Consensus Algorithm Research Articles

A Parallel Multi-Party Privacy-Preserving Record Linkage Method Based on a Consortium Blockchain

Privacy-preserving record linkage (PPRL) is the process of linking records from various data sources, ensuring that matching records for the same entity are shared among parties while not disclosing other sensitive data. However, most existing PPRL approaches currently rely on third parties for linking, posing risks of malicious tampering and privacy breaches, making it difficult to ensure the security of the linkage. Therefore, we propose a parallel multi-party PPRL method based on consortium blockchain technology which can effectively address the issue of semi-trusted third-party validation, auditing all parties involved in the PPRL process for potential malicious tampering or attacks. To improve the efficiency and security of consensus within a consortium blockchain, we propose a practical Byzantine fault tolerance consensus algorithm based on matching efficiency. Additionally, we have incorporated homomorphic encryption into Bloom filter encoding to enhance its security. To optimize computational efficiency, we have adopted the MapReduce model for parallel encryption and utilized a binary storage tree as the data structure for similarity computation. The experimental results show that our method can effectively ensure data security while also exhibiting relatively high linkage quality and scalability.

DTPBFT:A dynamic and highly trusted blockchain consensus algorithm for UAV swarm

With the continuous development of UAV technology, the application of UAV swarm in the military field is gradually becoming the focus of research around the world. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues such as a single point of failure brought by centralized management. Because of the advantages of distributed, tamper-proof, and traceability, blockchain is applied to UAV swarm to solve some of the security problems caused by centralized management. However, due to the limitations of its consensus algorithm Practical Byzantine Fault Tolerance (PBFT), its communication complexity will increase rapidly with the increase of the number of nodes, which also leads to the poor scalability of the algorithm and can only be applied to small-scale networks. To use the PBFT algorithm in large-scale networks such as UAV swarm, a dynamic and highly trusted PBFT (DTPBFT) algorithm is proposed in this paper. Firstly, a new consensus algorithm model including consensus layer and verification layer is designed. Then, a consensus node election scheme based on trust mechanism is proposed under this model. The trust degree of nodes in the blockchain network is comprehensively evaluated by selecting several representative indicators, and the weight factor of each indicator is calculated by entropy weight method. It not only reduces the communication complexity, but also ensures the reliability and dynamic update of consensus nodes. Experiments show that when the number of UAVs is 200, the consensus time of DTPBFT is 0.24 s, which indicates that this algorithm can support large-scale UAV swarm without causing communication congestion, so it has good scalability. In addition, experiments also show that DTPBFT can tolerate more than 13 malicious nodes, which improves the fault tolerance rate of PBFT.

Robust median consensus cubature Kalman filter for distributed sensor networks

Distributed estimation of multiagent systems has gained increasing attention in recent years, with consensus algorithms being one of the popular research topics. However, most existing algorithms aim to reach a consensus on the average value of all nodes, which can be significantly affected by outliers and disturbances. To achieve a more resilient distributed estimation, this paper focuses on the median value consensus strategy. Specifically, a continuous median consensus method with an adaptive parameter is presented and discretized using time-varying steps. This approach eliminates chattering errors caused by discretization and enhances convergence speed considerably. Furthermore, utilizing the proposed algorithm, a median consensus cubature Kalman filter (MC-CKF) is designed for nonlinear scenarios. Simulation results demonstrate that our proposed algorithm outperforms traditional algorithms when facing a denial-of-service attack.

RaBFT: an improved Byzantine fault tolerance consensus algorithm based on raft

RaBFT: an improved Byzantine fault tolerance consensus algorithm based on raft

Differentially private average consensus with improved accuracy-privacy trade-off

This paper studies the average consensus problem with differential privacy of initial states, for which it is widely recognized that there is a trade-off between the mean-square computation accuracy and privacy level. Considering the trade-off gap between the average consensus algorithm and the centralized averaging approach with differential privacy, we propose a distributed shuffling mechanism based on the Paillier cryptosystem to generate correlated zero-sum randomness. By randomizing each local privacy-sensitive initial state with an i.i.d. Gaussian noise and the output of the mechanism using Gaussian noises, it is shown that the resulting average consensus algorithm can eliminate the gap in the sense that the accuracy-privacy trade-off of the centralized averaging approach with differential privacy can be almost recovered by appropriately designing the variances of the added noises. We also extend such a design framework with Gaussian noises to the one using Laplace noises, and show that the improved privacy-accuracy trade-off is preserved.

