Consensus Algorithm Research Articles

Cooperative Optimization Analysis of Variable-Speed and Fixed-Speed Pumped-Storage Units Under Large Disturbances in the Power System

Aimed at the large disturbance of a power system caused by frequent new energy clusters going off-grid, we propose a cooperative optimization strategy of variable-speed and constant-speed pumped-storage units to address power oscillation due to significant power shortages following the clusters going off-grid. From a multi-time-scale perspective, we first investigate the fast power support control strategy of variable-speed pumped-storage (VSPS) units during new energy cluster off-grid scenarios. Using a consensus algorithm, the VSPS acts as the primary unit, while the constant-speed unit provides long-term power support. We present a rapid power control method for VSPS to prioritize frequency stability in mainland grids with high new energy penetration. This ensures stable power support for large-scale new energy clusters under large disturbances across multiple time scales. Simulation analysis on a high proportion of new energy power networks with new energy clusters confirms the effectiveness of our proposed method.

Delta-k approach for space surveillance multireceiver radars

Abstract The increasing population of resident space objects is currently fostering many space surveillance initiatives. In this framework, on-ground multireceiver radars allow to reconstruct the target angular track, but the array configuration may cause the presence of multiple solutions and, if no pass prediction is available, the ambiguity cannot be solved a-priori. This work proposes an evolution of the Music Approach for Track Estimate and Refinement (MATER) algorithm. Given two different signals reflected by the same target, at each observation epoch their Direction Of Arrival (DOA) is estimated from the signal Covariance Matrix (CM) through the MUltiple SIgnal Classification (MUSIC) algorithm. Then, the possible ambiguous estimations are solved through the delta-k technique: the correct DOA is considered as the one featuring the smallest angular deviation comparing the two CM results. This process is repeated for all the epochs, and the DOAs are clustered according to the RANdom SAmple Consensus (RANSAC) algorithm. Finally, the most populated cluster is considered as the correct one, and the angular track is computed through a time regression of the two angular coordinates. The evolution of MATER algorithm is tested through numerical simulations. The algorithm converges to the correct solution in 100% of the cases, with an angular accuracy in the order of 1–10 mdeg.

AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection

With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies.

A Delphi Consensus Algorithm for Modern REBOA Programs: Employing a titratable catheter and partial aortic occlusion to advance the procedure.

A Delphi Consensus Algorithm for Modern REBOA Programs: Employing a titratable catheter and partial aortic occlusion to advance the procedure.

E-Voting Using Blockchain

Abstract: Democratic rule is based on safe and open electoral systems that can preserve their authenticity. The conventional voting processes are hampered significantly by electoral fraud and security breaches as well as centralized management and inefficiencies in auditability and accessibility. The issues of electoral systems reduce public trust along with lowering the credibility of election results. This paper introduces a new three-tier blockchain-based e-voting system designed to enhance voter privacy alongside system scalability and end-to-end verifiability to restore trust in electoral processes. Voter verification at Layer 1 (Identity Verification) combines Decentralized Identity (DID) with Zero-Knowledge Proofs (ZKP) and multimodal biometric techniques involving fingerprint scanning, facial recognition technology, and voice analysis. The system enables only the participation of valid voters while also protecting their private data and fulfilling different user needs. The system's Layer 2 (Vote Casting & Secure Storage) employs a hybrid consensus algorithm combining Byzantine Fault Tolerance (BFT) and Delegated Proof-of-Stake (dPoS) to store votes securely while reducing energy consumption. The system leverages Triple-Blind Signatures to provide complete voter anonymity by decoupling voter identities from their votes as well as any accompanying metadata. Lattice-based post-quantum cryptography is employed to encrypt votes which are distributed across sharded blockchain subnets for enhanced performance without sacrificing fault tolerance. The system accumulates votes via Merkle roots and verifies them via zk-SNARKs in Layer 3 (Result Processing & Transparency) that allows public observation without compromising voter privacy. A Live Audit Dashboard provides voters with the capability to check their vote in real time which facilitates transparent and accountable voting processes. The suggested system attains a secure and open electronic voting process using sophisticated cryptographic protocols in a decentralized setup compliant with international requirements while enabling digital democratic participation.

