Consensus Strategy Research Articles

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%.

Novel Reference Trajectory-Based Finite-Time Consensus Protocols for Multiagent Systems With Non-Identical Nonlinear Dynamics

This paper investigates the finite-time consensus for second-order multiagent systems under directed networks, where each agent is described by a non-identical nonlinear system. By using the reference trajectory-based method, two novel protocols are developed for the leader-follower and leaderless cases, respectively. It is noted that the dynamical evolutions of each agent's state and the reference trajectory are coupled, and the information of reference trajectories is not allowed to transmit in the communication channels. By the developed protocols, the relative state errors among agents can converge to the origin (instead of a neighborhood of the origin) within a finite time, though there are unknown parameters in agents' dynamics. The nonlinear terms in agents' dynamics are not required to be bounded by known constants or functions, which invalidates many consensus schemes based on the sliding mode control. Using the graph theory, Lyapunov functional method and finite-time stability theory, sufficient conditions are established for the finite-time consensus of considered multiagent systems. Finally, simulation examples are given to validate the effectivity of the proposed protocols.

Optimized DPoS consensus strategy: Credit-weighted comprehensive election

Consensus algorithm, which allows distrust nodes to reach a consensus in the overall state of the blockchain, is one of the basic characteristics of the blockchain network. Since the delegate nodes elected in the DPoS consensus algorithm are always nodes with a large amount of currency, it usually leads to low voting activity of other nodes. To solve this problem, we propose a credit-weighted comprehensive election, codenamed as CW-DPoS. We propose that the voting weight of nodes is positively correlated with the credit value. First calculate the activity of the node based on k-shell,second calculate the weighted voting value of the node. Finally, the delegator is selected through the comprehensive method of node activity and weighted votes. The performance of the proposed method is compared with the existing consensus mechanism of DPoS, and the experimental result shows that the activity of the nodes has been improved to about 84 % and selects honest nodes more fairly.

UN BBNJ 해양유전자원 주요 법적 쟁점 연구

The 5-1 UN BBNJ Intergovernmental Conference (‘IGC’) was adjourned against international community’s huge expectation for reaching consensus on the BBNJ agreement in 2022. However, the 5-1 IGC has been considered the IGC which substantially narrowed down the divergence between Parties. Against this backdrop, there are high hopes for adopting UN BBNJ in the coming IGC 5-2 .
 However, there are still the unsolved issues which require significant amount of discussions. Out of many issues, there are two major legal issues which the IGC 5-2 should resolve in the area of Marine Genetic Resources(‘MGR’). Since the beginning of the BBNJ agreement negotiation, the legal status of MGR has divided the delegations into two groups. One is those who are proponents for common heritage of mankind. This group believes the principle of common heritage of mankind applies to MGR. The other is those who are against this. There have been no consensus at all so far. Without any creative ideas to resolve the divergence between two, there would be a very small window to see the adoption of the UN BBNJ agreement. Even if the IGC 5-2 succeeds in the legal status of MGR, we still have the digital sequence information issue. At issue is whether digital sequence information falls under the definition of MGR. Considering the same discussions in Convention on Biological Diversity Conference of Parties(‘CBD COP’), there is very slim chance to arrive in consensus on this issue, too.
 The best scenario would be the case where all the legal issues are cleared and Parties reach consensus. However, this scenario seems very unlikely to be realized at the IGC 5-2. Considering the difficulties ahead, the IGC 5 should carefully look into the approach taken at the CBD COP 15 for successful adoption of Kunming-Montreal Global Biodiversity. The lessons learned the IGC 5-2 should take are the effective conference management and targeted consensus strategy. The CBD COP 15 managed conference effectively by forming Plenary-Working Group-Contact Group-Friends of the Chair structures. With this structure, the CBD COP could filer out the agenda and focus its time and resources on hard issues. In addition, the CBD COP15 draw consensus only where it is likely to reach. This targeted approach produced the result that the CBD COP 15 left many issues unsolved till the COP 16. However, the CBD COP 15 achieved what the CBD COP 15 had to on time.
 Applying the lessons learned at CBD COP 15, the IGC 5-2 should enhance the small group which was introduce for the first time at the IGC 5-1 for consensus between Parties who have opposing views on one issue. There has been not enough time for narrowing divergent views between Parties during the whole IGCs. What the IGC 5-2 might try next is to explicitly agree not to agree. By creating provisions which state Parties do not agree in the issues such as the legal status of MGR and the digital sequence information, Parties could clean the brackets and produce a clean text. When debates seem endless, we should move toward a new direction instead of circling around.

Distributed consensus-based multi-agent temporal-difference learning

In this paper we propose two new distributed consensus-based algorithms for temporal-difference learning in multi-agent Markov decision processes. The algorithms are of off-policy type and are aimed at linear approximation of the value function. Restricting agents’ observations to local data and communications to their small neighborhoods, the algorithms consist of: (a) local updates of the parameter estimates based on either the standard TD(λ) or the emphatic ETD(λ) algorithm, and (b) dynamic consensus scheme implemented over a time-varying lossy communication network. The algorithms are completely decentralized, allowing efficient parallelization and applications where the agents may have different behavior policies and different initial state distributions while evaluating a common target policy. It is proved under nonrestrictive assumptions that the proposed algorithms weakly converge to the solutions of the mean ordinary differential equation (ODE) common for all the agents. It is also proved that the whole system may be stabilized by a proper choice of the network and that the parameter estimates weakly converge to consensus. Discussion is given on the asymptotic bias and variance of the estimates, on the projected forms of the proposed algorithms, as well as on restrictiveness of the adopted assumptions. Simulation results illustrate the main properties of the algorithms and provide comparisons with similar schemes.

