Consensus Operator Research Articles

Reconfigurable and Privacy-Preserving Economical Operation for Interconnected DC Microgrid Clusters

This article presents a new distributed and reconfigurable strategy for global economical consensus operation of multiple, interconnected dc microgrid clusters in a leader–follower graph architecture. The generation costs associated with the distributed energy resources are optimized globally by observer-based controllers in a multilayered hierarchical control structure, equipped with privacy-preserving feature. Self-rearranging and event-based reconfiguration of graphs are also incorporated for obtaining feasible solution to the economic dispatch problem. This is an important aspect of the proposed work as, unlike previously reported research works, it equally prioritizes both voltage regulation and optimal operation. Moreover, continuity of supply to critical loads is a challenging criterion in autonomous systems in which the pinning or leader node plays a pivotal role. Therefore, the multifold benefits of choosing the highest generation unit as the leader in improving the power quality is highlighted for the first time, which remained undiscovered in previous works in literature. The feasibility of the proposed scheme is tested by time-domain simulations in MATLAB/Simulink. Moreover, the results obtained from the controller hardware in loop environment using a real-time digital simulator and field-programmable-gate-array-based WAVECT controller demonstrate the economical operation of the clusters with renewable energy sources.

Using Blockchain to Achieve Decentralized Privacy in IoT Healthcare

With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for ehealth applications that does not require trust in a third party and provides an efficient privacypreserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resourcesconstraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol.

DBFT: A Byzantine Fault Tolerance Protocol With Graceful Performance Degradation

Byzantine Fault Tolerant (BFT) state machine replication protocols are used to achieve agreement among replicated servers with arbitrary faults. Most existing BFT protocols perform well in fault-free cases, but usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes graceful performance degradation in faulty cases. The major novelty of DBFT lies in the double-response mechanism, which lets replica nodes deterministically respond to clients twice: one is after the speculative execution phase and the other is after the commitment phase. The double-response mechanism ensures good performance in spite of inconsistency in speculative execution. Also, to further alleviates undetectable performance attacks by a smartly malicious primary, we change primary upon every outstanding request. Moreover, DBFT does not involve clients in critical consensus operations so as to reduce the load of clients. We prove the correctness properties, i.e., safety and liveness, of DBFT. We conduct extensive experiments and the results show that, DBFT outperforms similar BFT protocols obviously in normal cases.

How to Secure Distributed Filters Under Sensor Attacks

In this article, we study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results.

Representing voting rules in Łukasiewicz’s three-valued logic

We show how voting rules like the simple and the absolute majority rules, unanimity, consensus, etc. can be represented as logical operators in Łukasiewicz’s three-valued logic. First, we prove that the binary simple majority operator can be extended to express the majority choice of groups formed of n voters. Consequently, decisions in larger groups can be represented as more complex decisions in groups formed of pairs of voters. This property is not shared, however, by the consensus and the absolute majority operators. Secondly, we prove that the simple majority operator can be defined in terms of the consensus operator, but that the converse does not hold. Finally, we construct the classes of logical operators definable in terms of the simple majority and of the consensus and point to some more general implications of these results.

Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs

On 2 May 2014, a large-scale landslide in Abe Barek, Badakhshan, Afghanistan, produced extensive damage to the buildings and killed hundreds of people. Evaluations of the extent and the volume of the displaced materials are vital for post-disaster management activities. In this study, we present the applicability of a nonlinear geometric correction technique for decreasing the undesired registration errors between pre- and post-event digital elevation models (DEMs) generated from high-resolution stereo pair satellite imagery, identifying landslide affected areas, and quantifying the landslide volume from DEMs of difference (DoD) analysis. The nonlinear mapping method consists of shifting vector generation in subareas of the DEMs, consensus operations, and interpolation of the shifting vectors. The quality assessment confirmed that the method outperformed the simple DoD technique by eliminating a large-scale of geometric errors in an unaffected area. We estimated the volume of the landslide as 1.05 × 106 m3 from the DoD corrected by the nonlinear method, and discussed the relationship between the area and volume compared to those of the previous studies.

MMCo-Clus – An Evolutionary Co-clustering Algorithm for Gene Selection

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Curse of dimensionality</i> . To overcome this problem, the dimensionality reduction through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature selection</i> becomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> ulti-objective optimization based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</b> ulti-view <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>-Clus</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> tering algorithm (named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MMCo-</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Clus</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> ). A popular evolutionary technique – Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clus</i> algorithm considering the constructed views to identify a set of “good” co-clustering solutions. Finally, based on a concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consensus operation</i> on the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed method's potency.

