Dynamic Consensus Research Articles

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.

Dynamic Byzantine Fault-Tolerant Consensus Algorithm with Supervised Feedback Mechanisms

Among the existing consensus algorithms, there are very few low-latency consensus algorithms that can simultaneously take into account node dynamics, fault tolerance, and scalability, and which are applicable to large-scale open scenarios where low latency is required. Therefore, this paper proposes a low-latency scalable dynamic consensus algorithm with high fault tolerance utilizing a combination of layered and threshold signature technologies, known briefly as LTSBFT. Firstly, LTSBFT achieves linear communication complexity through the utilization of threshold signature technology and a two-layer structure. Secondly, the mutual supervision feedback mechanism among nodes enables the attainment of linear complexity for reaching consensus on the faulty upper-layer nodes during the view-change. Lastly, a node dynamic protocol was proposed for the first time to support dynamic changes in the number of nodes during the consensus process. That is to say, consensus can still be reached when the number of nodes dynamically changes without interrupting the client’s request for consensus in the network. The experimental results indicate that LTSBFT exhibits lower communication latency and higher throughput compared to the classic HotStuff and PBFT algorithms. Furthermore, in comparison to double-layer PBFT, the LTSBFT has been demonstrated to have improved fault tolerance.

Overlapping community-driven dynamic consensus reaching model of large-scale group decision making in social network

Large-scale group decision-making (LSGDM) serves as a pivotal tool for facilitating consistent decision results through intricate interactions among individuals within the network. However, the impact of complex trust relationships within overlapping communities on consensus is often overlooked in many studies. Moreover, the dynamic interaction between overlapping communities and the consensus reaching process (CRP) is seldom taken into account. This paper aims to build an overlapping community-driven consensus reaching model for addressing LSGDM challenges in social network. Given the advantages of intuitionistic fuzzy numbers (IFNs) in uncertain information representation, IFNs are used to express evaluation information and trust information. Firstly, a novel overlapping community detection method is developed to divide subgroups and detect overlapping communities. Secondly, to determine reliable subgroup weights, this paper constructs a weight determination model that considers multiple factors and their internal correlations. Then, an overlapping community-driven dynamic consensus model is proposed, which provides a new way to resolve the opinion conflicts, considering the dynamic change of CRP. Simultaneously, the reverse effect of opinion adjustment on the social trust network is considered. Finally, the practicality of the proposed model is demonstrated through illustrative cases. Furthermore, through a comparative analysis, the superiority of the proposed model is demonstrated and the efficiency improvement for CRP is verified.

Social network group decision-making method based on stochastic multi-criteria acceptability analysis for probabilistic linguistic term sets

In social network group decision-making (SNGDM) problems, decision-makers (DMs) often express their opinions or preferences using probabilistic linguistic term sets (PLTSs). In this paper, a novel SNGDM method for probabilistic linguistic information is proposed. Firstly, to obtain the prioritization of DMs in the clustering process, a DM clustering method for SNGDM is developed considering the influence of trust relationships and opinions similarity among DMs. Then, to satisfy the requirements of the consensus reaching process in SNGDM, a dynamic consensus threshold calculation method based on an optimization model is introduced. Furthermore, in the consensus measure stage, a novel consensus measure method for both DMs and subgroups is proposed, using a stochastic multi-criteria acceptability analysis (SMAA) method. Based on the consensus measure method, a novel SNGDM method based on SMAA for PLTSs is proposed. Finally, a case study of service quality evaluation in institutional pensions is used to illustrate the effectiveness of the proposed method. The results of case show that the proposed method can adjust consensus thresholds dynamically based on each round of collective opinions, and can solve the SNGDM problems when DMs can not provide their preferences for criteria weights.

Numerical methods for distributed stochastic compositional optimization problems with aggregative structure

ABSTRACT The paper studies the distributed stochastic compositional optimization problems over networks, where all the agents' inner-level function is the sum of each agent's private expectation function. Focusing on the aggregative structure of the inner-level function, we employ the hybrid variance reduction method to obtain the information on each agent's private expectation function, and apply the dynamic consensus mechanism to track the information on each agent's inner-level function. Then by combining with the standard distributed stochastic gradient descent method, we propose a distributed aggregative stochastic compositional gradient descent method. When the objective function is smooth, the proposed method achieves the convergence rate O ( K − 1 / 2 ) . We further combine the proposed method with the communication compression and propose the communication compressed variant distributed aggregative stochastic compositional gradient descent method. The compressed variant of the proposed method maintains the convergence rate O ( K − 1 / 2 ) . Simulated experiments on decentralized reinforcement learning verify the effectiveness of the proposed methods.

