Distributed Consensus Algorithm Research Articles

Finite-time consensus of heterogeneous unmanned aerial agents in network with fixed and switching topology

A finite-time consensus protocol is presented in this paper for a network of connected heterogeneous agents with fixed and switching topology. In this study, two cases directed networks with fixed topology and directed networks with switching topology are examined. Convergence analysis for both the cases are presented. An independent tracking controller, which assures that an agent follows an ideal trajectory designed by sliding mode based consensus control, is devised. Agents stability are independent of the distributed consensus algorithm. Under the proposed control methodology, the measurement vectors of heterogeneous agents converge to the agreement value in a finite amount of time. The comparison results with the existing works show the proposed algorithm’s improved performance in terms of convergence time in presence of disturbances.

Optimal and distributed energy management in interconnected energy hubs

Recently, multi-carrier energy systems (MCESs) have been rapidly developed as flexible multi-generation systems aiming to satisfy load demands by purchasing, converting, and storing different energy carriers. This study specifically focuses on the optimal and robust large-scale coordination of interconnected energy hubs (IEHs) in an iterative consensus-based procedure considering distribution network losses. Furthermore, a new robust-based hybrid IGDT/consensus algorithm is introduced to achieve risk-averse optimal energy management in IEHs under uncertainty. The fast convergence, needless to collect the total information from all hubs, minimal computational burden, and more robust communication system are the most important features of the proposed distributed consensus algorithm in this study. The effectiveness of the proposed consensus algorithm is verified by simulation results considering various energy trading structures in IEHs at different scales. The obtained results highlight the scalability capability of the proposed method. Regarding an IEHS of 30 energy hubs, the computation burden is lightened by 0.53 (s) and 0.1917 (s), respectively with and without uncertainty. Considering distribution network losses, the total purchasing costs can be increased by 8%. The simulation results also reveal an increase of 11% in the total power trading under the uncertainty.

Distributed Constrained Consensus of Multiagent Systems With Uncertainties and Disturbances Under Switching Directed Graphs

This paper provides a distributed leaderless consensus control framework for nonlinear multi-agent systems with time-varying asymmetric state constraints, uncertainties, and disturbances under switching directed graphs. In such a framework, original constrained states of agents are first transformed into free states in a transformed state space. To deal with switching directed graphs, we drive agents towards consensus in the transformed space by leveraging a model reference control scheme, and it is sufficient that the original states reach consensus strictly subject to the time-varying constraints under mild assumptions. A single-layer neural network with weights adapted online is leveraged to approximate the uncertainties in agent dynamics. For external disturbances and reconstruction errors in the approximation, we introduce a robust term with an adaptive gain for compensation. Distributed consensus algorithms are proposed, respectively, for multi-agent systems with first- or second-order dynamics. We prove convergence to consensus via Lyapunov analysis and study the proposed algorithms' performance using numerical simulations.

Narrowing and heuristic search for symbolic reachability analysis of concurrent object-oriented systems

A concurrent system specified as a rewrite theory can be analyzed symbolically using narrowing-based reachability analysis. Narrowing-based approaches have been applied to formally analyze cryptographic protocols and parameterized protocols. However, existing narrowing-based analysis methods, based on a breadth-first-search strategy, cannot deal with generic distributed systems with objects and messages due to the symbolic state-space explosion problem and implicit constraints imposed on object-oriented systems. This paper proposes a heuristic search approach for narrowing-based reachability analysis to guide the search for counterexamples with a small number of objects. As a result, our method can effectively find a counterexample if an error state is reachable. In addition, this paper also shows how to encode implicit object-oriented constraints using order-sorted signatures and equational constraints. We demonstrate the effectiveness of our technique using a nontrivial distributed consensus algorithm.

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance.

Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit

Distributed Consensus Algorithm for Decision-Making in Multi-agent Multi-armed Bandit

Retracted: Distributed Consensus Algorithm for Nonholonomic Wheeled Mobile Robot

Retracted: Distributed Consensus Algorithm for Nonholonomic Wheeled Mobile Robot

Adaptive protocol of raft in wireless network

Original distributed consensus algorithms are typically engineered with wired communication networks in mind. However, when these algorithms are applied in wireless networks, the unique characteristics of wireless communication among nodes, including high error rates, variable latency, and dynamic network topology, present novel challenges that necessitate the development of adaptive protocols. This article aims to address these challenges by designing an adaptive protocol specifically for the implementation of the Raft consensus algorithm within wireless environments. We first outline the key stages of this adaptive protocol, focusing on enhancing robustness and efficiency in the state synchronization and routing processes, which are demonstrated through extensive simulations. The proposed adaptive protocol intends to ensure consistent states across nodes in wireless networks, which promote reliable and effective distributed systems for a variety of applications in autonomous wireless networks, including Vehicle-to-everything (V2X), Internet of Things (IoT), and edge computing.

Navigating the Promises and Hurdles of Blockchain Technology: A Journey through Opportunities and Challenges

Everyone has come across the buzz word of the industry “Blockchain”. Today, Blockchain and the crypto-currencies have become parallel platforms where people have started performing their monetary/non-monetary transactions. It is a very popular technology that rules almost every sector in foreign country, but not approved fully by Indian government. Blockchain is the collection of blocks connected in a linear list fashion [1]. Each block encloses its own data, also holds the hash of the previous block which ties a new block to the previous one unlike linked list. If one of the blocks is removed from the blockchain, the entire chain will collapse because of loss of connectivity. Cryptography in hands with computer science ensures that nobody can change the data in blockchain network. Once information is added to the blockchain, it is impossible to remove or edit. The immutable nature makes the blockchain to serve as a trustworthy database of information. Once validated by the network’s consensus algorithm(s), these blocks are added to an existing sequential chain of cryptographic hash-linked blocks, to ensure the integrity of the data in blockchain [2]. Hence this paper brings about the structure of blockchain, the creation of blocks, the data structure-Merkle tree to store the transactions, the distributed consensus algorithms to achieve agreement among the nodes, the popular use-cases and applications of blockchain.

A Delay Classification-Based Approach to Distributed Consensus of Nonlinear Time-Delay Multiagent Systems

This paper investigates the problem of the fixed directed topology-based leader-following consensus control for nonlinear multiagent systems with output delays. Diverging from existing results, this study focuses on large time-varying output delay. Taking this condition into account, a novel delay classification-based distributed output feedback consensus algorithm is developed. First, we divide the output delay into two types through a designed constant and introduce a switching signal to distinguish these two types. Then, we build a distributed compensator for each follower using relevant outputs to reconstitute the leader-following tracking errors. This compensator relies on the output delay and is irrelevant to the nonlinear dynamics of the system. Then, we design a distributed linear controller for each follower with the compensator's assistance. Subsequently, a new unified Lyapunov-Krasovskii functional is developed to prove that all agents can realize leader-following full-state consensus with the action of the controller. We also provide sufficient conditions to demonstrate the controller's validity. Finally, a simulation example is supplied to validate the efficiency of our algorithm.

Rethink the Linearizability Constraints of Raft for Distributed Systems

With the deployment of modern hardware such as Flash-based SSDs and the high-speed network in distributed systems, the distributed consensus and consistency module (e.g., Raft) is typically the most time-consuming part. The reason lies in that Raft introduces some very strict constraints to ensure the linearizability. Therefore, in this paper, we rethink these constraints in-depth and find that some of them are not necessary, and can be broken to accelerate the performance significantly without breaking the linear consistency for distributed systems. An improved distributed consensus algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BUC-Raft</i> (Breaking Unnecessary Constraints of Raft) is proposed in this paper and implemented in an industry-level distributed system. The experimental results suggest that both the write and the read performance can be accelerated significantly by BUC-Raft.