Erdos: A Novel Blockchain Consensus Algorithm with Equitable Node Selection and Deterministic Block Finalization

Erdos: A Novel Blockchain Consensus Algorithm with Equitable Node Selection and Deterministic Block Finalization

Research on Multi-UAV Obstacle Avoidance with Optimal Consensus Control and Improved APF

To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities through the particle swarm optimization (PSO) algorithm. The optimal consensus control algorithm is then employed to achieve the optimal convergence rate of the UAV formation. To mitigate the limitations of traditional APF, a collinear force deflection angle is introduced, along with an obstacle avoidance method aimed at preventing UAVs from being trapped in locally optimal solutions. Additionally, an obstacle avoidance algorithm based on virtual force fields between UAVs is designed. Comparative analysis against the basic algorithm demonstrates the effectiveness of the designed optimal consensus algorithm in improving formation convergence performance. Moreover, the improved APF resolves local optimal solution issues, enabling UAVs to effectively navigate around obstacles. Simulation results validate the efficacy of this method in achieving multi-UAV formation control while effectively avoiding obstacles.

Research on Distributed Coordination Control Method for Microgrid System Based on Finite-Time Event-Triggered Consensus Algorithm

Research on Distributed Coordination Control Method for Microgrid System Based on Finite-Time Event-Triggered Consensus Algorithm

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Unlocking Potential: Blockchain Technology and its impact on Agricultural Supply Chains in Pakistan

This review article explores the potential of blockchain technology in revolutionizing agricultural supply chains in Pakistan. In light of growing evidence, this is a new and emerging connection with great scope for agricultural development globally. The case of Pakistan is particularly interesting since its agriculture sector serves as the backbone of the nation's economy with agriculture significantly contributing to economic growth. However, sectoral and supply chain challenges allow for technology adoption to emerge as a transformative solution. Against this backdrop, the article reviews how blockchain technology, with its decentralized architecture and consensus algorithms, addresses key issues within agricultural supply chains particularly. The article highlights the global and local contexts, emphasizing blockchain technology as a solution to ensuring security in data sharing, permanence in record-keeping, and transparency end-to-end in the supply chain. Examining applications from developing countries, including India, Vietnam, Malaysia, Nigeria, and keeping a focus on specific evidence and learnings derived for Pakistan, the review provides a cross-country analysis, aiding understanding of this emerging technology and its applications. The global evidence identified in the article aid the development of key learnings for Pakistan, emphasizing the potential for improved data management, transparency, and reduced reliance on intermediaries. The article concludes with a futuristic outlook for Pakistan, proposing the effectiveness of blockchain-based frameworks for the agricultural supply chain and highlighting the role of the government that makes optimizing agricultural processes and fostering sector-wide transformation possible.

BlockEdge: Building Bridges to Safe and Efficient Distributed Systems

Abstract: In the ever-evolving landscape of distributed systems, ensuring safety, efficiency, and scalability remains a paramount challenge. BlockEdge emerges as a pioneering framework designed to address these critical issues by leveraging the principles of blockchain technology and advanced consensus mechanisms. This abstract outlines the key features, innovations, and potential impacts of BlockEdge on the realm of distributed computing. BlockEdge integrates blockchain's immutable ledger properties with a novel consensus algorithm tailored for distributed systems. Unlike traditional blockchain applications that prioritize decentralization for financial transactions, BlockEdge focuses on enhancing the performance and reliability of distributed applications. The framework employs a hybrid consensus model that combines Byzantine Fault Tolerance (BFT) with Proof-of-Stake (PoS), optimizing both security and energy efficiency. A standout feature of BlockEdge is its modular architecture, which allows seamless interoperability between different types of distributed networks. This modularity facilitates the integration of various consensus protocols, catering to the specific needs of diverse applications, from IoT networks to large-scale data processing systems. By enabling secure and efficient cross-chain communication, BlockEdge effectively mitigates the silo effect prevalent in current distributed system designs.