An Event-Driven Resilient Control Strategy for DC Microgrids

Though recent advancements in dc microgrids are largely based on distributed control strategies to enhance reliability, their susceptibility to cyber attacks still remains a challenging issue. Additionally in converter-dominated dc microgrids, mitigation of cyber attacks upon detection in a timely manner is the need of the hour to prevent the system from immediate shutdown. Since most of the existing research is primarily focused on the detection of cyber attacks in dc microgrids without giving prior attention to comprehensive steps of mitigation, this article classifies cyber attacks as events and introduces an event-driven cyber attack resilient strategy for dc microgrids, which immediately replaces the attacked signal with a trusted event-driven signal constructed using True transmitted measurements. This mechanism not only disengages the attack element from the control system, but also replaces it with an event-triggered estimated value to encompass normal consensus operation during both steady state as well as transient conditions even in the presence of attacks. Finally, the event detection criteria and its sensitivity are theoretically verified and validated using simulation and experimental conditions in the presence of both stealth voltage and current attacks.

Rules of Expansion: an Updated Consensus Operator Site for the CopR-CopY Family of Bacterial Copper Exporter System Repressors.

Copper is broadly toxic to bacteria. As such, bacteria have evolved specialized copper export systems (cop operons) often consisting of a DNA-binding/copper-responsive regulator (which can be a repressor or activator), a copper chaperone, and a copper exporter. For those bacteria using DNA-binding copper repressors, few studies have examined the regulation of this operon regarding the operator DNA sequence needed for repressor binding. In Streptococcus pneumoniae (the pneumococcus), CopY is the copper repressor for the cop operon. Previously, homologs of pneumococcal CopY have been characterized to bind a 10-base consensus sequence T/GACANNTGTA known as the cop box. Using this motif, we sought to determine whether genes outside the cop operon are also regulated by the CopY repressor, which was previously shown in Lactococcus lactis We found that S. pneumoniae CopY did not bind to cop operators upstream of these candidate genes in vitro During this process, we found that the cop box sequence is necessary but not sufficient for CopY binding. Here, we propose an updated operator sequence for the S. pneumoniaecop operon to be ATTGACAAATGTAGAT binding CopY with a dissociation constant (Kd ) of ∼28 nM. We demonstrate strong cross-species interaction between some CopY proteins and CopY operators, suggesting strong evolutionary conservation. Taken together with our binding studies and bioinformatics data, we propose the consensus operator RNYKACANNYGTMRNY for the bacterial CopR-CopY copper repressor homologs.IMPORTANCE Many Gram-positive bacteria respond to copper stress by upregulating a copper export system controlled by a copper-sensitive repressor, CopR-CopY. The previous operator sequence for this family of proteins had been identified as TACANNTGTA. Here, using several recombinant proteins and mutations in various DNA fragments, we define those 10 bases as necessary but not sufficient for binding and in doing so, refine the cop operon operator to the 16-base sequence RNYKACANNTGTMRNY. Due to the sheer number of repressors that have been said to bind to the original 10 bases, including many antibiotic resistance repressors such as BlaI and MecI, we feel that this study highlights the need to reexamine many of these sites of the past and use added stringency for verifying operators in the future.

Distributed Estimation Framework for Beyond 5G Intelligent Vehicular Networks

Intelligent vehicular networks (IVNs) have drawn substantial interests in recent years due to its great potential in enabling diverse applications in the fifth-generation (5G) and beyond communication systems. In IVNs, vehicles are equipped with multi-functional advanced wireless sensors which are capable to collect real-time and practical environmental information. In this paper, we first provide an overview of the existing researches on IVNs for beyond 5G (B5G) communications, while emphasizing the requirements and technical approaches. To fully unleash the potential of vehicular intelligence, smart vehicles should acquire the values of some important variables of interest, e.g. traffic volume in the network. Thus, we introduce a generalized framework which formulates the acquisition of desired variables as a joint estimation and detection problem. Our framework adopts factor graph to solve problems in IVNs. This is done by collecting the observations from vehicles at road side units (RSUs) for inferring such variables and sending them back to vehicles. Nevertheless, this centralized framework critically depends on the functional reliability of the RSUs. To this end, we propose a distributed estimation framework to improve the scalability and robustness, in which vehicles can communicate wirelessly with other vehicles within the communication range. Then, we introduce different consensus operations as a realization of this proposed framework and briefly compare them in terms of implementation feasibility and convergence behavior. Three approximation schemes are further considered for reducing the required communication signaling overhead. To shed light on the proposed distributed estimation framework, we focus on two cases, i.e., target tracking and network decoding in IVNs. Through simulations, we show that the distributed algorithms can efficiently track the target and decode the broadcasted messages, while achieving the same performance of the centralized schemes. Finally, important conclusions are drawn and some challenges and open problems in this research area are outlined.

