Consensus Algorithm Research Articles

BSPM: Blockchain-based Security Protection Model for power load management terminals

Power load management terminals are essential components of the smart grid system. However, they encounter numerous security challenges owing to their widespread deployment across large areas. These nodes exist in uncontrollable physical environments and are vulnerable to malicious attacks. Researching security measures for these terminals is vital to enhancing the security of the smart grid. Blockchain technology, with its distributed storage and tamper-proof features, can effectively protect the data security of these terminals. This paper proposes a blockchain-based security protection model for power load management terminals. First, the architecture of the model is designed using blockchain technology. In addition, a dynamic trust evaluation method for terminals is established through the beta reputation system. The blockchain consensus algorithm for power load management terminals is designed based on the trust evaluation value and practical Byzantine fault tolerance. The effectiveness of the trust evaluation model is verified by simulating the terminal operating situation. The message complexity and storage requirements of the security protection model are analyzed by comparing them with results from related studies.

Blockchain-Based Cold Chain Traceability with NR-PBFT and IoV-IMS for Marine Fishery Vessels

Due to limited communication, computing resources, and unstable environments, traditional cold chain traceability systems are difficult to apply directly to marine cold chain traceability scenarios. Motivated by these challenges, we construct an improved blockchain-based cold chain traceability system for marine fishery vessels. Firstly, an Internet of Vessels system based on the Iridium Satellites (IoV-IMS) is proposed for marine cold chain monitoring. Aiming at the problems of low throughput, long transaction latency, and high communication overhead in traditional cold chain traceability systems, based on the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, a Node-grouped and Reputation-evaluated PBFT (NR-PBFT) is proposed to improve the reliability and robustness of blockchain system. In NR-PBFT, an improved node grouping scheme is designed, which introduces a consistent hashing algorithm to divide nodes into consensus and candidate sets, reducing the number of nodes participating in the consensus process, to lower communication overhead and transaction latency. Then, a reputation evaluation model is proposed to improve the node selection mechanism of NR-PBFT. It enhances the enthusiasm of nodes to participate in consensus, which considers the distance between fishery vessels, data size, and refrigeration temperature factors of nodes to increase throughput. Finally, we carried out experiments on marine fishery vessels, and the effectiveness of the cold chain traceability system and NR-PBFT were verified. Compared with PBFT, the transaction latency of NR-PBFT shortened by 81.92%, the throughput increased by 84.21%, and the communication overhead decreased by 89.4%.

Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems

This study explores fault-tolerant consensus in leader–following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler–Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Modern Applications of Blockchain Technologies in Contemporary Times: A Systematic Review

Blockchain technology is one of the most innovative and versatile technological advancements of contemporary times, finding applications in various fields such as finance, healthcare, education, and real estate. This paper provides a systematic review of blockchain technology, its historical evolution, fundamental concepts, different types of blockchain, and its various applications. The study also explores the key characteristics of blockchain, including decentralization, transparency, and immutability, and discusses the challenges and limitations faced by the technology, such as scalability issues, security concerns, and regulatory challenges. Additionally, the paper looks into future trends and developments in blockchain technology, particularly advancements in consensus algorithms, integration with other emerging technologies, and the rise of Central Bank Digital Currencies (CBDCs) and Decentralized Finance (DeFi).

Distributed state estimation with compressed and synchronized auxiliary particle filters using graph theory

In this paper, we propose a novel compressed distributed auxiliary particle filter that uses graph theory (CDAPF-GT) to reduce the communication cost and improve the estimation accuracy of a nonlinear state space model. Our method compresses the global loglikelihood function into a set of parameters that are updated by an average consensus algorithm over a network of nodes. Unlike the existing methods, our method synchronizes the particle sets among all the nodes and uses the latest measurements to construct a better proposal distribution. Our method is suitable for applications that require fast and reliable distributed state estimation. We show through various simulation scenarios that our method outperforms the common counterpart method in terms of estimation accuracy with the same level of communication.

A distributed coordinated control strategy for isolated AC microgrids based on consensus algorithm considering communication delay

AbstractIn the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions.