ReCaLS-in-DLT: resilient consensus algorithm for leader selection in distributed ledger technology

ReCaLS-in-DLT: resilient consensus algorithm for leader selection in distributed ledger technology

Coordination analysis over wide-area network of nonlinear multi-agent systems: hybrid event-triggered communication approach

In this study, we develop a distributed hybrid dynamic event-triggered consensus algorithm for wide-area networks comprising multiple clusters of nonlinear multi-agent systems. The network is modelled as a directed graph structure, with each cluster featuring strong connectivity and balance under the leadership of a designated node. Inter-cluster communication between leaders is regulated by a dynamic event-triggering mechanism, while intra-cluster agent updates are governed by state-dependent events. We employ a hybrid Lyapunov function approach to analyse the stability of the closed-loop system and rigorously examine the absence of Zeno behaviour. The effectiveness of our proposed algorithm is verified through two numerical simulations.

Optimizing Blockchain Scalability and IoT Security: A Multi-Objective Performance Framework

In this paper, a lightweight blockchain-based security framework for IoT networks that easily mitigates security, scalability, and data integrity issues is proposed. The framework associated with this paper integrates the hybrid consensus mechanism (DPoS + PBFT) and the hybrid cryptographic hashing approach (SHA-256 and ECC) to optimize both security and performance. The framework is implemented in a python based blockchain that employs IoT generated transactional data and is evaluated based on the performance parameters like latency, throughput, consensus delay, computational speed and also memory usage. Experimental results demonstrate that the hybrid consensus approach used in this framework achieves better performance metrics compared to the traditional consensus algorithms like PoW, PoS. hybrid hashing approach allows to achieve additional security with the same computational costs. The result shows that the potential of the proposed framework as a secure, scalable and energy solution in the context of real time IoT networks, and the future scope will include the autonomous deployment in the real-world, AI integration for Anomaly detection, and enhanced optimization for large IoT datasets.

Surgical treatment of lower urinary tract symptoms secondary to benign prostatic obstruction: an analysis and meta-synthesis of available guidelines

PurposeThe increase in minimally invasive treatments (MITs) for lower urinary tract symptoms secondary to benign prostatic obstruction (LUTS/BPO) has diversified surgical options, often outpacing solid evidence. The variety of available treatments, while beneficial, can confound physicians. Clinical guidelines provide direction but often differ due to varied evidence interpretation.MethodsWe have analyzed the available guidelines on the surgical treatment of LUTS/BPO updated within the last three years, focusing on those offering specific procedural recommendations. We compared recommendations, analyzed discrepancies, and developed a consensus algorithm that incorporated all pertinent advice.Results and limitationsOut of 14 guidelines, four met the inclusion criteria. Major challenges were inconsistent nomenclature and a lack of clear recommendations, especially for newer procedures such as Temporary Implantable Nitinol Device (iTIND™), Prostate Artery Embolization (PAE), Robotic Assisted Simple Prostatectomy (RASP), and Water Vapor Thermal Therapy (Rezūm™). Despite these issues, a consensus algorithm could be synthesized.Conclusions and clinical implicationsGuidelines for the treatment of LUTS/BPO present a disparate picture, with consensus mostly on older, well-established procedures due to substantial evidence. Newer interventions display significant variation in guideline recommendations and evidence interpretation. The consensus algorithm created from current guidelines offers a synthesized overview of recommendations, underscoring the need for standardized evidence criteria for guideline recommendations. Our work emphasizes the evolving complexity in LUTS/BPO management, aiming to aid urologists in decision-making and patient counseling by providing a clear and comprehensive tool.

Research on Consensus Algorithm for Intellectual Property Transactions Based on Practical Byzantine Fault Tolerant (PBFT) Algorithm

Aiming at the problems of significant communication overheads, the low reliability of primary nodes, and the insufficient dynamic adaptability of traditional consensus algorithms in intellectual property transaction scenarios, an Improved Practical Byzantine Fault Tolerant (IPBFT) algorithm based on the Chord algorithm and entropy weight method is proposed. Firstly, the Chord algorithm is employed to map nodes onto a hash ring, enabling dynamic grouping. Secondly, an entropy-based dynamic reputation model is constructed, quantifying the evaluation of node behaviors and calculating the overall reputation value. A three-level reputation classification mechanism is used to dynamically select primary and supervisory nodes, thereby reducing the probability of Byzantine nodes being elected. Then, a three-phase monitoring strategy for supervisory nodes is developed, which includes collection, review, and blackout. This improves the Raft consensus process, enhancing the detection and fault tolerance against malicious leaders. Finally, a grouped dual-layer consensus architecture is proposed. The lower layer uses an improved Raft algorithm for efficient consensus within groups, while the upper layer uses the PBFT algorithm for cross-group global consistency verification. Experimental findings demonstrate that the IPBFT algorithm is able to balance security, scalability, and consensus efficiency in a dynamic network environment, providing a better consensus solution for intellectual property transactions.