Application of Machine Learning to Detect Building Points in Photogrammetry-based Point Clouds

Different point cloud technologies such as Terrestrial Laser Scanners (TLS), Airborne Laser Scanners (ALS), Mobile Mapping Systems (MMS), and Unmanned Aerial Vehicles (UAV) have become increasingly more common in land surveying and geoinformatics over recent years. Thanks to these modern tools, experts can survey large areas cost-effectively with either high resolution or high accuracy. However, processing the point cloud, which consists of millions of points, can be a massive challenge. Manual processing of these large datasets can often be very time-consuming and hardware-demanding, and most of the time, only a limited part of the point cloud is used to derive the final products. The solution can be to automate the process as much as possible. Several advanced mathematical methods, especially Machine Learning (ML) algorithms, allow efficient automated processing of point clouds. This paper presents a processing chain to detect and separate building points from large-scale photogrammetry-based point clouds. The processing is based on the combination of Random Sample Consensus (RANSAC) and Machine Learning (ML) algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Multi-Layer Perceptron (MLP). Presented methods were trained and tested on established and open available Heissigheim 3D (H3D) dataset to separate roof and vegetation points with over 90% accuracy in order to enhance the separation of building points on large-scale point clouds.

Camera-IMU extrinsic calibration method based on intermittent sampling and RANSAC optimization

Accurately calibrate the extrinsic parameters between a camera and an Inertial Measurement Unit (IMU) is a prerequisite for achieving sensor data fusion in visual-inertial navigation systems. Due to delays introduced by triggering, transmission, and other factors, the sampled times of the camera and IMU do not align with the system timestamps, leading to a decrease in the accuracy of extrinsic calibration. Existing calibration methods are unable to avoid the temporal misalignment between the camera and IMU. This paper introduces a novel intermittent calibration sampling method to address temporal misalignment challenges. The approach entails detecting motion-stillness transition points in the IMU data as keyframe segmentation criteria, thereby decoupling data acquisition from system timestamps and aligning it with the sensor’s motion process. Subsequently, the robot hand-eye calibration method is applied to the extrinsic calibration of the Camera-IMU, combining the Random Sample Consensus algorithm to filter the poses of the camera and IMU. This process achieves precise calibration of extrinsic parameters. Experimental validation shows that the algorithm proposed in this paper enhances calibration accuracy when compared to the widely used Kalibr Camera-IMU calibration toolbox. The rotational extrinsic parameter accuracy increased by 181.25%, and the translational extrinsic parameter accuracy improved by 168.33%. These findings provide effective technical means for precise measurement of Camera-IMU extrinsic parameters.

BLPCA-ledger: A lightweight plenum consensus protocols for consortium blockchain based on the hyperledger indy

An examination of different distributed real-time applications operating on the blockchain platform is conducted. These applications can be broadly classified into three types: permissionless public, permissioned private, and consortium chains. In order for a decentralized network to function independently, consensus mechanisms are needed to facilitate the delivery of transactions and keep track of them in a ledger. But the fundamental idea behind Blockchain technology is the use of several consensus protocols, like Proof of Stake, Proof of Elapsed Time, etc., which requires greater processing power. In order to arrange transactions, it increases the demand for buying more computing units. Furthermore, present consortia blockchain consensus mechanisms lack a policy to collect socio-economic financial levies, including monies for charitable donations, education, and social activities. To collect socio-economic taxes, this study suggests a lightweight Plenum consensus algorithm called "BLPCA" for consortium blockchains built on Hyperledger Indy. The Byzantine Fault Tolerance concept combined with optimization is used in the suggested BLPCA systems to manage large-scale decentralized traffic of socioeconomic hierarchy. Because there are no transaction costs, BLPCA encourages government analysts to review funds using fewer resources. By including a multithreading heterogeneous technique, the BLPCA can effectively handle multi-transaction needs and execute this protocol in an industrial setting that operates in real-time. It is observed from the simulations that even in the worst-case network scenario—such as a fork—the suggested consortium blockchain does not crash a single transaction. In order to guarantee node scalability, a high propagation speed is determined. Furthermore, the BLPCA shows an excellent average time while constructing socioeconomic transaction blocks.