Probabilistic Linguistic Petri Nets for Knowledge Representation and Acquisition With Dynamic Consensus Reaching Process

Probabilistic Linguistic Petri Nets for Knowledge Representation and Acquisition With Dynamic Consensus Reaching Process

Event-Triggered Consensus Control in Euler–Lagrange Systems Subject to Communication Delays and Intermittent Information Exchange

In this paper, we investigate the consensus control problem of Euler–Lagrange systems which can be used to describe the motion of various mechanical systems such as manipulators and quadcopters. We focus on consensus control strategies, which are important for achieving coordinated behavior in multi-agent systems. The paper considers the key challenges posed by random communication delays and packet losses that are increasingly common in networked control systems. In addition, it is assumed that each system receives information from neighboring agents intermittently. Addressing these challenges is critical to ensure the reliability and efficiency of such systems in real-world applications. Communication delay is time-varying and can be very large, but should be smaller than some bounded constant. To decrease the frequency of control input updates, we implement an event-triggered scheme that regulates the controller’s updates for each agent. Specifically, it does not update control inputs at traditional fixed intervals, but responds to predefined conditions and introduces a dynamic consensus item to handle information irregularities caused by communication delays and intermittent information exchange. The consensus can be achieved if the communication graph of agents contains a spanning tree with the desired velocity as the root node. That is, all Euler–Lagrange systems need to obtain the desired velocity, directly or indirectly (via neighbors), to reach consensus. We establish that the Zeno behavior can be avoided, ensuring a positive minimum duration between successive event-triggered instances. Finally, we provide simulation results to show the performance of our proposed algorithm.

MRAC-based dynamic consensus of linear systems with biased measurements over directed networks

In this paper we address the dynamic leaderless consensus control problem for generic linear systems (stable, unstable, marginally stable, etc.) interconnected over directed networks and under the influence of biased measurements. Essentially, the control problem consists in redesigning a standard distributed consensus controller which, for each system, relies on own state biased measurements and respective erroneous data received from a set of neighbors. The difficulty in such a scheme resides in the fact that the measurement bias is directly amplified by the control gain so it cannot be handled as an additive external disturbance. Our control design relies, on one hand, on the solution of a Riccati equation and, on the other, on the design of an estimator reminiscent of a model-reference-adaptive control design. The estimator successfully computes a bias estimate and completely compensates for its effect if the bias is constant—indeed, in this case, we establish exponential stability of the consensus manifold and we show that the controller provides robustness with respect to time-varying biases.

Singular-Perturbations-Based Analysis of Dynamic Consensus in Directed Networks of Heterogeneous Nonlinear Systems

Singular-Perturbations-Based Analysis of Dynamic Consensus in Directed Networks of Heterogeneous Nonlinear Systems

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Agriculture Internet of Things (AIoTs) deployments require design of high-efficiency Quality of Service (QoS) & security models that can provide stable network performance even under large-scale communication requests. Existing security models that use blockchains are either highly complex or require large delays & have higher energy consumption for larger networks. Moreover, the efficiency of these models depends directly on consensus-efficiency & miner-efficiency, which restricts their scalability under real-time scenarios. To overcome these limitations, this study proposes the design of an efficient Q-Learning bioinspired model for enhancing QoS of AIoT deployments via customized shards. The model initially collects temporal information about the deployed AIoT Nodes, and continuously updates individual recurring trust metrics. These trust metrics are used by a Q-Learning process for identification of miners that can participate in the block-addition process. The blocks are added via a novel Proof-of-Performance (PoP) based consensus model, which uses a dynamic consensus function that is based on temporal performance of miner nodes. The PoP consensus is facilitated via customized shards, wherein each shard is deployed based on its context of deployment, that decides the shard-length, hashing model used for the shard, and encryption technique used by these shards. This is facilitated by a Mayfly Optimization (MO) Model that uses PoP scores for selecting shard configurations. These shards are further segregated into smaller shards via a Bacterial Foraging Optimization (BFO) Model, which assists in identification of optimal shard length for underlying deployment contexts. Due to these optimizations, the model is able to improve the speed of mining by 4.5%, while reducing energy needed for mining by 10.4%, improving the throughput during AIoT communications by 8.3%, and improving the packet delivery consistency by 2.5% when compared with existing blockchain-based AIoT deployment models under similar scenarios. This performance was observed to be consistent even under large-scale attacks.

Convergence speed of dynamic consensus with delay compensation

A well-known drawback in distributed average consensus of multi-agent systems is that the exchanged information is usually delayed due to the time elapsed during the data transmission process. Using classical dynamic average consensus, delays may lead to poor performance or even instability. In this paper, we propose a novel dynamic consensus method that counteracts the negative effects of delays by means of delay compensation techniques. The interest of our dynamic consensus method with delay compensation is that it converges under mild conditions on graph connectivity and bounded reference signals, no matter how large the delays are, as long as delays are fixed and known. We also provide a formal characterization of the convergence speed of our method. Additionally, our results apply to fixed directed strongly connected, and undirected topologies.