Blockchain Technology: Revolutionizing Data Security and Decentralized Applications

Blockchain technology, initially introduced as the underlying infrastructure for cryptocurrencies like Bitcoin, has become a powerful tool with far-reaching applications across various sectors, including finance, supply chain, healthcare, and government. This paper explores the fundamental principles of blockchain technology, including its architecture, consensus mechanisms, and cryptographic foundations. We highlight its decentralized nature and explore its significant applications, especially in secure transactions, smart contracts, and immutable data storage. Despite its transformative potential, blockchain faces several challenges related to scalability, energy consumption, interoperability, and regulatory concerns. The paper concludes by discussing potential future advancements in blockchain technology, such as consensus algorithm optimization, energy-efficient blockchain designs, and solutions to the blockchain scalability problem. Keywords: Blockchain, Decentralization, Cryptocurrency, Consensus Mechanisms, Smart Contracts, Scalability, Blockchain Security

Raft Consensus Algorithm: Simplicity and Robustness in Distributed Systems

The Raft consensus algorithm provides a more understandable alternative to previous protocols like Paxos while maintaining strong consistency guarantees in distributed systems. By breaking consensus into three distinct components—leader election, log replication, and safety—Raft creates a clear mental model for developers. Its widespread adoption spans distributed databases, configuration management, container orchestration, microservices infrastructure, and blockchain systems. Despite inherent challenges, including leader bottlenecks and brief unavailability during leader changes, Raft offers significant benefits through its straightforward design. Current innovations address these limitations through performance optimizations, multi-Raft architectures, formal verification, edge computing adaptations, and educational tools, ensuring the algorithm's continued relevance as distributed computing evolves.

Blockchain for Secure Energy Trading and Grid Management in Offshore Oil & Gas Facilities

The offshore oil and gas sector grapples with persistent energy management challenges, including inefficient electricity distribution, cybersecurity vulnerabilities, and opaque energy trading systems. Centralized approaches exacerbate these issues by introducing single points of failure, data manipulation risks, and operational inefficiencies. This research investigates how blockchain technology can revolutionize energy trading and grid management in offshore facilities through a decentralized, secure, and transparent framework. This study outlines a blockchain-based solution focusing on four key areas. First, smart contracts are employed to automate electricity transactions between offshore platforms and onshore grids. These self-executing agreements streamline processes by reducing administrative costs, accelerating settlements, and eliminating intermediaries, while supporting dynamic pricing models responsive to real-time supply and demand. Second, blockchain’s immutable ledger is leveraged to create tamper-proof records of power generation and consumption. This ensures transparency and auditability, critical for regulatory compliance and operational trust. The system integrates with sensors and IoT devices, validated through consensus mechanisms suitable for offshore conditions, such as intermittent connectivity. Third, we introduce a Decentralized Energy Ledger (DEL), a blockchain-based system that serves as a digital twin of energy flows across offshore supply chains. The DEL enhances visibility, improves forecasting accuracy, and optimizes energy use, yielding efficiency gains of 15–22% in case studies. Fourth, cybersecurity is fortified using blockchain’s cryptographic tools, such as public-key encryption, to secure communication channels and protect against threats. Testing reveals a 65% reduction in security incidents compared to traditional systems. The proposed framework integrates a permissioned blockchain, smart contracts, cryptographic security, and a tailored ledger system, optimized for marine environments. Results suggest operational cost reductions of 18–25%, energy efficiency improvements of 12–17%, and enhanced grid resilience. Challenges, including hardware durability, connectivity, and legacy system integration, are addressed with a phased implementation plan. This research advances industrial blockchain applications by tailoring solutions to offshore energy needs, offering a scalable model for secure, efficient, and sustainable operations. Future work should explore custom consensus algorithms and regulatory frameworks to support broader adoption.