Event-triggered tracking control of stochastic switched multi-agent systems subjected to input saturation

This paper focuses on the tracking control problem of stochastic nonlinear multi-agent systems subjected to input saturation and directed switching topologies. In addition, the external disturbances are also considered to ensure the system robustness. A dynamic event-triggered consensus scheme based on a switching compensator for every follower agent is proposed via output feedback. By the Lyapunov stability analysis, a sufficient condition for switching signal is obtained such that the tracking errors can reach a small compact set of the origin. Moreover, the proposed event-triggered strategy by fully exploiting the dynamic gain can save resources effectively and avoid the Zeno behavior. Finally, simulation studies confirm the effectiveness of the theoretical results.

Leader-Following Consensus Control for Uncertain Feedforward Stochastic Nonlinear Multiagent Systems.

This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. Output information for all agents, except for state information, can be acquired based on sensor measurement. Moreover, the stochastic disturbances from external unpredictable environments are considered on all agent systems with a feedforward structure. In these conditions, we propose a novel consensus scheme with a simple design procedure. First, for each follower, we construct a dynamic gain-based switched compensator using its output and its neighbor agents' outputs to provide feedback control signals. Then, for each follower, we develop a compensator-based distributed controller that is not directly associated with the topology switching signal such that it has a first derivative and antishake. Thereafter, by means of the Lyapunov stability theory, we verify that the leader-following consensus can be acquired asymptotically in probability under the controllers' action if the topology switching signal fulfills an average dwell time condition. Finally, the feasibility of the control algorithm is checked via numerical simulation.

Fixed-Time Consensus Tracking for Multi-Agent Systems With a Nonholomonic Dynamics

In this article, we investigate the control strategy of fixed-time leader-following consensus for chained-form systems with matched disturbances. A new decentralized observer is designed under a directed topology to estimate the leader state in a fixed time for each follower. Then, a novel control protocol is developed to track the estimated leader state in a prescribed finite time. Different from existing results, the bound we derive for the setting time does not depend on the initial conditions even in the presence of matching perturbations. Moreover, the chattering phenomenon of control output is effectively reduced as signum function gets smaller in the proposed method.

Event-Triggered Consensus LMB Filter for Distributed Multitarget Tracking

This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multitarget tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components (BCs) that achieve enough information gain. Two message transmission strategies, i.e., joint and independent transmission, are devised. The possibility to separately trigger BCs allows the proposed method to better tradeoff tracking performance versus communication load with respect to existing distributed multitarget tracking approaches.

Large-scale group consensus hybrid strategies with three-dimensional clustering optimisation based on normal cloud models

Large-scale group decision-making (LSGDM) is characterised by a large number of experts and a complex consensus reaching process. Clustering is used to divide the large group into a number of manageable subgroups; however, the simultaneous presence of all subgroup members at the negotiation process is rare. Thus, the selection of subgroup representatives for a smooth negotiation is necessary. Few LSGDM consensus recommendation optimisation models truly consider the problems of subgroup representative selection in their strategy to reach a consensus. This article proposes LSGDM consensus hybrid strategy framework with three-dimension clustering optimisation based on normal cloud models (NCMs) whose aims are threefold: (1) the use of NCMs to represent the imprecision of linguistic preferences provided in real complex decision scenarios with a large number of experts; (2) to establish a clustering optimisation method to choose subgroup representatives using three sensible criteria: preference similarity level within the subgroup, preference precision level, and preference consistency level; and (3) to establish two consensus recommendation optimisation strategies for individual negotiation-guided and moderator-guided consensus reaching, respectively. The feasibility and applicability of the proposed method are illustrated via a power curtailment policy assessment example, then some sensitive and comparative analyses are conducted to explicit the effectiveness and advantages of the proposed consensus hybrid strategies.

Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

A group decision-making method that links changes in experts’ preferences and weights: Application to site selection of waste-to-energy plants

The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately.

