes via an example.

Getting Everyone on Board: Building Consensus Around a New Idea

Abstract: This essay explores effective strategies for building consensus around new ideas. The introduction establishes why buy-in is crucial for successful organizational change. It describes the author's past work witnessing positive and negative impacts of consensus. The essay then outlines five key steps for gaining support: understanding diverse perspectives through listening; explaining the rationale and benefits of ideas compellingly; addressing skepticism through collaboration rather than persuasion; rallying stakeholders around a shared inspiring vision; and leading with integrity through role-modeling values. Each step provides research and practical examples. The conclusion emphasizes how consensus cultivation requires empathy, problem-solving over edicts, and inspires higher commitment and performance outcomes through collaborative leadership focused on all stakeholders.

Adaptive distributed unknown input observer for linear systems

This paper studies the adaptive distributed unknown input observer (ADUIO) for linear systems with local outputs, which contains a group of local observers under directed graph. The difficulty is the adaptive estimation of global output for the systems with unknown inputs. To solve the problem, disturbance decoupling principle and leader-following consensus strategy are integrated to estimate local outputs of other observers, which can be accumulated to recover the global outputs. Based on the estimated global outputs, an ADUIO is constructed to estimate the full state which avoids using local output matrices of other observers and global information of the graph. Different from the extensive joint detectability assumption in existing results, a detectability assumption is given to make the estimation errors converge to zero asymptotically.

Consensus algorithms for double-integrator dynamics with different velocity and actuator saturation constraints

This paper considers consensus strategy design for double-integrator multi-agent systems with matched disturbances under a directed graph. Specifically, we consider the case where both the control inputs and velocities of the agents are subject to different and asymmetric constraints. A novel distributed algorithm exploiting saturation functions is proposed to achieve asymptotic consensus without violating the predefined velocity and actuator saturation constraints. Subsequently, we extend the obtained results to event-triggered communication, where agent position broadcasting occurs only when specific triggering conditions are met. We rigorously prove that there exists a positive minimum inter-event time to rule out Zeno behavior. Finally, the simulation results confirm the theoretical findings and illustrate the achievable performance.

Distributed delay control strategy for leader-following cyber secure consensus in Riemann–Liouville fractional-order delayed multi-agent systems under denial-of-service attacks

Considered distributed delay control strategy for Leader-Following cyber secure consensus in Riemann–Liouville fractional-order delayed Multi-Agent system under Denial-of-Service Attacks, where the associated topology is a switching weighted digraph. By using the classical Lyapunov technique and algebraic graph theory some algebraic criteria are offered. The new aspect of this paper is how thoroughly it discusses denial-of-service (DoS) attacks in the context of fractional-order delayed systems and how to create defences to keep cybersecure consensus maintained. It suggests a distributed delay control approach that ensures agents can reach a consensus even with these difficulties and in the case of DoS (denial-of-service) attacks. This work is significant because it incorporates cybersecurity techniques to strengthen the system’s resilience against disruptive assaults, in addition to addressing the technical issues related to delays and fractional-order behaviours. This contribution is essential for enhancing the security and dependability of distributed systems in applications such as networked sensors and autonomous vehicles. To further demonstrate the applicability of our method, two instances are given.

Fluctuation suppression for UI-driven bias accuracy enhancement based on micro-machined gyroscopes array

Unknown-input (UI) fluctuations sourcing from external environment severely deteriorate the accuracy of micro-machined gyroscope because of the unpredictable statistical characteristics. To enhance the accuracy of a four-micro-gyros array under UI fluctuations, an array-based consensus strategy (ACS) consisting of a UI-driven bias model, a local estimator, and an array fusion estimator is proposed. The UI-driven bias model is originally constructed as two behavior-contrasted independent items according to different drift characteristics of individual gyro. For the local estimator, a UI-decoupling operation is proposed to transform the variance-unknown UI model implicitly into a linear combination of variance-estimated variables, which transforms the non-stationary model to an equivalent stationary model. For the array fusion estimator, the reconstructed weight coefficient is designed based on the support theory and Markowitz mean-variance theory to evaluate the confidence level of gyros. Experiment results show that the root-mean-square error (RMSE) and Allan bias instability of the estimated angular rate under UIs are 7.9×10−3 °/s and 3.87 °/h, which are respectively reduced by 85.1 % and 35.1 % compared with the average original outputs of the gyros array.

