Consensus Latency Research Articles

Research on PBFT consensus algorithm for grouping based on feature trust

The consensus mechanism is the core of the blockchain system, which plays an important role in the performance and security of the blockchain system . The Practical Byzantine Fault Tolerance (PBFT) algorithm is a widely used consensus algorithm, but the PBFT algorithm also suffers from high consensus latency, low throughput and performance. In this paper, we propose a grouped PBFT consensus algorithm (GPBFT) based on feature trust. First, the algorithm evaluates the trust degree of nodes in the transaction process through the EigenTrust trust model, and uses the trust degree of nodes as the basis for electing master nodes and proxy nodes. Then, the algorithm divides the nodes in the blockchain system into multiple groups, and the consensus within each independent group does not affect the other groups, which greatly reduces the communication overhead of the consensus process when the number of nodes in the system is large. Finally, we demonstrate through theoretical and experimental analysis that the GPBFT algorithm has a significant improvement in security and performance.

Blockchain Technology in the Management of Scientific and Technological Achievement Transformation in Chinese Universities

The article uses blockchain technology to design and develop a blockchain-based management system for the transformation of scientific and technological achievements in universities, which solves the problems of difficulty in promoting information of scientific and technological achievements, low trust of both parties in the process of transformation, and lack of corresponding service platform. It solves the problems such as low trust between the two parties and lack of a corresponding service platform. The main research work of the article is as follows: first, it conducts demand analysis, proposes the overall architecture of the system, and designs the scientific research results management module. The system is divided into two parts, namely, user information management module and result information management module. Second, the data protection module is designed. A trust-optimised consensus algorithm C-DPOS is introduced to provide basic technical support for the operation of the blockchain-based university science and technology results trading and knowledge sharing system, thereby reducing the probability of malicious nodes committing mischief in the blockchain network environment during the operation of knowledge sharing, and comparing the probability of malicious and normal nodes participating in consensus. Third, system implementation and testing. By comparing the difference in consensus latency between DPoS and the improved C-DPoS, it is concluded that C-DPoS has a greater operational speed advantage over DPoS. The implementation of the C-DPoS consensus algorithm can further ensure that blockchain-based result trading and knowledge sharing can operate stably. According to the Law on Promoting the Transformation of Scientific and Technological Achievements (2015 revised version), the transformation of scientific and technological achievements of universities refers to the activities of subsequent testing, development, application, and promotion of scientific and technological achievements until the formation of new technologies, new processes, new materials, and new products and the development of new industries to improve the productivity level. Through the development of the transformation management system of scientific and technological achievements, the research data can be summarised in real time and comprehensively, and the blockchain can be used to achieve data protection, which has the characteristics of comprehensive management functions and strong security and effectively improves the efficiency and information security of the transformation of scientific and technological achievements.

Ensuring Data Freshness for Blockchain-Enabled Monitoring Networks

The Age of Information (AoI) is a recently proposed metric for quantifying data freshness in real-time status monitoring systems, where timeliness is of importance. In this article, the problem of characterizing and controlling the AoI is studied in the context of blockchain-enabled monitoring networks (BeMNs). In BeMN, status updates from sources are transmitted and recorded in a blockchain. To investigate the statistical characteristics of the AoI in BeMN, the transmission latency and the consensus latency are first rigorously modeled. Then, the average AoI, the AoI violation probability, and the peak AoI violation probability are derived in a closed form so as to quantify the performance of BeMN. Furthermore, a simplified form is derived for the AoI violation probability, and it is shown that this quantity can capture the upper or lower bounds of the actual AoI violation probability. Simulation results show that each BeMN parameters (i.e., target successful transmission probability, block size, and timeout) can have conflicting effects on the AoI-related performance. Subsequently, design insights are provided to maintain the freshness of the status data in BeMN. Then, experimental results with a real Hyperledger Fabric platform further validate the accuracy of our modeling and analysis.