Dynamic-FROST: Schnorr threshold signatures with a flexible committee

Abstract Threshold signatures enable any subgroup of predefined cardinality t t out of a committee of n n participants to generate a valid, aggregated signature. Although several ( t , n ) \left(t,n) -threshold signature schemes exist, most of them assume that the threshold t t and the set of participants do not change over time. Practical applications of threshold signatures might benefit from the possibility of updating the threshold or the committee of participants. Examples of such applications are consensus algorithms and blockchain wallets. In this article, we present Dynamic-FROST (D-FROST) that combines FROST, a Schnorr threshold signature scheme, with CHURP, a dynamic proactive secret sharing scheme. The resulting protocol is the first Schnorr threshold signature scheme that accommodates changes in both the committee and the threshold value without relying on a trusted third party. Besides detailing the protocol, we present a proof of its security: as the original signing scheme, D-FROST preserves the property of existential unforgeability under chosen-message attack.

Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection.

Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains limited by subjective interpretation and lack of quantitative standards. This study introduces an integrated approach combining fluorescence curve analysis, biophysical modeling, and machine learning to improve intraoperative perfusion assessment. Laparoscopic fluorescence videos from 68 low rectal cancer patients were analyzed, with 1,263 measurement points (15-20 per case) selected along colonic bands. Fluorescence intensity dynamics were extracted via color space transformation, video stabilization and image registration, then modeled using the Random Sample Consensus (RANSAC) algorithm and nonlinear least squares fitting to derive biophysical parameters. Three clinicians independently scored perfusion quality (0-100 scale) using morphological features and biophysical metrics. An XGBoost model was trained on these parameters to automate scoring. The model achieved superior test performance, with a root mean square error (RMSE) of 2.47, a mean absolute error (MAE) of 1.99, and an R2 of 97.2%, outperforming conventional time-factor analyses. It demonstrated robust generalizability, showing no statistically significant prediction differences across age, diabetes, or smoking subgroups (P > 0.05). Clinically, low perfusion scores in distal anastomotic regions were significantly associated with postoperative complications (P < 0.001), validating the scoring system's clinical relevance and discriminative capacity. The automated software we developed completed analyses within 2min, enabling rapid intraoperative assessment. This framework synergistically enhances surgical evaluation through three innovations: (1) Biophysical modeling quantifies perfusion dynamics beyond time-based parameters; (2) Machine learning integrates multimodal data for surgeon-level accuracy; (3) Automated workflow enables practical clinical translation. By addressing limitations of visual assessment through quantitative, rapid, and generalizable analysis, this method advances intraoperative perfusion monitoring and decision-making in colorectal surgery.

Design and Implementation of a Position-Based Coordinated Formation System for Underwater Multiple Small Spherical Robots

Due to the excellent concealment and high mobility, multiple small spherical underwater robots are essential for near coast defending missions. The formation of multiple small spherical underwater robots is particularly effective for tasks such as patrolling, reconnaissance, surveillance, and capturing sensitive targets. Moreover, some tasks need higher flexibility and mobility, such as reconnaissance, surveillance, or target encirclement at fixed locations. For this purpose, this paper proposes a position-based formation mechanism which is easily deployed for multiple spherical robots. A position planning method during the formation process is designed. This method creatively integrates the virtual linkage strategy with an improved consensus algorithm and the artificial potential field (APF) method. The virtual linkage strategy is in charge of computing the global formation desired target positions for robots according to the predefined position of the virtual leader joint. The improved consensus algorithm and APF are responsible for planning the local desired positions between two formation desired target positions, which is able to prevent collisions and excessive communication distance between robots. In order to verify the effectiveness of the proposed formation mechanism, adequate simulations and experiments are conducted. Thereby, the proposed formation frame offers great potential for future practical marine operations of the underwater multi-small robot systems.

Building Scalable and Quantum Attack Resistant Authenticated Message Delivery System for Internet of Vehicles With Blockchain Consensus Mechanism