Distributed collaborative optimal economic dispatch of integrated energy system based on edge computing

A distributed collaborative optimal dispatching strategy for the integrated energy system (IES), based on edge computing and consistency algorithm, is proposed in this paper. To solve the optimal dispatching problem of the IES coupled with electricity, heat, and gas, a cloud-edge-device architecture is constructed. A distributed group consensus algorithm (DGCA) is designed with two consistency protocols. The first protocol defines three groups of consistency variables, represented by the incremental cost of electricity, heat, and gas, respectively. The output estimation deviation of each component is used as a feedback. The second protocol considers the output estimation deviation of the three components as the consistency variables. The coupling variables of the two consistency protocols tend to be consistent, which can obtain the optimal output of each unit and achieve the optimal operation of the system. Furthermore, the proposed strategy accounts for transmission loss during transmission. Finally, through the hardware in the loop simulation based on the edge intelligent terminal (EIT), the effectiveness and feasibility of the strategy are verified, considering different cases.

Multi-stage Proof-of-Works: Properties and vulnerabilities

Since its appearance in 2008, Bitcoin has attracted considerable attention. So far, it has been the most successful cryptocurrency, with the highest market capitalization. Nevertheless, due to the method it uses to append new transactions and blocks to the blockchain, based on a Proof-of-Work, Bitcoin suffers from poor scalability, which strongly limits the number of transactions per second and, hence, its adoption as a global payment layer for everyday uses. In this paper we analyze some recent proposals to address this issue. In particular, we focus our attention on permissionless blockchain protocols, whose distributed consensus algorithm lies on a Proof-of-Work composed of k>1 sequential hash-puzzles, instead of a single one. Such protocols are referred to as multi-stage Proof-of-Works. We consider a simplified scenario, commonly used in the blockchain literature, in which the number of miners, their hashing powers, and the difficulty values of the hash-puzzles are constant over time. Our contribution is threefold. Firstly, we derive a closed-form expression for the mining probability of a miner, that is, the probability that the miner completes the Proof-of-Work of the next block to be added to the blockchain, before any other miner does. Secondly, we show that in multi-stage Proof-of-Works the mining probability might not be strictly related to the miner hashing power. This feature could be exploited by a smart miner, and could open up potential fairness and decentralization issues in mining. Finally, we focus on a more restricted scenario and present two attacks, which can be applied successfully against multi-stage Proof-of-Works: a Selfish Mining attack and a Selfish Stage-Withholding attack. We show that both are effective, and we point out that Selfish Stage-Withholding can be seen as a complementary strategy to Selfish Mining, which in some cases increases the selfish miner profitability in the Selfish Mining attack.

Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology.

Information aggregation in distributed sensor networks has received significant attention from researchers in various disciplines. Distributed consensus algorithms are broadly developed to accelerate the convergence to consensus under different communication and/or energy limitations. Non-Bayesian social learning strategies are representative algorithms for distributed agents to learn progressively an underlying state of nature by information communications and evolutions. This work designs a new non-Bayesian social learning strategy named the hypergraph social learning by introducing the higher-order topology as the underlying communication network structure, with its convergence as well as the convergence rate theoretically analyzed. Extensive numerical examples are provided to demonstrate the effectiveness of the framework and reveal its superior performance when applying to sensor networks in tasks such as cooperative positioning. The designed framework can assist sensor network designers to develop more efficient communication topology, which can better resist environmental obstructions, and also has theoretical and applied values in broad areas such as distributed parameter estimation, dispersed information aggregation and social networks.

A Survey of Blockchain Consensus Protocols

Blockchain consensus protocols have been a focus of attention since the advent of Bitcoin. Although classic distributed consensus algorithms made significant contributions to the development of blockchain consensus protocols, there are still many issues to be resolved due to the complexity and diversity of the blockchain. In this survey, we summarize the state-of-the-art blockchain consensus protocols. We first introduce the theoretical basis, models, and challenges of blockchain consensus protocols. Then, we present the existing blockchain protocols in the categories of proof-based protocols, committee-based protocols, and other miscellaneous protocols. Finally, we analyze their performance and discuss future research directions by comparing existing protocols.