Blockchain characteristics and consensus in modern business processes

Abstract Blockchain technology has attracted a great deal of attentions as an effective way to innovate business processes. It has to be integrated with other Business Process Management system (BPM) components to implement specified functionalities related to the applications. The current efforts in integrating this technology into BPM are at a very early stage. To apply Blockchain into business processes efficiently, Blockchain and business process characteristics must be identified. Inconsistency of confirmation settlement that heavily relies on the implementation of consensus protocol poses a major challenge in business process operations, especially ones that are time-critical. In addition, validators, nodes responsible for performing consensus operations in a Blockchain system, can introduce bias and as a result are not trustable. This paper first defines Blockchain and also investigates the characteristics of Blockchain and business processes. Then, we suggest an architecture of business processes in Blockchain era to overcome the problems of time inconsistency and consensus bias. The architecture provides persistency, validity, auditability, and disintermediary that Blockchain offers. The architecture also provides flexibility by allowing business partner to select nodes in performing consensus; thus bias is mitigated.

Computer Vision Evidence Supporting Craniometric Alignment of Rat Brain Atlases to Streamline Expert-Guided, First-Order Migration of Hypothalamic Spatial Datasets Related to Behavioral Control.

The rat has arguably the most widely studied brain among all animals, with numerous reference atlases for rat brain having been published since 1946. For example, many neuroscientists have used the atlases of Paxinos and Watson (PW, first published in 1982) or Swanson (S, first published in 1992) as guides to probe or map specific rat brain structures and their connections. Despite nearly three decades of contemporaneous publication, no independent attempt has been made to establish a basic framework that allows data mapped in PW to be placed in register with S, or vice versa. Such data migration would allow scientists to accurately contextualize neuroanatomical data mapped exclusively in only one atlas with data mapped in the other. Here, we provide a tool that allows levels from any of the seven published editions of atlases comprising three distinct PW reference spaces to be aligned to atlas levels from any of the four published editions representing S reference space. This alignment is based on registration of the anteroposterior stereotaxic coordinate (z) measured from the skull landmark, Bregma (β). Atlas level alignments performed along the z axis using one-dimensional Cleveland dot plots were in general agreement with alignments obtained independently using a custom-made computer vision application that utilized the scale-invariant feature transform (SIFT) and Random Sample Consensus (RANSAC) operation to compare regions of interest in photomicrographs of Nissl-stained tissue sections from the PW and S reference spaces. We show that z-aligned point source data (unpublished hypothalamic microinjection sites) can be migrated from PW to S space to a first-order approximation in the mediolateral and dorsoventral dimensions using anisotropic scaling of the vector-formatted atlas templates, together with expert-guided relocation of obvious outliers in the migrated datasets. The migrated data can be contextualized with other datasets mapped in S space, including neuronal cell bodies, axons, and chemoarchitecture; to generate data-constrained hypotheses difficult to formulate otherwise. The alignment strategies provided in this study constitute a basic starting point for first-order, user-guided data migration between PW and S reference spaces along three dimensions that is potentially extensible to other spatial reference systems for the rat brain.

On Consensus in Belief Merging

We define a consensus postulate in the propositional belief merging setting. In a nutshell, this postulate imposes the merged base to be consistent with the pieces of information provided by each agent involved in the merging process. The interplay of this new postulate with the IC postulates for belief merging is studied, and an incompatibility result is proved. The maximal sets of IC postulates which are consistent with the consensus postulate are exhibited. When satisfying some of the remaining IC postulates, consensus operators are shown to suffer from a weak inferential power. We then introduce two families of consensus operators having a better inferential power by setting aside some of these postulates.

ASIC-Resistance of Multi-Hash Proof-of-Work Mechanisms for Blockchain Consensus Protocols

Blockchain technology rapidly gained popularity based on its open and decentralized operation. Consensus protocol is the core mechanism of a blockchain network that securely maintains the distributed ledger from possible attacks from adversaries. Proof-of-work (PoW) is a commonly used consensus protocol that requires a significant amount of computation to find a new valid block. As the application-specific integrated circuits (ASICs) that are specially designed for PoW computation begin to dominate blockchain consensus operation, the decentralized nature of blockchain networks is being threatened. Many PoW mechanisms are being proposed to disincentivize the use of ASICs in the consensus operation. Employing multiple hash functions in the PoW computation (i.e., multi-hash PoW) is one of the commonly adopted approaches to achieve such ASIC-resistance. In this paper, we experimentally evaluate the level of ASIC-resistance of the multi-hash PoW mechanisms. We assess the level of ASIC-resistance based on the performance gap between ASICs and general-purpose computing platforms. Contrary to the expectation of the multi-hash PoW mechanisms, our results reveal that ASIC-resistance of these PoW mechanisms is not strong enough to prevent ASIC-based mining. Most of them show similar levels of ASIC-resistance as those of PoW mechanisms that are already defeated by ASIC-based systems.