SecureVFL: privacy-preserving multi-party vertical federated learning based on blockchain and RSS

Vertical Federated Learning (VFL), which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions, encounters numerous privacy and security threats. Existing solutions often suffer from centralized architectures, and exorbitant costs. To mitigate these issues, in this paper, we propose SecureVFL, a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy. SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm, Proof of Feature Sharing (PoFS), to facilitate decentralized, trustworthy, and high-throughput federated training. SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing (RSS) protocol for feature intersection summation among overlapping users. Furthermore, we propose a (42)-sharing protocol to achieve federated training in a four-party VFL setting. This protocol involves only addition operations and exhibits robustness. SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities, and provides mechanisms to unmask these identities when malicious activities are performed. We illustrate the proposed mechanism through a case study on VFL across four banks. Finally, our theoretical analysis proves the security of SecureVFL. Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes, such as MP-FedXGB, in terms of both overhead and model performance.

PoAh 2.0: AI-empowered dynamic authentication based adaptive blockchain consensus for IoMT-edge workflow

This paper introduces a significant advancement in the Proof of Authentication (PoAh) consensus algorithm, designed specifically for resource-constrained Internet of Things (IoT) devices. Building upon the foundations of PoAh consensus, this enhanced iteration, known as PoAh 2.0, integrates Artificial Intelligence (AI) at the block creator node level. This novel approach allows for the generation of block transactions embedded with AI-determined sensitivity and other applicable transaction-related metadata, a pioneering concept in this domain. The verifier node, a trusted entity, is tasked with verifying incoming blocks, utilizing the block header and its metadata information to determine authenticity while preserving the privacy of the content of the block’s data. A core innovation of PoAh 2.0 is its dynamic authentication mechanism, which adapts to the sensitivity level of the data within each block, behaving in an adaptive way based on the situation. AI plays a crucial role in this process, ensuring the block’s integrity and security are maintained. To demonstrate the efficacy of this advanced AI-enabled PoAh 2.0 consensus, we conducted a case study in an Internet of Medical Things (IoMT)-based eHealth scenario. The results from this study reveal that our developed dynamic authentication technique not only significantly enhances the original PoAh version but also establishes a new benchmark in block validation and security for eHealth applications. The integration of AI and improved dynamic authentication, calibrated to the security needs of each block, marks a novel and significant stride in blockchain research. This development not only enriches the current understanding of blockchain applications in IoT, but also sets a new direction for future research in secure and efficient blockchain implementations in the IoMT-Edge centric eHealth landscape.

An enhanced consensus algorithm for blockchain

Consensus plays a crucial role in blockchain technology, with the delegated proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link–delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism.

Updated delegated proof of stake and Nash equilibrium: A mining pool game model for blockchain network

SummaryIn the present era, blockchain technology has an extensive variety of uses in the financial, marketing, and so forth. The performance and security of the blockchain system are directly affected by the consensus algorithm. Since there are 30 consensus techniques like proof of stake, delegated proof of stake, and proof of work, and so forth in the previous years, the operating efficiency, safety, and stability still lag behind the basic requirements. To this concern, this research manuscript introduces a novel consensus mechanism‐based game model using upgraded delegated proof of stake (UDPoS) and Nash equilibrium (NE) that is UDPoS‐NE. In a blockchain network powered by UDPoS, blockchain miners provide computing power to publish blocks. Due to a single miner's limited Computing Power (CP), miners frequently join mining pools and divide the pool's profits in accordance with their respective contributions. However, certain miners launch block‐withholding attacks that lead to loss the computer power and endanger the blockchain network's effectiveness. Therefore, NE is used to calculate cooperative game solutions and optimize policy decisions of the miners to frustrate the block withholding attack by the influence of stakes and computing power on producing blocks. Using this mechanism, efficiency, and stability in the consensus process is achieved through the combined influence of computing resources and stakes on block generation. Finally, the suggested model's applicability and validity are confirmed with a throughput (600 TPS), delay (3 s), and energy consumption (<100 KWh) for 10 nodes and processing time (0.9 s) for 200 nodes.

