Consensus Strategy Research Articles

Privacy of Distributed Optimality Schemes in Power Networks

The increasing participation of local generation and controllable demand units within the power network motivates the use of distributed schemes for their control. Simultaneously, it raises two issues; achieving an optimal power allocation among these units, and securing the privacy of the generation/demand profiles. This study considers the problem of designing distributed optimality schemes that preserve the privacy of the generation and controllable demand units within the secondary frequency control timeframe. We propose a consensus scheme that includes the generation/demand profiles within its dynamics, keeping this information private when knowledge of its internal dynamics is not available. However, the prosumption profiles may be inferred using knowledge of its internal model. We resolve this by proposing a privacy-preserving scheme which ensures that the generation/demand cannot be inferred from the communicated signals. For both proposed schemes, we provide analytic stability, optimality and privacy guarantees and show that the secondary frequency control objectives are satisfied. The presented schemes are distributed, locally verifiable and applicable to arbitrary network topologies. Our analytic results are verified with simulations on a 140-bus system, where we demonstrate that the proposed schemes offer enhanced privacy properties, enable an optimal power allocation and preserve the stability of the power network.

Reaching consensus for membership dynamic in secret sharing and its application to cross-chain

The communication efficiency optimization, censorship resilience, and generation of shared randomness are inseparable from the threshold cryptography in the existing Byzantine Fault Tolerant (BFT) consensus. The membership in consensus in a blockchain scenario supports dynamic changes, which effectively prevents the corruption of consensus participants. Especially in cross-chain protocols, the dynamic access to different blockchains will inevitably bring about the demand for member dynamic. Most existing threshold cryptography schemes rely on redefined key shares, leading to a static set of secret sharing participants. In this paper, we propose a general approach to coupling blockchain consensus and dynamic secret sharing. The committee performs consensus confirmation of both dynamic secret sharing and transaction proposals. Our scheme facilitates threshold cryptography membership dynamic, thus underlying support for membership dynamic of threshold cryptography-based BFT consensus schemes. We instantiate a dynamic HotStuff consensus to demonstrate the effectiveness of the scheme. After the correctness and security proof, our scheme achieves the secrecy and integrity of the threshold key shares while ensuring consensus liveness and safety. Experimental results prove that our scheme obtains dynamic membership with negligible overhead.

LightBlocks: A trusted lightweight signcryption and consensus scheme for industrial IoT ecosystems

Industrial Internet-of-Things (IIoT) plays a vital cog in Industry 4.0 design principles and has revolutionized the manufacturing process by shifting from traditional mechanical systems to smart automation. In IIoT, smart embedded sensors are placed strategically on machine parts, components, and devices. These sensors exchange data over open public channels (Internet), which poses high security and privacy risks to the operational processes, and could lead to machinery failure. The problem increases fourfold in distributed systems, where trust and security are crucial factors. Thus, recent schemes have combined blockchain (BC) with crypto-based primitives in IIoT, but less focus is placed on the resource requirements of such systems. Thus, this paper proposes a scheme named LightBlocks that combines lightweight crypto-primitives with scalable BC operations and presents a low-powered consensus approach, which is appropriate for IIoT ecosystems. The scheme allows the local sensor nodes (LSN) to identify a cluster head (CH), which forwards data to gateway nodes (GN). At GN, a low-powered signcryption scheme is proposed for sensor data. Once the data is signed and energy-efficient consensus Proof-of-Light (PoL) is proposed, block validations are done through selected verifier nodes in a defined consensus period only. Due to this, the transactional throughput improves significantly, and mining overheads are also reduced. We present the attacker model and propose a formal security model to evaluate the security strength of the scheme. The simulation compares the scheme against traditional IIoT frameworks for parameters like mining latency, node commits, signing latency, and energy dissipation. The improvements indicate the resilience of the scheme for practical IIoT setups.

Assessing treatment adherence is crucial to determine adequacy of mineralocorticoid therapy.