A collaborative approach to low-frequency noise mitigation

This case study examines efforts to study and mitigate low-frequency noise produced by boilers serving a large hospital. The work was completed by a collaborative group of citizens, hospital staff, acoustical consultants, and noise control solutions providers. While none of the noise controls are novel from an engineering perspective, the criteria, experimental methods, and consensus strategies used for this project could serve as a model for similar community challenges.

Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.

New Distributed Consensus Schemes With Time Delays and Output Saturation

Estimates of agents in a distributed consensus control are aligned with a particular value through interacting on communication graphs, which are of particular interest for researchers in the field of multi-agent coordination. Following this pattern, a discrete-time constrained consensus issue with generalized time delays is first established in this article, which is manipulated into an optimization problem via a quadratic performance index introduced as a global objective function. Then, a novel consensus scheme is investigated and proposed for handling this problem, enabling the consensus to approach the desired state globally and rapidly with optimal system property ensured. Besides, to advance the convergence speed and stability in resisting constant bias or oscillation, an adjustment control method is explored to construct a modified consensus scheme; further, to enhance the scene adaptability, fix topologies are extended to switching ones, and the latter is involved to develop another scheme on this basis. Moreover, the convergence and robustness of these three proposed consensus schemes are substantiated by theoretical analysis and numerical simulations. To highlight the practical implementations, the proposed consensus schemes are incorporated with a winner-take-all operation to accomplish multi-agent competitive coordination in a distributed way, and the results embody their effectiveness and superior consensus control ability, along with strong plasticity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is dedicated to investigating and optimizing distributed consensus schemes with time delays and output saturation with application to the competitive coordination of multi-agent systems. On the one hand, the consensus problem with output saturation receives limited attention, and few consensus algorithms consider both saturation limitation and channel gain. On the other hand, most existing studies on consensus with saturation consider simply the dynamic behaviour of agents without employing optimization, thus limiting further improvement of system performance. In this paper, a consensus algorithm is built from an optimization perspective that guarantees the state consensus of a multi-agent system with output constraints and generalized time delays. In addition, a consensus scheme is designed in a discrete-time framework and further modified and perfected along with the idea of considering time delays and expanding diverse topologies. Finally, experiments are conducted by applying the consensus scheme to a winner-take-all operation, with contributions of this paper verified.

A Grounded Theory Approach to Understanding Jombang Government Strategy in Facing the Era of Digitalization

In 1967, Glaser and Strauss created a book entitled "The Discovery of Grounded Theory; Strategies for Qualitative Research'', which was published and became a breakthrough in qualitative research. The work offers systematic consensus methodologies and strategies for a wide range of qualitative research practices. When talking about public administration, grounded theory is rarely used in literature studies, especially in Indonesia. In public administration, grounded theory methodology can be directed toward understanding how the government develops strategic processes for policy, both at the organizational and individual levels. This article discusses the grounded theory research method approach, which is aimed at understanding how the Jombang Regency Regional Government develops strategic processes in facing the era of digitalization. In-depth interviews were conducted with 10 high-ranking regional government officials. This is intended to understand in depth their understanding and experience in managing changing situations. The research results show that there are five core categories: (1) human resource development; (2) technological innovation; (3) improving the management system; (4) security; and (5) mindset. By using selective and axial coding, it was found that there were two main functions of strategy that emerged, namely (1) system maintenance; and (2) adaptation to global change. Keywords: Grounded Theory, Public Administration, Public Policy, Strategy

Distributed Consensus Multi-Distribution Filter for Heavy-Tailed Noise

Distributed state estimation is one of the critical technologies in the field of target tracking, where the process noise and measurement noise may have a heavy-tailed distribution. Traditionally, heavy-tailed distributions like the student-t distribution are employed, but our observation reveals that Gaussian noise predominates in many instances, with occasional outliers. This sporadic reliance on heavy-tailed distributions can degrade performances or necessitate frequent parameter adjustments. To overcome this, we introduce a novel distributed consensus multi-distribution state estimation method that combines Gaussian and student-t filters. Our approach establishes a system model using both Gaussian and student-t distributions. We derive a multi-distribution filter for a single sensor, assigning probabilities to Gaussian and student-t noise models. Parallel estimation under both distributions, utilizing Gaussian and student-t filters, allows us to calculate the likelihood of each distribution. The fusion of these results yields a mixed-state estimation and corresponding error matrix. Recognizing the increasing degrees of freedom in the student-t distribution over time, we provide an effective approximation. An information consensus strategy for multi-distribution filters is introduced, achieving global estimation through consensus on fused local filter results via interaction with neighboring nodes. This methodology is extended to the distributed case, and the recursive process of the distributed multi-distribution consensus state estimation method is presented. Simulation results demonstrate that the estimation accuracy of the proposed algorithm improved by at least 20% compared to that of the traditional algorithm in scenarios involving both Gaussian and heavy-tailed distributions.