On Identity, Transaction, and Smart Contract Privacy on Permissioned and Permissionless Blockchain: A Comprehensive Survey

Blockchain is a decentralized distributed ledger that combines multiple technologies, including chain data structures, P2P networks, consensus algorithms, cryptography, and smart contracts. This gives the blockchain the characteristics of decentralization, immutability, and traceability. However, blockchain stores smart contracts and transactions in blocks publicly, which poses the risk of data leakage and misuse. For example, by mining and analyzing blockchain transaction information, attackers can correlate transactions with user information, resulting in the disclosure of user privacy. Many current reviews focus on the privacy of permissionless blockchains or cryptocurrencies, requiring more in-depth investigations and detailed categorical analysis. To fill this gap, this work comprehensively reviews the latest and traditional methods related to identity, transaction, and smart contract privacy within permissioned and permissionless blockchains. Additionally, we summarize the existing problems, threats, and challenges of data management in different blockchain architectures. Last, we discuss future research directions for blockchain privacy protection technology, which can offer feasible ideas for researchers to explore further.

A modified dispersed frequency and phase consensus algorithm based on differential evolution and credibility weighting matrix

In distributed antenna array systems, accurate synchronization of the frequency and phase of the transmission signals among subarrays is a premise for beamforming. To solve this problem, a modified dispersed frequency and phase consensus (MDFPC) algorithm, which is based on differential evolution (DE) and the credibility weighting matrix, is proposed in this paper. DE algorithm is adopted to optimize the mixing matrix of MDFPC algorithm, and the convergence speed of consensus algorithm can be improved. Moreover, Lanczos method is adopted to calculate the fitness of individuals during optimization, thus the computation amount of the eigenvalue decomposition of the large-scale, sparse and symmetric mixing matrix can be effectively reduced. In addition, the influence of the transmission distance between subarrays on the received Signal-to-Noise Ratio (SNR) is considered, and the credibility weighting matrix can be obtained. Then the contribution of low-credible frequency and phase messages to the iterative updated values can be decreased by the credibility weighting matrix, thus more accurate frequency and phase updated values can be obtained in each iteration. It is shown from simulation results that the proposed algorithm can further reduce the phase synchronization error by at least 8% and achieve at least 18% convergence speed improvement compared to dispersed frequency and phase consensus (DFPC) algorithm.

Optimisation of real‐scene 3D building models based on straight‐line constraints

AbstractDue to the influence of repeated textures or edge perspective transformations on building facades, building modelling based on unmanned aerial vehicle (UAV) photogrammetry often suffers geometric deformation and distortion when using existing methods or commercial software. To address this issue, a real‐scene three‐dimensional (3D) building model optimisation method based on straight‐line constraints is proposed. First, point clouds generated by unmanned aerial vehicle (UAV) photogrammetry are down‐sampled based on local curvature characteristics, and structural point clouds located at the edges of buildings are extracted. Subsequently, an improved random sample consensus (RANSAC) algorithm, considering distance and angle constraints on lines, known as co‐constrained RANSAC, is applied to further extract point clouds with straight‐line features from the structural point clouds. Finally, point clouds with straight‐line features are optimised and updated using sampled points on the fitted straight lines. Experimental results demonstrate that the proposed method can effectively eliminate redundant 3D points or noise while retaining the fundamental structure of buildings. Compared to popular methods and commercial software, the proposed method significantly enhances the accuracy of building modelling. The average reduction in error is 59.2%, including the optimisation of deviations in the original model's contour projection.

Decentralized Multi-Area Economic Dispatch in Power Systems Using the Consensus Algorithm

A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation.

Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

Decentralized intelligent multi-party competitive aggregation framework for electricity prosumers

Electricity management systems are experiencing significant challenges due to the increased penetration of distributed energy resources. Electricity flows in distribution networks are transforming from unidirectional to bi-directional form. Consumers are transitioning to prosumers with different characteristics, where they take more active roles in electricity generation and consumption. Aggregators are vital financial intermediary agents in the power system transitions, as they could aggregate energy profiles of prosumers. The market competition between aggregators and interactions between prosumers and aggregators are complex and dynamic, which requires a holistic framework to model the market competition. This paper proposes an intelligent aggregation framework with edge computing, enabling decentralized competition for multiple aggregators and prosumers, which can be solved with a graph-based consensus algorithm. This study mathematically proves the proposed algorithm's convergence guarantee and convergence rate. In addition, the proposed framework is applied to an open-source dataset to demonstrate its applicability. Lastly, a benchmark analysis is conducted to show that the proposed algorithm has better communication complexity than the benchmark algorithms.

Blockchain Innovation: Enhancing Security and Clarity in Digital Exchanges

Blockchain technology, which was originally intended for cryptocurrency transactions, has grown into a transformative tool for improving security and transparency in a variety of industries. This research paper examines blockchain mechanisms such as decentralisation, cryptographic security, and consensus algorithms, emphasising their importance in ensuring data integrity and transparency. The broad impact of blockchain is demonstrated through applications in financial services, supply chain management, healthcare, digital identity verification, real estate, and public services. Despite challenges such as scalability, regulatory issues, and energy consumption, blockchain offers significant benefits in terms of fraud prevention, cost savings, and efficiency. Future possibilities include combining blockchain with emerging technologies such as AI and IoT, central bank digital currencies, and more energy-efficient consensus mechanisms. This study emphasises blockchain's crucial role in improving digital exchanges and its potential to revolutionise them. Key Words: Blockchain technology, security, transparency, digital transactions, decentralization, cryptographic security, smart contracts

Disturbance observer‐based matrix‐weighted consensus

AbstractIn this paper, we proposed several disturbance observer‐based matrix‐weighted consensus algorithms. A new disturbance observer is firstly designed for linear systems with unknown matched or mismatched disturbances representable as the multiplication of a known time‐varying matrix with a unknown constant vector. Under some assumptions on the boundedness and persistent excitation of the regression matrix, the disturbances can be estimated at an exponential rate. Then, a suitable compensation input is provided to compensate the unknown disturbances. Second, disturbance‐observer based consensus algorithms are proposed for matrix‐weighted networks of single‐ and double‐integrators with matched or mismatched disturbances. We show that both matched and mismatched disturbances can be estimated and actively compensated, and the consensus system uniformly globally asymptotically converges to a fixed point in the kernel of the matrix‐weighted Laplacian. Depending on the network connectivity, the system can asymptotically achieve a consensus or a cluster configuration. The disturbance‐observer based consensus design is further extended for a network of higher‐order integrators subjected to disturbances. Finally, simulation results are provided to support the mathematical analysis.

A survey on scalable consensus algorithms for blockchain technology

The process of reaching an agreement on a value within a distributed network, known as a consensus problem, is a defining feature of blockchain. This consensus problem can be seen in various applications like load balancing, transaction validation in blockchain, and distributed computing. In recent years, many researchers have provided solutions to this problem. Hence we have presented a survey in which we delved into blockchain consensus algorithms and conducted a comparative analysis of all the consensus algorithms to provide information about each protocol’s advantages and drawbacks. This survey starts with the standard proof-of-work consensus protocol applied in bitcoin cryptocurrency and its limitations on the ground of the following parameters: throughput (transactions per second), latency, forks, fault tolerance, double spending attacks, and power consumption. The rest of the consensus algorithms in this paper have been systematically covered to address the limitations of proof-of-work. This paper also covered Raft and PBFT consensus algorithms suitable for permissioned networks. Although the PBFT consensus protocol has a high throughput and a low latency, it has limited node scalability. The PBFT has a low byzantine fault tolerant rate. This paper also covers PoEWAL for blockchain-based IoT applications and WBFT, which prevents corrupt nodes from taking part in consensus. A comparative analysis of the consensus algorithms provides an explicit knowledge of the present research, which also offers guidance for future study.

Lightweight adaptive Byzantine fault tolerant consensus algorithm for distributed energy trading

With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O(n). (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by cross-validation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in large-scale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT’s communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.