A comprehensive review of nature-based solutions: current status and future research

<abstract> <p>Climate change and the rapid development of cities have brought considerable challenges to the sustainable development of urban and rural areas, and using nature-based solutions to strengthen ecosystems' resilience and response capacity has become a consensus strategy. Natural solutions are the collective name for all solutions that increase the city's resilience while benefiting the environment and humanity. To deepen the theoretical research and practical development of NBS, I reviewed 87 papers on NBS through the Web of Science database. The study found that NBS-related research mostly focuses on five aspects: Concept of ideas, applied technology, implementation guidelines, performance evaluation and platform building. Currently, the emphasis is predominantly on ideas and platform development in developed countries. While the other three domains were also explored, they primarily adhere to conventional methodologies and content within the NBS context. While NBS research covered many areas and boasts an integrative, collaborative approach, it remained fragmented and lacked a cohesive system. On this basis, I proposed a systematic framework to strengthen the systematicity of the NBS system, give full play to the unique advantages of NBS as a comprehensive concept and promote the specific implementation and development of NBS. I examined NBS's progression and benefits, providing a thorough insight into its significance in sustainable urban development. The research introduced a cohesive framework by elucidating NBS's foundational concepts guiding subsequent inquiries. Such findings are pivotal for facilitating informed strategies and enhancing resilience to climate adversities, underscoring a comprehensive approach to sustainability.</p> </abstract>

A Robust Density Estimation Method for Glacier-Height Retrieval From ICESat-2 Photon-Counting Data

Retrieval of glacier surface heights from ICESat-2 photon-counting data is significant to monitor glacier surface morphologies (e.g., crevasses) and their changes. However, existing methods are susceptible to complex glacier surfaces and diverse signal-to-noise ratios (SNRs). Therefore, we propose a robust density estimation method for glacier-height retrieval from ICESat-2 photon-counting data. A multi-scale random sample consensus (RANSAC) strategy is firstly employed to obtain the best elliptical neighborhood for each photon, to improve the consistency of neighboring photons. Then, a hybrid weighted density estimation method is developed to robustly describe the differences in the spatial distribution patterns between signal and noise photons. High-quality extraction of signal photons is finally implemented using adaptive thresholds in the local along-track segments. To test its performance, four datasets with strong and weak beams from the Jakobshavn Isbræ Glacier, western Greenland, were selected. Results showed that the proposed method achieved excellent performances in glacier-height retrieval in various glacier surface morphologies and diverse SNRs, due to discriminative densities between noise and signal photons were obtained. The average percentages of along-track segments with signal densities larger than noise densities for weak and strong beams were 92.0% and 99.9%. The average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F₁-scores</i> reached 0.927 and 0.968 for the weak and strong beams, even the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F₁-score</i> reached 0.892 for the heavily crevassed complex surface with a low SNR. Moreover, comparison experiments with existing methods demonstrated that the proposed method showed superior performances in density estimation and glacier-height retrieval, and improved more than 17.33% and 16.10% in the most complex surface, respectively.

EU ENVIRONMENTAL LEGISLATION: MITIGATION OF DIRECTIVE ILLOCUTIONARY FORCE

This article discusses the illocutionary underpinning of varying degrees of legal force of an international document, derived from the conflict between its genre and institutional nature as an act of hard law and the sphere of environmental relations associated with soft law. The purpose of the article is to identify the types of hedging and mitigation of directive speech acts in the text of binding international law, and their impact on the degree of legal force of the document. Despite the predominance of directive speech acts that correspond to the obligative deontic modality of the document in accordance with the genre and institutional strategy of mandatory prescriptions, most of the acts are presented in indirect, hedged and mitigated versions with varying degrees of deintensification of the directive illocutionary force. The mitigation of coercion contributes to the discursive strategy of consensus and solidarity in such a sensitive area of international law as environmental legislation. The study revealed that the weakening of the intensity of directive illocutionary force is achieved by employing a number of mitigating and structural devices. The unconditional validity of legal force is reduced through hedging of explicit directives by introducing the parenthetical constructions in the propositional part of the act to refer to the circumstances, i.e. to the varying interests and needs of the states that can be taken into account during the implementation of the document. In addition to hedging, mitigation of explicit directive acts is achieved through bushes – the lexemes with the semes denoting an intention to act or a deliberation process instead of actions themselves that blur the propositional or denotative scope of statements, thus influencing the categoricity of the legal norm and, indirectly, its level of obligatoriness. The next type of directive action modifications affecting the mitigation of the directive illocutionary force is achieved through structural transformations of the directive act, whereby the designation of the states as subjects of the directive action is omitted and the position of the phrasal subject is occupied by the nomination of an object or a prescription purpose in combination with a binding verb. As a result, the utterance loses its performative-illocutionary part, which significantly affects the decrease in the degree of strength of the directive illocutionary force.

Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

Linguistic Knowledge Representation in DPoS Consensus Scheme for Blockchain

Accelerating Distributed Optimization via Fixed-Time Convergent Flows

Distributed optimization has gained significant attention in recent years, primarily fueled by the availability of a large amount of data and privacy-preserving requirements. This paper presents a fixed-time convergent optimization algorithm for solving a potentially non-convex optimization problem using a first-order multi-agent system. Each agent in the network can access only its private objective function, while local information exchange is permitted between the neighbors. The proposed optimization algorithm combines a fixed-time convergent distributed parameter estimation scheme with a fixed-time distributed consensus scheme as its solution methodology. The results are presented under the assumption that the team objective function is strongly convex, as opposed to the common assumptions in the literature requiring each of the local objective functions to be strongly convex. The results extend to the class of possibly non-convex team objective functions satisfying only the Polyak-Lojasiewicz (PL) inequality. It is also shown that the proposed continuous-time scheme, when discretized using Euler's method, leads to consistent discretization.