A Review on Blockchain Technology

Blockchain technology has emerged as a disruptive force with the potential to revolutionize various industries by offering decentralized, secure, and transparent solutions for data management and transactions. Initially conceptualized as the underlying technology for cryptocurrencies like Bitcoin, blockchain has evolved into a versatile tool applicable across diverse domains such as finance, supply chain management, healthcare, and government services. This paper provides an overview of blockchain technology, its fundamental principles, and key components. It explores the decentralized consensus mechanisms, cryptographic techniques, and data structures that underpin blockchain networks, ensuring immutability, transparency, and integrity of recorded transactions. Furthermore, the paper examines the challenges and opportunities associated with blockchain adoption, including scalability, interoperability, regulatory concerns, and environmental impact. Through a comprehensive review of existing literature and case studies, this research elucidates the potential benefits of blockchain technology, such as increased efficiency, reduced costs, enhanced security, and improved trust among participants. Additionally, it highlights ongoing developments, innovations, and future directions in blockchain research and implementation, emphasizing the need for interdisciplinary collaboration and regulatory frameworks to realize the full potential of this transformative technology. Keywords : IOT, blockchain, cryptocurrency, smart contracts, consensus algorithms, internet of things.

A Robust Mismatch Removal Method for Image Matching Based on the Fusion of the Local Features and the Depth

Feature point matching is a fundamental task in computer vision such as vision simultaneous localization and mapping (VSLAM) and structure from motion (SFM). Due to the similarity or interference of features, mismatches are often unavoidable. Therefore, how to eliminate mismatches is important for robust matching. Smoothness constraint is widely used to remove mismatch, but it cannot effectively deal with the issue in the rapidly changing scene. In this paper, a novel LCS-SSM (Local Cell Statistics and Structural Similarity Measurement) mismatch removal method is proposed. LCS-SSM integrates the motion consistency and structural similarity of a local image block as the statistical likelihood of matched key points. Then, the Random Sampling Consensus (RANSAC) algorithm is employed to preserve the isolated matches that do not satisfy the statistical likelihood. Experimental and comparative results on the public dataset show that the proposed LCS-SSM can effectively and reliably differentiate true and false matches compared with state-of-the-art methods, and can be used for robust matching in scenes with fast motion, blurs, and clustered noise.

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

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

Investigating the impact of unstable network connections on the cluster running a consensus algorithm

This study explores the effects of unstable network connections on the operational efficiency of a computing cluster executing a consensus algorithm. Consensus algorithms are critical for maintaining the integrity and consistency of distributed systems, particularly in blockchain technologies and distributed databases. However, the reliability of these systems is often challenged by network instability, which can lead to delays, decreased throughput, and, in severe cases, loss of consensus. Research employs simulation experiments to quantify the impact of network disruptions such as latency variability on cluster running consensus algorithm. By systematically varying network conditions and observing the resultant effects on a cluster running a representative consensus algorithm, we identify critical thresholds of instability that significantly impair system performance. The findings contribute to a deeper understanding of the dynamics between network stability and distributed system, offering valuable insights for developers and engineers tasked with designing and maintaining robust distributed systems in the face of network unreliability.

Model-independent event-based consensus of multiple Euler-Lagrange systems with input disturbances

This paper investigates leaderless consensus (LLC) and leader-follower consensus (LFC) issues of multiple Euler-Lagrange systems (MELSs) with uncertain system parameters and input disturbances. Firstly, by utilizing event-based control strategy, event-based consensus protocols are proposed to achieve LLC and LFC, respectively. Then, to confirm that no Euler-Lagrange (EL) system will exhibit Zeno behavior under the above event-based consensus protocols, we show that the low bound of event-triggering interval is strictly positive for any time instants. Distinct with the existing related works, the proposed event-triggered consensus protocols only rely on local sampled state, and are irrelevant to the system parameters. Finally, some simulation examples are presented to illustrate the feasibility of the proposed event-based consensus algorithms.

An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.