A model of multi-agent consensus for vague and uncertain beliefs.

Consensus formation is investigated for multi-agent systems in which agents’ beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning borderline. This is combined with a probabilistic model of uncertainty. A belief combination operator is then proposed, which exploits borderline truth values to enable agents with conflicting beliefs to reach a compromise. A number of simulation experiments are carried out, in which agents apply this operator in pairwise interactions, under the bounded confidence restriction that the two agents’ beliefs must be sufficiently consistent with each other before agreement can be reached. As well as studying the consensus operator in isolation, we also investigate scenarios in which agents are influenced either directly or indirectly by the state of the world. For the former, we conduct simulations that combine consensus formation with belief updating based on evidence. For the latter, we investigate the effect of assuming that the closer an agent’s beliefs are to the truth the more visible they are in the consensus building process. In all cases, applying the consensus operators results in the population converging to a single shared belief that is both crisp and certain. Furthermore, simulations that combine consensus formation with evidential updating converge more quickly to a shared opinion, which is closer to the actual state of the world than those in which beliefs are only changed as a result of directly receiving new evidence. Finally, if agent interactions are guided by belief quality measured as similarity to the true state of the world, then applying the consensus operator alone results in the population converging to a high-quality shared belief.

Flexibility of KorA, a plasmid-encoded, global transcription regulator, in the presence and the absence of its operator.

The IncP (Incompatibility group P) plasmids are important carriers in the spread of antibiotic resistance across Gram-negative bacteria. Gene expression in the IncP-1 plasmids is stringently controlled by a network of four global repressors, KorA, KorB, TrbA and KorC interacting cooperatively. Intriguingly, KorA and KorB can act as co-repressors at varying distances between their operators, even when they are moved to be on opposite sides of the DNA. KorA is a homodimer with the 101-amino acid subunits, folding into an N-terminal DNA-binding domain and a C-terminal dimerization domain. In this study, we have determined the structures of the free KorA repressor and two complexes each bound to a 20-bp palindromic DNA duplex containing its consensus operator sequence. Using a combination of X-ray crystallography, nuclear magnetic resonance spectroscopy, SAXS and molecular dynamics calculations, we show that the linker between the two domains is very flexible and the protein remains highly mobile in the presence of DNA. This flexibility allows the DNA-binding domains of the dimer to straddle the operator DNA on binding and is likely to be important in cooperative binding to KorB. Unexpectedly, the C-terminal domain of KorA is structurally similar to the dimerization domain of the tumour suppressor p53.

On Recursive Blind Equalization in Sensor Networks

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms.

A three-valued photoelectrochemical logic device realising accept anything and consensus operations.

A new application of a hybrid material exhibiting the photoelectrochemical photocurrent switching (peps) effect in a three-valued logic device is reported. In contrast to other similar peps-based systems, the one described here is capable of performing basic ternary logic operations: gullibility and consensus.

Majority OWA operator for opinion rating in social media

In recent years, the acquisition of products and services via Internet has grown exponentially. Today, a person goes through different websites in search of better alternatives and prices. In this searching process, he/she uses social media applications to examine the opinions of others who have purchased such services or products. Also, the seller offers summarized information of buyers' valuation by a satisfaction value on a predefined scale (expressed with a system of stars or linguistic labels). This number is calculated using an arithmetic mean and, in many cases, does not adequately reflect all users' opinions. In this article, a new consensus operator called selective aggregated majority ordered weighted averaging (SAM-OWA) is proposed. It is based on the opinions of the majority though also considering the opinion of the minority. SAM-OWA calculates the individual weights of each value of the satisfaction scale, using the number of votes obtained (cardinality). The mathematical foundation of SAM-OWA operator and some application examples to improve the results obtained by the arithmetic mean are presented.

Subjective Logic Dynamic Model and its Application Research

In Josang's subjective logic, the consensus operator will have unreasonable results when the evidence with high conflict; the base rate (a) and non-informative prior weight (C) could not change dynamically. This paper proposed a dynamic subjective logic model that expand and rich the theory of Josang's subjective logic. Through the establishment of a cartesian coordinate system and using line / curve fitting algorithm to outline the dynamic track of the opinion, it can more intuitively observe the change and development tendency of the opinions in different observation periods. At the same time, according to different application environment, the dynamic function of the base rate (a) and non-informative prior weight (C) were given. The subjective logic was extended to five tuples and the consensus operator was improved. The new consensus operator not only met the commutative and associative but also was consistent with cognitive rules of the people. A strict mathematical proof was given. Finally, through the simulation experiments, the results show that the subjective logic improved more reasonable and effective. It could be better adapted to the changing environment.