There is no consensus strategy for mineralocorticoid (MC) therapy titration in patients with primary adrenal insufficiency (PAI). We aim to measure serum fludrocortisone (sFC) and urine fludrocortisone (uFC) levels and to determine their utility, alongside clinical/biochemical variables and treatment adherence to guide MC replacement dose titration. Multi-centre, observational, cross-sectional study on 41 patients with PAI on MC replacement therapy. sFC and uFC levels (measured by liquid chromatography-tandem mass spectrometry), plasma renin concentration (PRC), electrolytes (Na+, K+), mean arterial blood pressure (MAP), total daily glucocorticoid (dGC) and MC (dMC) dose, and assessment of treatment adherence were incorporated into statistical models. We observed a close relationship between sFC and uFC (r = 0.434, P = 0.005) and between sFC and the time from the last fludrocortisone dose (r = -0.355, P = 0.023). Total dMC dose was related to dGC dose (r = 0.556, P < 0.001), K+ (r = -0.388, P = 0.013) as well as sFC (r = 0.356, P = 0.022) and uFC (r = 0.531, P < 0.001). PRC was related to Na+ (r = 0.517, P < 0.001) and MAP (r = -0.427, P = 0.006), but not to MC dose, sFC or uFC. Regression analyses did not support a role for sFC, uFC or PRC measurements and confirmed K+ (B = -44.593, P = 0.005) as the most important variable to guide dMC titration. Of the patients, 32% were non-adherent with replacement therapy. When adherence was inserted into the regression model, it was the only factor affecting dMC. sFC and uFC levels are not helpful in guiding dMC titration. Treatment adherence impacts on clinical variables used to assess MC replacement and should be included as part of routine care in patients with PAI.

Secure and efficient blockchain-based consensus scheme for MWSNs with clustered architecture

Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.

Robust information filtering for distributed networks using mixed Gaussian distribution

This paper proposes a robust distributed state estimation algorithm for sensor networks. In a distributed sensor network, sensor nodes may be affected by outliers and thus influence the information fusion results. In this respect, the measurement likelihood and one-step state prediction are modeled by a mixture of Gaussian distributions. In this Gaussian mixture, one Gaussian uses nominal covariance, while the other uses adaptive covariance with larger values and lower probabilities. We build a centralized multi-sensor fusion model in the variational Bayesian framework based on the aforementioned mixture of Gaussian distributions. Then, a consensus strategy is employed to convert the centralized method into a distributed one. Each node completes estimation tasks separately based on the information from itself and nearby nodes. In addition, the weight of sensor information will adaptively update according to the Gaussian distribution that mainly affects the current sensor measurement noise. Finally, simulation experiments demonstrate the effectiveness of the proposed method in handling outlier effects.

Distributed and Secure Federated Learning for Wireless Computing Power Networks

The explosively growth of mobile applications imposes much burden on the current computing networks. Wireless Computing Power Network (WCPN), as an emerging computing architecture, can sense and coordinate computing resources through agile wireless communications, and realize distributed intelligence based on federated learning. However, the mobility and heterogeneity of WCPN nodes typically impact the security (e.g., malicious node disturbance) and efficiency of federated learning in WCPN. In light of this, this paper proposes a provable secure and decentralized federated learning based on blockchain for WCPN, where nodes can freely participate or leave the WCPN federated training without authorization and security threats. Particularly, we design a blockchain with proof-of-accuracy (PoAcc) consensus scheme to deeply integrate with the federated learning procedure, in which high-accuracy local models have the priority of aggregation, thus accelerating the convergence of federated learning and improving the efficiency of WCPN. The proposed PoAcc is proved to be secure as long as the ratio of honest to malicious nodes is above a lower bound. To further meet the security requirement of PoAcc, we then propose an evolutionary game-based incentive scheme that incentivizes honest nodes to participate the WCPN federated learning under malicious node disturbance. Numerical results show that the proposed scheme ensures the consistency and security of federated learning in WCPN, while outperforming the benchmarks in terms of model accuracy and resource consumption.