Multi-attribute decision-making based on data mining under a dynamic hybrid trust network

The issues arising from the evolution of big data and network environment have rendered the relationship among experts to no longer be an independent multi-attribute group decision-making process. In response, a PROMETHEE multi-attribute decision-making method based on data mining under dynamic hybrid trust network is proposed. This method incorporates evaluation similarity based on degree centrality and expert trust values based on K-hop centrality in the social network to determine the experts’ weights. Furthermore, because the expert weight is somewhat affected by the attribute weight, the public-level attribute weight is obtained through data crawling and TF-IDF technology. The comprehensive attribute weight is acquired through a combination of expert-level attribute weights. Additionally, establishing a minimum adjustment cost model enables experts to follow a dynamic consensus reaching process in a composed hybrid trust network. Finally, to express the evaluation information more accurately in a complex linguistic environment, the probabilistic linguistic PROMETHEE method is introduced and applied to the site selection evaluation of charging and swapping stations. This highlights the feasibility and effectiveness of the proposed method through the comparison of decision-making methods and parameter sensitivity analysis.

Dynamic Coordinated Control for Multiconverter Systems via a Multistep Prediction Scheme

Modular DC-DC converters are popular in DC power systems such as demagnetization systems due to their advantages of high efficiency, high reliability, and low current stress on components. However, the dynamic consensus of output currents of the multiple converter degaussing systems (MCDSs) has not been well solved yet, especially when communication delay is considered. In this paper, a novel coordinated control method based on multi-step state predictions with active compensation of delay is proposed to address the poor dynamic consensus performance of currents in MCDSs. The implementation of the scheme consists of two main phases: the design of a multi-step state predictor for MCDSs and the optimization of the coordinating cost of currents. The multi-step state predictor can estimate immeasurable future states of converters over a large horizon length, which presents a novel approach to the current regulator design. The optimal control protocol is derived by minimizing a distributed coordinating performance index function (PIF) for the output currents. This optimization of the PIF minimizes the consensus error between output currents for converters and compensates for communication constraints. Further, the conditions for simultaneous stability and consensus of the closed-loop degaussing system with the distributed multi-step state prediction controller are given. Finally, the performance of the proposed scheme is verified by numerous case studies in an FPGA-based OPAL-RT experimental testbed.

Distributed Model-Predictive Control Using Exponentially Weighted Laguerre Functions and Periodic Time Triggering for Dynamic Consensus of Linear Multiagent Systems

Model Predictive control (MPC) is becoming increasingly popular in addressing the consensus problem of multi-agent systems (MAS) due to its ability to manage constraints in a multivariable control framework and to perform online optimization. However, in order to achieve the desired closed loop performance, longer prediction and control horizons are essential for conventional MPC. This increases the computational complexity and causes the numerical instability of the optimization problem. This concern is addressed in this article by introducing an exponential-data-weighted Laguerre-based control design that minimises computational burden, improves numerical conditioning, and provides a specified degree of closed loop stability of the MAS. The proposed design which incorporates an embedded integrator model, rejects constant disturbances, avoiding the need for a separate disturbance model. Furthermore, periodic time-triggered control (PTTC) is incorporated with the consensus algorithm, enabling a relaxation in the over-usage of resources. An explicit formula for computing the upper bound of triggering interval is also derived. The proposed algorithm outperforms current methods in terms of numerical conditioning, stability margins, and rejection of deterministic disturbances. Validation of the proposed strategy is performed through simulation of dynamic consensus on a cluster of grounded vehicles.

Distributed Online Voltage Control With Fast PV Power Fluctuations and Imperfect Communication

Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row-stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.

A consensus based adaptive virtual capacitor control strategy for reactive power sharing and voltage restoration in microgrids

In the islanded microgrid, virtual synchronous generator (VSG) control can solve the problems of insufficient damping and low inertia of the system. However, reactive power among distributed generation (DG) units controlled by VSG scannt be shared according to capacity due to the mismatch of line impedance, and the bus voltage can be significantly below the rated value due to the influence of droop control and line impedance. In this paper, an adaptive virtual capacitor control strategy (AVSSC) based on consensus is proposed to solve this problem. The strategy ensures the accurate sharing of reactive power among VSGs and adjusts the estimated bus voltage to the rated value by paralleling virtual capacitors. The voltage estimates used in AVSSC are obtained by the proposed discrete dynamic consensus algorithm (DDCA). The algorithm can obtain the average value of the local voltage estimates of all VSG units to eliminate the limitation of local information and the influence of accidental factors on voltage estimation. The multi-agent consensus theory proves that AVSSC can achieve the expected goal. The simulation results of a microgrid system with four parallel VSGs show that the AVSSC can accurately share reactive power, and the bus voltage error decreases from 0.81% to 0.39%.