Fast and Scalable Distributed Consensus Over Wireless Large-Scale Internet of Things Network

Due to the rapid paradigm shift in Internet of Things networks from wired and centralized to flexible wireless and decentralized networks, building effective and reliable distributed consensus mechanisms over wireless is becoming essential. Especially, since the performance of consensus over communication endpoints in a large-scale wireless network is limited by their communication capability, it requires a careful co-design of communication and consensus to attain a fast and scalable distributed wireless consensus mechanism with high resiliency against faulty nodes. Within this context, this article addresses such problem by designing two wireless consensus mechanisms that well-suit in large-scale wireless networks. On the one hand, as a reinterpretation of the conventional referendum consensus (RC) in a large-scale wireless network, gossip-broadcasting-based RC (GB-RC) is proposed. On the other hand, to overcome the scalability issue of the GB-RC, cooperative-broadcast-based electoral-college consensus (CB-EC) is proposed. By mathematically analyzing the performance of both of the consensus mechanisms, in terms of consensus latency and resiliency against the faulty nodes, we show that the GB-RC outperforms the conventional RC, while the CB-EC significantly reduces the consensus latency compromising the stochastic resiliency. We further evaluate their performance numerically to show their effectiveness and feasibility under realistic large-scale wireless environments.

A fair consensus model in blockchain based on computational reputation

Fair selection of community members’ transactions in blockchain-based technologies, especially in finance, is essential and vital. Therefore, in this paper, a novel consensus model for fair selection of community members’ transactions through reputation models is presented as fair reputation-based consensus model (FRCM), so that all transactions of community members have the same weight and transactions are selected regardless of the transaction fee or the assets or computing power but based on the reputation of the trustees who are responsible for placing the transactions in the block. For this purpose, a FRCM prototype has been designed and implemented based on the proposed algorithms. Experimental and theoretical evaluations and simulation results show that the FRCM can select the transactions of community members fairly while reducing the average response time effectively. In addition, comparative analysis with other consensus mechanisms proved that FRCM model has lower consensus latency than other mechanisms, while improving throughput and scalability.

AWARE: Adaptive Wide-Area Replication for Fast and Resilient Byzantine Consensus

With upcoming blockchain infrastructures, world-spanning Byzantine consensus is getting practical and necessary. In geographically distributed systems, the pace at which consensus is achieved is limited by the heterogeneous latencies of connections between replicas. If deployed on a wide-area network, consensus-based systems benefit from weighted replication, an approach that utilizes extra replicas and assigns higher voting weights to well-connected replicas. This approach enables more choice in quorum formation and replicas can leverage proportionally smaller quorums to advance, thus decreasing consensus latency. However, the system needs a solution to autonomously adjust to its environment if network conditions change or faults occur. We present Adaptive Wide-Area REplication (AWARE), a mechanism that improves the geographical scalability of consensus with nodes being widely spread across the world. Essentially, AWARE is an automated and dynamic voting-weight tuning and leader positioning scheme, which supports the emergence of fast quorums in the system. It employs a reliable self-monitoring process and provides a prediction model seeking to minimize the system’s consensus latency. In experiments using several AWS EC2 regions, AWARE dynamically optimizes consensus latency by self-reliantly finding a fast configuration yielding latency gains observed by clients located across the globe.

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.

Electronic Health Records Sharing Model Based on Blockchain With Checkable State PBFT Consensus Algorithm

With the popularity of IoT devices and cloud technology in the medical industry. Sharing EHRs (Electronic Health Records) among medical institutions improves the accuracy of medical diagnosis and promotes the development of public medical. However, it is difficult to share EHRs among hospitals, and patients typically don’t know about the usage of their health records. In this paper, we propose a patient-controlled EHRs sharing scheme based on cloud computing collaborating blockchain technology. The medical abstract and the access strategy are stored in the blockchain to avoid being tampered with. To achieve the fine-grained access control, we propose the attribute-based encryption scheme and multi-keyword encryption scheme to encrypt EHRs. Moreover, we proposed a node-state-checkable Practical Byzantine Fault Tolerance consensus algorithm (sc-PBFT) to prevent the Byzantine nodes from sneaking into the consortium blockchain. First, we check the state of the elected master node to avoid the master node having any malicious records. Then, using pre-prepared, prepare, and commit processes to complete the consensus request submitted by the client. At last, the proposed consensus algorithm evaluates the state of the master node according to the completion of the three-stage process to reduce the impact of the malicious node on the whole consortium blockchain. By doing this, the malicious node will be marked and isolated into the isolation area. The experimental results show that the proposed sc-PBFT algorithm has better handling capability and lower consensus latency. Compared with the PBFT algorithm in the case of Byzantine nodes, sc-PBFT not only improves the robustness of the consortium blockchain network but also improves the handling capability.