Information Fusion over Network Dynamics with Unknown Correlations: An Overview

Survey/review study Information Fusion over Network Dynamics with Unknown Correlations: An Overview Wangyan Li 1, and Fuwen Yang 2,* 1 College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China 2 Griffth School of Engineering, Griffth University, Gold Coast Campus, QLD 4222, Australia * Correspondence: fuwen.yang@griffth.edu.au Received: 24 October 2022 Accepted: 22 November 2022 Published: 23 June 2023 Abstract: Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source information fusion problems, and thereby, their corresponding fusion problems have become one of the most important topics in information fusion domain. During the past three decades, the research on this topic has been growing rapidly and extensively, and, as a result, various important advances have been reported. In this overview, we intend to summarize the culmination of years of development in the field of information fusion under UCs as a roadmap. First, the potential reasons leading to UCs are investigated. According to the unknown nature of correlations, we further divide UCs into two categories, i.e., fully UCs, and partially UCs. For each category, the corresponding fusion methods are reviewed. Next, this roadmap witnesses the recent development of information fusion under UCs in a distributed way thanks to the popularity of distributed sensing technology. In particular, the distributed fusion techniques based on consensus, diffusion, and multi-object tracking strategies for UCs are examined. Finally, some future perspectives on information fusion under UCs are pointed out.

Consensus Strategies for a Hegselmann–Krause Model with Leadership and Time Variable Time Delay

Consensus Strategies for a Hegselmann–Krause Model with Leadership and Time Variable Time Delay

A distributed event‐triggered dynamic average consensus scheme with a time‐varying threshold and its application in DC microgrid

AbstractWe propose a distributed event‐triggered dynamic average consensus (DAC) scheme with a time‐varying threshold for a multi‐agent system (MAS) in a directed graph. In this scheme, each agent is not required to monitor its neighbors in real time, and a threshold is chosen related to consensus performance directly in an MAS, where the threshold is adjusted adaptively according to average consensus performance. Also, we introduce a bounded and monotonically increasing function in a time‐varying threshold, and this function is for guaranteeing the value of a threshold to locate in an appropriate range. Stability analysis shows that the tracking error is eventually bounded and the MAS achieves DAC without Zeno behaviors. Furthermore, the proposed event‐triggered DAC scheme is applied to voltage restoration and adjustable current sharing in DC microgrid. The effectiveness of the proposed scheme is verified by two examples.

Batch-Based Learning Consensus of Multiagent Systems With Faded Neighborhood Information.

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate how to ensure the precise consensus tracking to a given reference leader using heavily contaminated information. To this end, a novel distributed learning consensus scheme is proposed, which consists of a classic distributed control structure, a preliminary correction mechanism, and a separated design of learning gain and regulation matrix. The influence of biased and unbiased randomness is discussed in detail according to the convergence rate and consensus performance. The iterationwise asymptotic consensus tracking is strictly established for linear MAS first to demonstrate the inherent principles for the effectiveness of the proposed scheme. Then, the results are extended to nonlinear systems with nonidentical initialization condition and diverse gain design. The obtained results show that the distributed learning consensus scheme can achieve high-precision tracking performance for an MAS under unreliable communications. The theoretical results are verified by two illustrative simulations.

THE EXTENT OF STRATEGIC LEADERSHIP, ORGANIZATIONAL COMMITMENT, ORGANIZATIONAL CULTURE, CONSENSUS STRATEGY AND ACHIEVEMENTS OF DEVELOPMENT STRATEGY PLANS AT GOVERNMENT INSTITUTIONS IN TIMOR LESTE

The Strategic Plan for Development in Timor Leste is one such documentlong-term development strategy plans for various prioritized sectorseveryfive years, and for the preparation of the state budget every year. Onthe initial implementation of the development strategy plan all government agencies refer toon the document, but because of the change of government, and there is no consensusat the political level, it has an impact on conflicting party-political interestspower, and considers that the 2011-2030 PED document does not reflectThe National Development Strategy Plan is good, and therefore it was not usedas a reference for planning the budget and costs of the state of Timor Leste. Strategic management studies that determine the success or failure of a strategic plandevelopment in its implementation is inseparable from several determining factors namelytop leadership in government agencies, it is necessary to establish organizational commitment,the creation of an organizational culture and one of the important factors is the existence of a commonalityunderstanding of the development strategy plan is the presence of strategic consensusso as to ensure the implementation of development strategy plans in various sectorsin accordance with the functions of existing government agencies in Timor Leste. Strategy implementationis part of the management strategy which also includes strategy formulation andplan control. Strategic management is seen as a set of decisions andactions designed to achieve an organizations mission, vision, strategy, and strategic goals.Strategy implementationseen as an important part of strategic management because it is seen asthe process that transforms the formulated strategy into actions that ensure the visionand the mission of the organization is achieved as planned.The researchers focused on the Extent of Strategic Leadership,Organizational Commitment, Organizational Culture, and Consensus Strategy AgainstAchievements of the Development Strategy Plan for Government Agencies in Timor Leste.