Scaled Consensus of Exponentially Unstable Networked Systems With Time-Varying Input Delay

This work studies the scaled consensus problems of exponentially unstable networked systems with time-varying input delays. First, a distributed consensus algorithm is designed under the truncated predictor feedback technique. Then, a delay bound is evaluated by utilizing the Lyapunov function-based method. Moreover, the consensus can be guaranteed when the upper bound of the time-varying input delay is no more than the delay bound. Furthermore, the relation between the delay bound and the network synchronizability is established. In other words, better network synchronizability implies a bigger delay bound. Finally, in the simulation examples, the feasibility of the theoretical results is verified, and the actual delay bounds are evaluated.

Deception attacks on event-triggered distributed consensus estimation for nonlinear systems

This paper considers the security problem of distributed consensus estimation for nonlinear systems subject to deception attacks with enough/limited energy. Depending on whether the communication resources of the attacker are limited, two different attack strategies are introduced under the distributed consensus estimation framework. Then an event-triggered distributed consensus extended Kalman filter is devised for nonlinear systems, where the upper bound for the estimation error covariance matrix is derived in terms of the variance-constrained method. The stability analysis is executed to establish sufficient conditions that guarantee the boundedness of the estimation error. Finally, the results of a numerical simulation are analyzed to show the estimation performances of the proposed distributed consensus algorithm.

Multiagent-Based Consensus Optimal Control for Microgrid With External Disturbances via Zero-Sum Game Strategy

Due to the fact that the external disturbances are unavoidable in nonlinear microgrids (MGs) systems, which could induce the oscillate of controller, leading to the excessive line loss, and the nonlinear could also result in the controller design difficulty in MGs. Therefore, based on the distributed consensus algorithm of multi-agent systems, this paper studies the distributed voltage recovery consensus optimal control problem for the MGs with nonlinear item and external disturbances. Firstly, the distributed secondary voltage restoration consensus control problem can be transformed into a distributed zero-sum (ZS) differential game problem of the voltage restoration consensus tracking error dynamics via applying the adaptive command-filtered backstepping technique, in which the auxiliary system and neural networks (NNs) are introduced to solve the unknown nonlinearities and external disturbances. Subsequently, the distributed voltage restoration consensus optimal control protocol is constructed via ZS differential game algorithm to achieve the voltage recovery of island MGs, in which a critic network is proposed to estimate the associated cooperative cost function online with a designed weight update law. Further, the consensus optimal control protocol proposed not only ensures the cooperative cost function to be minimized, but also ensures in the closed-loop systems all signals to be cooperatively uniformly ultimately bounded (CUUB). In addition, based on the Lyapunov stability theory, it is proved that the voltage recovery synchronization error can converge to an arbitrarily small range near the origin. Finally, simulation results verify the effectiveness of the proposed controller in island MG.

Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.

Sparsity promoting decentralized learning strategies for radio tomographic imaging using consensus based ADMM approach

Radio tomographic imaging (RTI) has wide applications in the detection and tracking of objects that do not require any sensor to be attached to the object. Consequently, it leads to device-free localization (DFL). RTI uses received signal strength (RSS) at different sensor nodes for imaging purposes. The attenuation maps, known as spatial loss fields (SLFs), measure the power loss at each pixel in the wireless sensor network (WSN) of interest. These SLFs help us to detect obstacles and aid in the imaging of objects. The centralized RTI system requires the information of all sensor nodes available at the fusion centre (FC), which in turn increases the communication overhead. Furthermore, the failure of links may lead to improper imaging in the RTI system. Hence, a distributed approach for the RTI system resolves such problems. In this paper, a consensus-based distributed strategy is used for distributed estimation of the SLF. The major contribution of this work is to propose a fully decentralized RTI system by using a consensus-based alternating direction method of multipliers (ADMM) algorithm to alleviate the practical issues with centralized and distributed incremental strategies. We proposed distributed consensus ADMM (DCADMM-RTI) and distributed sparse consensus ADMM (DSCADMM-RTI) for the RTI system to properly localize targets in a distributed fashion. Furthermore, the effect of quantization noise is verified by using the distributed consensus algorithms while sharing the quantized data among the neighbourhoods.