Distributed optimal coordination algorithm for nonlinear second-order multi-agent systems and its application to vehicle platoon

This paper addresses the problem of distributed optimal coordination over second-order multi-agent systems, where the dynamics are nonlinear and unknown. To solve this problem, a state-based observer is designed to handle the unknown matched and unmatched nonlinear terms. Using this observer and the proportional–integral control concept, a distributed optimal coordination algorithm with fixed control parameters is constructed through the consensus scheme and gradient flow method. With the help of the Routh–Hurwitz stability criterion, the exponential convergence proof is guaranteed under the condition that the nonlinear term and the convex local cost function are all locally Lipschitz. Finally, we present four simulation examples, including the vehicle platoon’s cooperative control, to demonstrate the efficiency of our proposed algorithms.

Robust median consensus cubature Kalman filter for distributed sensor networks

Distributed estimation of multiagent systems has gained increasing attention in recent years, with consensus algorithms being one of the popular research topics. However, most existing algorithms aim to reach a consensus on the average value of all nodes, which can be significantly affected by outliers and disturbances. To achieve a more resilient distributed estimation, this paper focuses on the median value consensus strategy. Specifically, a continuous median consensus method with an adaptive parameter is presented and discretized using time-varying steps. This approach eliminates chattering errors caused by discretization and enhances convergence speed considerably. Furthermore, utilizing the proposed algorithm, a median consensus cubature Kalman filter (MC-CKF) is designed for nonlinear scenarios. Simulation results demonstrate that our proposed algorithm outperforms traditional algorithms when facing a denial-of-service attack.

Adaptive Dynamic Average Consensus Scheme With Preserving Privacy and Against False Data Injection Attacks: Dynamic Event-Triggered Mechanism

Adaptive Dynamic Average Consensus Scheme With Preserving Privacy and Against False Data Injection Attacks: Dynamic Event-Triggered Mechanism

Distributed event-triggered collaborative control for multiagent systems against DoS attacks

This paper deals with the event-triggered collaborative control problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. For large-scale MASs, it is difficult to implement fully distributed collaborative control by tuning the coupling strengths of all edges due to the limitation of computing resources in practical applications. To overcome this difficulty, an edge-pinning-based adaptive strategy for coupling strengths is established, under which only the coupling strengths of partial edges are tuned instead of all ones. Under this strategy, the edge-pinning-based resilient distributed event-triggered synchronous and asynchronous adaptive laws are designed, both of which ensure the implementation of security consensus against DoS attacks and the elimination of Zeno behavior. It should be pointed out that the designed two resilient consensus strategies are fully distributed. Additionally, under these event-triggered strategies, the updating of control protocols and the monitoring of triggering functions do not require continuous communications, thus reducing the communication burden among agents. Finally, simulations conducted on a network composed by six aircrafts are provided to illustrate the theoretical results.

Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier-actor-critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.

A Marginal Cost Consensus Scheme With Reset Mechanism for Distributed Economic Dispatch in BESSs

A Marginal Cost Consensus Scheme With Reset Mechanism for Distributed Economic Dispatch in BESSs

Convergence of the Distributed SG Algorithm Under Cooperative Excitation Condition.

In this article, a distributed stochastic gradient (SG) algorithm is proposed where the estimators are aimed to collectively estimate an unknown time-invariant parameter from a set of noisy measurements obtained by distributed sensors. The proposed distributed SG algorithm combines the consensus strategy of the estimation of neighbors with the diffusion of regression vectors. For the theoretical investigation of the proposed algorithm, the main challenge lies in analyzing the influence of the Laplacian matrix on the state transition matrix and the properties of the product of nonindependent and nonstationary random matrices. Some analysis techniques such as graph theory and martingale theory are used to deal with the above issues. A cooperative excitation condition is introduced, under which the convergence of the distributed SG algorithm can be obtained without relying on the independency or stationarity assumptions of regression vectors which are commonly used in the existing literature. Furthermore, the convergence rate of the algorithm can be established. Finally, we show that all the sensors can cooperate to fulfill the estimation task even though any individual sensor cannot by a simulation example.