Distributed Fault Detection and Dynamic Event-Triggered Consensus for Heterogeneous Multiagent Systems Under Deception Attacks

This paper focuses on the problem of distributed fault detection and leader-following output consensus for heterogeneous multiagent systems subject to deception attacks. During the information exchange and dissemination, a malicious attacker can make full use of specialized computer technology and launch stochastic deception attacks against some vulnerable agents over the network. The attack signals in actual operation tend to be energy-constrained, and Bernoulli distribution can be used to describe the random features. Taking the attack information into account, the distributed fault detection observer and the dynamic consensus compensator are designed in two separate steps. In order to reduce unnecessary information transmission, a dynamic event-triggered mechanism with output-dependent threshold is introduced to the adjustment of consensus protocol. According to Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions are derived for developing the model gains of the observer and the compensator. Finally, a simulation example of RLC circuit systems is provided to illustrate the effectiveness of the obtained theoretical results.

Dynamic consensus of linear multi-agent system using self-triggered distributed model predictive control

This article discusses self-triggering algorithm using distributed model predictive control (DMPC) to achieve dynamic consensus in linear multi-agent systems (MASs). The iterative computations and communications required at each time step in traditional consensus algorithms cause escalation of the energy consumption and shorten the life span of the MAS. An attempt to solve this problem is made by proposing a sequential self-triggering consensus algorithm, where each agent computes its own triggering instants. A Laguerre based DMPC design is adopted that notably reduces the computational complexity of conventional DMPC. The proposed self-triggered DMPC algorithm optimizes the control input and triggering interval while guaranteeing the dynamic consensus of the agents. By virtue of the Laguerre function based control architecture, the additional computations owing to the self-triggered algorithm do not impose stress on the controller; yet reduce the load on communication resources. The equality constraint on the terminal state of the agents is utilized along with Lyapunov criteria to establish the closed loop stability of the MAS. The proposed scheme achieves a considerable drop in controller design computations as well as data transmissions among agents, and the same is established by comparing these traits of existing schemes while achieving comparable performance. The proposed algorithm is verified through simulation of platoon configuration of vehicles, each of which is modeled as a linear multi-input multi-output (MIMO) system.

VaccineChain: A checkpoint assisted scalable blockchain based secure vaccine supply chain with selective revocation

In the present era of the pandemic, vaccination is necessary to prevent severe infectious diseases, i.e., COVID-19. Specifically, vaccine safety is strongly linked to global health and security. However, the main concerns regarding vaccine record forgery and counterfeiting of vaccines are still common in the traditional vaccine supply chains. The conventional vaccine supply chains do not have proper authentication among all supply chain entities. Blockchain technology is an excellent contender to resolve the issues mentioned above. Although, blockchain based vaccine supply chains can potentially satisfy the objectives and functions of the next-generation supply chain model. However, its integration with the supply chain model is still constrained by substantial scalability and security issues. So, the current blockchain technology with traditional Proof-of-Work (PoW) consensus is incompatible with the next-generation vaccine supply chain framework. This paper introduces a model named “VaccineChain” - a novel checkpoint-assisted scalable blockchain based secure vaccine supply chain. VaccineChain guarantees the complete integrity and immutability of vaccine supply records to combat counterfeited vaccines over the supply chain. The dynamic consensus algorithm with various validating difficulty levels supports the efficient scalability of VaccineChain. Moreover, VaccineChain includes anonymous authentication among entities to provide selective revocation. This work also consists of a use case example of a secure vaccine supply chain using checkpoint assisted scalable blockchain with customized transaction generation-rule and smart contracts to demonstrate the application of VaccineChain. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of VaccineChain. Further, the detailed performance analysis with test simulations shows the practicability of VaccineChain.

Distributed Autonomous Economic Control Strategy for DC Microgrid

A distributed autonomous economic control strategy is proposed to realize the economic operation of the DC microgrid in the islanded state. The control strategy first introduces incremental cost in primary control, but due to the influence of line impedance, it is not possible to precisely achieve the consensus of incremental cost at each unit, and it generates a steady-state error. To overcome this problem, a distributed secondary control based on a dynamic consensus algorithm is designed on the basis of primary control, which makes each distributed generator operate under equal incremental cost. The two layers of control strategies cooperate work together to realize the hierarchical-distributed autonomous economic operation of the DC microgrid. Finally, the simulation model of the DC microgrid is constructed, which verifies the effectiveness of the proposed method.