MedShard: Electronic Health Record Sharing Using Blockchain Sharding

Electronic health records (EHRs) are important assets of the healthcare system and should be shared among medical practitioners to improve the accuracy and efficiency of diagnosis. Blockchain technology has been investigated and adopted in healthcare as a solution for EHR sharing while preserving privacy and security. Blockchain can revolutionize the healthcare system by providing a decentralized, distributed, immutable, and secure architecture. However, scalability has always been a bottleneck in blockchain networks due to the consensus mechanism and ledger replication to all network participants. Sharding helps address this issue by artificially partitioning the network into small groups termed shards and processing transactions parallelly while running consensus within each shard with a subset of blockchain nodes. Although this technique helps resolve issues related to scalability, cross-shard communication overhead can degrade network performance. This study proposes a transaction-based sharding technique wherein shards are formed on the basis of a patient’s previously visited health entities. Simulation results show that the proposed technique outperforms standard-based healthcare blockchain techniques in terms of the number of appointments processed, consensus latency, and throughput. The proposed technique eliminates cross-shard communication by forming complete shards based on “the need to participate” nodes per patient.

DR-BFT: A consensus algorithm for blockchain-based multi-layer data integrity framework in dynamic edge computing system

Edge computing, as a part a distributed computing architecture, has become an increasingly popular paradigm. It expands the capacity of cloud by facilitating data from the end devices to be stored and processed at the edge of the network closer to the data instead of delivering it to the cloud. Data integrity is a big concern in edge computing. As a promising solution to data integrity, blockchain is viable to protect the data in edge computing systems. However, most existing consensus algorithms cannot meet the requirements of edge computing in a dynamic network, where the nodes may join or leave the blockchain network dynamically. In this paper, we introduce a two-layer blockchain-based framework to provide data integrity in edge computing, and propose a novel Dynamic Random Byzantine Fault Tolerance (DR-BFT) consensus algorithm. DR-BFT consists of three sub-algorithms, String Consensus, Data Correctness Validation, and Binary Consensus. String consensus tries to reach consensus on the data of end devices or edge servers, and the sub-algorithm is based on an agreement and borrows some ideas from the Phase King Protocol. If the string consensus fails “early termination”, each node will agree on a value from a random primary node and go through data correctness validation sub-algorithm. The system then reaches consensus on the data with binary consensus sub-algorithm which is a variant of Ben-Or and Michael’s Random Consensus. We also propose an improved quorum method to cope with contention and dynamic node leaving/joining. We analyze DR-BFT with regard to consensus correctness, security, and system overhead. DR-BFT satisfies agreement, validity and termination. We conduct experiments through simulations. Experimental results show that the proposed consensus DR-BFT can effectively improve the performance in dynamic edge computing, including communication overhead and consensus latency.

Deep-Reinforcement-Learning-Based Latency Minimization in Edge Intelligence Over Vehicular Networks

A novel paradigm that combines federated learning with blockchain to empower edge intelligence over vehicular networks (FBVN) can enable latency-sensitive deep neural network-based applications to be executed in a distributed pattern. However, the complex environments in FBVN make the system latency much harder to minimize by traditional methods. In this article, we model the training and transmission latency of each autonomous vehicle (AV) and consensus latency of the blockchain in-edge side in FBVN. Considering the dynamic and time-varying wireless channel conditions, unpredictable packet error rate, and unstable data sets quality, we adopt duel deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning (DDQL) as the solving approach. We propose a federated DDQL algorithm, in which the learning agent is deployed on each AV side, and the sensing states on each AV do not need to be shared so that it increases scalability and flexibility for practical implementation. Simulation results show that the proposed algorithm has better performance in reducing system latency compared with the other schemes.