ABSTRACTThe goal of intelligent transportation systems is becoming more and more realized with the use of the Internet of Vehicles (IoV) and the rapid advancement of processing and communication technologies. Nonetheless, a lot of Internet of Vehicles applications depend on a central processing and storage unit as well as wireless transmission mediators. This may result in exorbitant expenses and delays, as well as the disclosure of real data. We suggest the Vehicle‐Based Quantum‐Safe Blockchain Consensus (VBQBC) algorithm as a solution to these problems and an enhancement of the effectiveness of data storage, authentic processing, and data sharing in the Internet of Vehicles. The proposed VBQBC algorithm uses a consensus algorithm and blockchain technology to assure authentic communication between cars, overcoming the shortcomings and constraints of current state‐of‐the‐art systems. This algorithm uses ring learning with errors, and short integer solution assumptions in the construction of aggregation signatures to provide authenticity in blockchain technology. This aggregation technique reduces the size of data required for verification and improves scalability. The algorithm also incorporates a quantum‐safe authentication procedure as well as a key distribution and request process, which are demonstrated when vehicles move between different zones. This allows blockchain‐based systems to maintain their security, scalability, and efficiency even in the face of future cryptographic problems. The aggregation of signatures size for the proposed framework to the number of signatures to be aggregated N varies between (63.48 kb) and (131.34 kb). For aggregation of signatures, the proposed framework has signature size 6.3 kb, and the aggregated size of signatures is 64 kb. In simulation findings, our suggested VBQBC algorithm outperformed previous techniques in terms of authentication delay, key processing time, attack detection rate, throughput, and packet loss rate.

Bolt-On Strong Consistency: Specification, Implementation, and Verification

Strongly-consistent replicated data stores are a popular foundation for many kinds of online services, but their implementations are very complex. Strong replication is not available under network partitions, and so achieving a functional degree of fault-tolerance requires correctly implementing consensus algorithms like Raft and Paxos. These algorithms are notoriously difficult to reason about, and many data stores implement custom variations to support unique performance tradeoffs, presenting an opportunity for automated verification tools. Unfortunately, existing tools that have been applied to distributed consensus demand too much developer effort, a problem stemming from the low-level programming model in which consensus and strong replication are implemented—asynchronous message passing—which thwarts decidable automation by exposing the details of asynchronous communication. In this paper, we consider the implementation and automated verification of strong replication systems as applications of weak replicated data stores. Weak stores, being available under partition, are a suitable foundation for performant distributed applications. Crucially, they abstract asynchronous communication and allow us to derive local-scope conditions for the verification of consensus safety. To evaluate this approach, we have developed a verified-programming framework for the weak replicated state model, called Super-V. This framework enables SMT-based verification based on local-scope artifacts called stable update preconditions , replacing standard-practice global inductive invariants. We have used our approach to implement and verify a strong replication system based on an adaptation of the Raft consensus algorithm.

Fixed-Time Consensus Algorithm for Second-Order Multi-agent Systems With Actuator Faults Based on Non-singular Terminal Sliding Mode

Fixed-Time Consensus Algorithm for Second-Order Multi-agent Systems With Actuator Faults Based on Non-singular Terminal Sliding Mode

RACER: A Lightweight Distributed Consensus Algorithm for the IoT with Peer-Assisted Latency-Aware Traffic Optimisation

Internet-of-Things (IoT) devices are interconnected objects embedded with sensors and software, enabling data collection and exchange. These devices encompass a wide range of applications, from household appliances to industrial systems, designed to enhance connectivity and automation. In distributed IoT networks, achieving reliable decision-making necessitates robust consensus mechanisms that allow devices to agree on a shared state of truth without reliance on central authorities. Such mechanisms are critical for ensuring system resilience under diverse operational conditions. Recent research has identified three common limitations in existing consensus mechanisms for IoT environments: dependence on synchronised networks and clocks, reliance on centralised coordinators, and suboptimal performance. To address these challenges, this paper introduces a novel consensus mechanism called Randomised Asynchronous Consensus with Efficient Real-time Sampling (RACER). The RACER framework eliminates the need for synchronised networks and clocks by implementing the Sequenced Probabilistic Double Echo (SPDE) algorithm, which operates asynchronously without timing assumptions. Furthermore, to mitigate the reliance on centralised coordinators, RACER leverages the SPDE gossip protocol, which inherently requires no leaders, combined with a lightweight transaction ordering mechanism optimised for IoT sensor networks. Rather than using a blockchain for transaction ordering, we opted for an eventually consistent transaction ordering mechanism to specifically deal with high churn, asynchronous networks and to allow devices to independently and deterministically order transactions. To enhance the throughput of IoT networks, this paper also proposes a complementary algorithm, Peer-assisted Latency-Aware Traffic Optimisation (PLATO), designed to maximise efficiency within RACER-based systems. The combination of RACER and PLATO is able to maintain a throughput of above 600 mb/s on a 100-node network, significantly outperforming the compared consensus mechanisms in terms of network node size and performance.