Federated transfer learning in fault diagnosis under data privacy with target self-adaptation

The past decades have witnessed great developments and applications of the data-driven machinery fault diagnosis methods. Due to the difficulties and significant expenses in collecting labeled data, multiple industrial users generally would like to collaborate for training powerful models. However, for data privacy concerns, the local data generally cannot be shared with others for centralized training. Moreover, the data at different users are usually obtained under different conditions. The model generalization problem is quite challenging since no simultaneous data access is permitted. To address these issues, a federated transfer learning method is proposed. Fake data of additional classes are generated to suppress the decision boundaries, and only the models are communicated across different clients. A prediction alignment scheme is proposed at the target client for self-adaptation. A model and instance-level consensus scheme is introduced to enhance model performance. The proposed method achieves promising knowledge transfer effects with only communications of models, and the challenging partial transfer learning problems can be also well addressed. Experiments on real-world machinery datasets validate the proposed method, which is promising for applications in the real industries.

Active disturbance rejection‐based event‐triggered bipartite consensus control for nonaffine nonlinear multiagent systems

AbstractThis article investigates the active disturbance rejection‐based distributed event‐triggered bipartite consensus problem of nonaffine nonlinear multiagent systems with input saturation. To reduce the update frequency of the control signal, an event‐triggered mechanism is employed for each follower. Additionally, the active disturbance rejection technology, as a combination of the extended state observer and the tracking differentiator, is introduced to estimate the uncertainties of the system and address the problem of explosion of complexity in the backstepping design process. Compared with the traditional distributed bipartite consensus schemes using neural networks/fuzzy logic systems, the design complexity of the control law is reduced in the proposed scheme. Meanwhile, the tracking error is maintained within a predefined range via the funnel function. Finally, the stability of the system is proved under the Lyapunov stability analysis theory, and the effectiveness of the proposed strategy is verified via simulation examples.

A graph neural network based deep reinforcement learning algorithm for multi-agent leader-follower flocking

Flocking control is one of the important topics in multi-agent system (MAS), and has great value in both military and civilian applications. At present, the slow cluster consensus and poor control strategy in the stochastic environment are still serious problems faced by the flocking control algorithms. Focusing on the problems, we propose a leader-follower flocking algorithm based on a novel reinforcement learning (RL) model. We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking control system. In HGNN-MADDPG, we design a GNN with loss weight to complete the feature extraction of the structural information and improve the perception ability of agents to the system structure. Furthermore, in order to improve training efficiency, we also propose a new cooperative reinforcement learning architecture in which the network and experience between agents are shared. Simulation results show that the HGNN-MADDPG has a better learning effect and faster convergence than the traditional RL algorithms. In addition, the comparative experiments demonstrate the leader-follower flocking algorithm based on HGNN-MADDPG has a faster cluster consensus and more stable control than the traditional and other RL-based leader-follower algorithms in the stochastic environment.

Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

AbstractThis paper focuses on the formation encirclement problem of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy. A target encirclement control algorithm is designed to achieve an enclosed formation, which drives autonomous quadrotors around the target so the target can't get away. Afterward, we propose a decentralized consensus‐based control protocol to realize the encirclement of the target quadrotor, where onboard miniature computers carry out all necessary computations. Notably, the quadrotor is modeled by a double integrator and the switching topology is described by a signed graph subjected to the semi‐Markov jump process. Whereafter, some criteria in the form of linear matrix inequalities are constructed based on the Lyapunov stability theory to achieve encirclement behavior. Ultimately, the proposed approach is verified by a comparison simulation and two flight experiments with three follower quadrotors enclosing a leader quadrotor.