Communication and Consensus Co-Design for Distributed, Low-Latency, and Reliable Wireless Systems

Designing distributed, fast, and reliable wireless consensus protocols is instrumental in enabling mission-critical decentralized systems, such as robotic networks in the Industrial Internet of Things (IIoT), drone swarms in rescue missions, and so forth. However, chasing both low-latency and reliability of consensus protocols is a challenging task. The problem is aggravated under wireless connectivity that may be slower and less reliable, compared to wired connections. To tackle this issue, we investigate fundamental relationships between consensus latency and reliability through the lens of wireless connectivity, and co-design communication and consensus protocols for low-latency and reliable decentralized systems. Specifically, we propose a novel communication-efficient distributed consensus protocol, termed random representative consensus (R2C), and show its effectiveness under gossip and broadcast communication protocols. To this end, we derive a closed-form end-to-end (E2E) latency expression of the R2C that guarantees target reliability, and compare it with a baseline consensus protocol, referred to as referendum consensus (RC). The result shows that the R2C is faster compared to the RC and more reliable compared when co-designed with the broadcast protocol compared to that with the gossip protocol.

Deep Reinforcement Learning (DRL)-Based Device-to-Device (D2D) Caching With Blockchain and Mobile Edge Computing

Device-to-Device (D2D) caching assists Mobile Edge Computing (MEC) based caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes’ sharing willingness. In this paper, a Blockchain-based Cache and Delivery Market (CDM) is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and MEC caching nodes’ willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on transactions and prevent frauds, a consensus protocol among the smart contract execution nodes (SCENE) is necessary. To minimize the latency of reaching consensus while guaranteeing its confidence level, we propose partial Practical Byzantine Fault Tolerance (pPBFT) protocol. Further, the model of cache sharing and transaction execution consensus is proposed, and we further formulate caching placement and SCENE selection as Markov Decision Process problems. Due to the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problem. The simulation results show that the proposed schemes outperform conventional solutions in terms of traffic offloading, content retrieval latency, and consensus latency.

Hashgraph the Future of Decentralized Technology and the End of Blockchain

Hashgraph is data structure and consensus algorithm that is fast, with a very high throughput and low consensus latency, secure because of the asynchronous byzantine fault tolerant and fair due to the fairness of access, ordering, and timestamps. These properties enable new decentralized applications such as a stock market, improved collaborative applications, games, and auctions. Hashgraph is a new consensus protocol that has garnered attention lately by being projected as a technology that will make blockchains obsolete. Hashgraph currently scales only in the number of transactions processed but does not scale with the number of nodes in the network. Hashgraph will face the same issues that other public blockchains are facing today and may not be able to maintain its security and performance. In fact, scalability is still an open problem for public blockchains.

Consensus in Sparse, Mobile Ad Hoc Networks

Consensus is central to several applications including collaborative ones which a wireless ad hoc network can facilitate for mobile users in terrains with no infrastructure support for communication. We solve the consensus problem in a sparse network in which a node can at times have no other node in its wireless range and useful end-to-end connectivity between nodes can just be a temporary feature that emerges at arbitrary intervals of time for any given node pair. Efficient one-to-many dissemination, essential for consensus, now becomes a challenge; enough number of destinations cannot deliver a multicast unless nodes retain the multicast message for exercising opportunistic forwarding. Seeking to keep storage and bandwidth costs low, we propose two protocols. An eventually relinquishing (◇ RC) protocol that does not store messages for long is used for attempting at consensus, and an eventually quiescent (◇ QC) one that stops forwarding messages after a while is used for concluding consensus. Use of the ◇ RC protocol poses additional challenges for consensus, when the fraction, f/n, of nodes that can crash is 1/4 ≤ f/n <; 1/2. Consensus latency and packet overhead are measured through simulations and both decrease considerably even for a modest increase in network density.