Consensus-based distributed moving horizon estimation with constraints

This paper concerns distributed state estimation for linear systems over a sensor network. Two novel algorithms utilizing the framework of moving horizon estimation (MHE) are proposed, which are fully distributed, scalable and allow for taking into account constraints on the noises and the system states. The proposed methods estimate the state by minimizing a local quadratic objective function, which can be efficiently solved by quadratic programming (QP). Consensus technique is employed to fuse information to construct the local quadratic objective function for each node. The first algorithm, which minimizes a quadratic function involving consensus on measurement costs (CM), approaches the centralized MHE with a sufficiently large number of consensus steps. The second one involving consensus both on arrival costs (CA) and measurement costs, enjoys the benefits of both the CA and CM. To avoid directly running consensus on some functions, a novel consensus strategy for the CM is developed. The estimation errors of the proposed methods are proven to be stochastically ultimately bounded under certain conditions. Finally, numerical results are presented to verify the effectiveness of the developed algorithms.

Reputation based proof of cooperation: an efficient and scalable consensus algorithm for supply chain applications

The growing interest in blockchain technology has gained a lot of attention in Supply Chain Management (SCM) and sparked the quest for decentralized, scalable, efficient and trustworthy consensus schemes. Traditional blockchains rely on computationally expensive consensus mechanisms with low throughput and high latency. This paper conducts a performance evaluation of several existing consensus protocols to illustrate blockchain’s shortcomings in terms of consensus and propose a new consensus algorithm: Reputation based proof of cooperation (RPoC). The RPoC algorithm uses a layered architecture to segment the nodes that participate in the consensus phase in order to improve scalability and efficiency while maintaining trust among peers. The layered design addresses the issues of flexibility and scalability and breaks down the extensive mining process into segments. Rather than choosing a few nodes for mining, the proposed consensus process involves all network nodes, making it more efficient, decentralized and scalable. Through extensive theoretical analysis and experimentation, the suitability of the proposed algorithm is established in terms of scalability and efficiency.

Collaborative search and rescue based on swarm of H-MASSs using consensus theory

Swarm of marine autonomous surface ships (MASSs) constitutes the most critical part in maritime search and rescue (MSR). A collaborative consensus strategy for the swarm of heterogeneous MASSs (H-MASSs) is proposed, which realizes large-scale continuous and efficient search after marine incidents. (1). The MSR area is allocated according to the search and rescue capability (SRC) of H-MASSs based on the drift model; (2). Based on the search and rescue progress rate (SRPR), a consensus controller is designed to make the swarm of H-MASSs collaborate to complete the task synchronously; (3). Line-of-Sight method is improved to ensure stable path tracking of swarm. The experimental verification and efficiency comparison shows that the collaborative consensus strategy improves the efficiency by 42% compared with the strategy without considering the SRC, and is 22% higher than the decentralized control strategy, which has strong efficiency and stability. It can provide a theoretical basis for the large-scale cooperative operation of the swarm of H-MASSs in the future, and gradually realize the autonomous navigation of the swarm of H-MASSs formation in the whole sea area with the development of ship networking technology.

FedG2L: a privacy-preserving federated learning scheme base on “G2L” against poisoning attack

Federated learning (FL) can push the limitation of “Data Island” while protecting data privacy has been a broad concern. However, the centralised FL is vulnerable to a single-point failure. While decentralised and tamper-proof blockchains can cope with the above issues, it is difficult to find a benign benchmark gradient and eliminate the poisoning attack in the later stage of global model aggregation. To address the above problems, we present a global to local based privacy-preserving federated consensus scheme against poisoning attacks (FedG2L). This scheme can effectively reduce the influence of poisoning attacks on model accuracy. In the global aggregation stage, a gradient-similarity-based secure consensus algorithm (SecPBFT) is designed to eliminate malicious gradients. During this procedure, the gradient of the data owner will not be leaked. Then, we propose an improved ACGAN algorithm to generate local data to further update the model without poisoning attacks. Finally, we theoretically prove the security and correctness of our scheme. Experimental results demonstrated that the model accuracy is improved by at least 55% than no defense scheme, and the attack success rate is reduced by more than 60%.