Consensus and agreement algorithms

Data security is a major issue for smart home networks. Yet, different existing tools and techniques have not been proven highly effective for home networks’ data security. Blockchain is a promising technology because of the distributed computing infrastructure network that makes it difficult for hackers to intrude into the systems through the use of cryptographic signatures and smart contracts. In this paper, an architecture for smart home networks that could guarantee data integrity, robust security, and the ability to protect the validity of the blockchain transactions has been investigated. The system model is tested using various sizes of realistic datasets (30, 3 k, and 30 k to represent a small, medium, and large number of transactions, respectively). Four different consensus algorithms were considered, the conventional schemes concatenated hash transactions (CHT) and Merkle hash tree (MHT), as well as the newly proposed odd and even modified MHT (O&E MHT) and modified MHT (MMHT). Moreover, 15 hash functions were also examined and compared to understand the effects of each consensus algorithms on the data integrity verification check execution time and the time optimization provided by the proposed MMHT algorithm. The results show that even though the CHT algorithm gives the lowest execution time, it is impractical for a blockchain implementation due to the requirement to copy the entire blockchain ledger in real time. Meanwhile, the O&E MHT does not give any tangible benefit in the execution time. However, the proposed MMHT offers a minimum of 30% gain in time optimization than the conventional MHT algorithm typically used in blockchains. This work shows that the proposed MMHT consensus algorithm not only can identify malicious codes but has an improved data integrity check performance in smart homes, all while ensuring network stability.

Cryptocurrencies have seen a massive surge in popularity and behind these new virtual currencies is an innovative technology called the blockchain: a distributed digital ledger in which cryptocurrency transactions are recorded after having been verified. The transactions within a ledger are verified by multiple clients or “validators,” within the cryptocurrency's peer-to-peer network using one of many varied consensus algorithms for resolving the problem of reliability in a network involving multiple unreliable nodes. The most widely used consensus algorithms are the Proof of Work (PoW) algorithm and the Proof of Stake (PoS) algorithm; however, there are also other consensus algorithms which utilize alternative implementations of PoW and PoS, as well as other hybrid implementations and some altogether new consensus strategies. In this paper, we perform a comparative analysis of typical consensus algorithms and some of their contemporaries that are currently in use in modern blockchains. Our analysis focuses on the algorithmic steps taken by each consensus algorithm, the scalability of the algorithm, the method the algorithm rewards validators for their time spent verifying blocks, and the security risks present within the algorithm. Finally, we present our conclusion and some possible future trends for consensus algorithms used in blockchains.

In previous work, it has been shown how to solve atomic broadcast by reduction to consensus on messages. While this solution is theoretically correct, it has its limitations in practice, since executing consensus on large messages can quickly saturate the system. The problem can be addressed by executing consensus on message identifiers instead of the full messages, in order to decouple the size of the messages from the size of the data sent by the consensus algorithm. In this paper, we study the impact of executing consensus on message identifiers instead of on the full messages, in the context of solving atomic broadcast. We also discuss the implications of executing consensus on message identifiers on the consensus and atomic broadcast algorithms.

Multitask diffusion and consensus algorithms have been proposed to solve distributed optimization problems in real time from streaming data, where the nodes cooperate to estimate node-dependent parameters. A fundamental question motivating their development is: how do standard diffusion LMS and real time consensus (RTC) algorithms perform in such environments? Previously, only moment stability has been studied via energy conservation arguments which require unrealistic white regressor assumptions. We present realization-wise stability results for the first time, under both correlated regressors and noise. This allows us to give an explicit expression for the limit point of these algorithms in the slow adaptation regime. We see that the Adapt-Then-Combine (ATC), Combine-Then-Adapt (CTA) and RTC algorithms all converge to a common Pareto optimal solution of the distributed MMSE problem.

Aiming at the performance bottleneck problem and insufficient scalability caused by consensus algorithm in supply chain financing application scenarios, this paper proposes a Multi-Raft consensus algorithm for improving the Raft consensus algorithm. In the Multi-Raft consensus algorithm, we introduce the contractor election algorithm. Contractor nodes are selected by the KNN algorithm in machine learning. The optimized Multi-Raft consensus algorithm is applied to the Easy-Visible supply chain financing platform for testing. By analyzing the experimental results, it can be seen that the optimized Multi-Raft consensus algorithm has significant improvements in throughput, delay and scalability compared with the original Raft consensus algorithm. The research in this paper solves the problems of performance bottleneck and low scalability of the consensus algorithm of the alliance chain in practical applications. A solution is proposed for the large-scale implementation of the enterprise-level alliance chain. It has certain engineering value and application value.

This article was published in the Eurasip Journal on Wireless Communications and Networking [©2016 Published by Springer International Publishing.] and the definite version is available at: http://dx.doi.org/10.1186/s13638-016-0698-x
\n. The article website is at:http://jwcn.eurasipjournals.springeropen.com/articles/10.1186/s13638-016-0698-x

Blockchain with these characteristics of decentralized structure, transparent and credible, time-series and immutability, has been considering as a promising technology. Consensus algorithm as one of the core techniques of blockchain directly affects the scalability of blockchain systems. Existing probabilistic finality blockchain consensus algorithms such as PoW, PoS, suffer from power consumptions and low efficiency; while absolute finality blockchain consensus algorithms such as PBFT, HoneyBadgerBFT, could not meet the scalability requirement in a large-scale network. In this paper, we propose a novel optimized practical Byzantine fault tolerance consensus algorithm based on EigenTrust model, namely T-PBFT, which is a multi-stage consensus algorithm. It evaluates node trust by the transactions between nodes so that the high quality of nodes in the network will be selected to construct a consensus group. To reduce the probability of view change, we propose to replace a single primary node with a primary group. By group signature and mutual supervision, we can enhance the robustness of the primary group further. Finally, we analyze T-PBFT and compare it with the other Byzantine fault tolerant consensus algorithms. Theoretical analysis shows that our T-PBFT can optimize the Byzantine fault-tolerant rate, reduce the probability of view change and communication complexity.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

As a distributed shared ledger and database technology, blockchain has the characteristics of decentralization, tamper-proof, openness and transparency. With the continuous development of blockchain technology, the technology has received high attention from government departments, finance, technology and other industries. The key technology of blockchain is the design of consensus algorithm, which affects the overall efficiency and performance of the blockchain system. How to achieve consistency and efficiency among nodes in a simple and efficient blockchain distributed system. This paper divides consensus algorithms into CFT and BFT according to fault tolerance types, and analyzes the basic principles and consensus process of each algorithm in detail. In five aspects of block generation speed, degree of decentralization, security, consistency and resource consumption, the mainstream consensus algorithms in the blockchain are compared and analyzed, the advantages and disadvantages of each consensus algorithm are summarized. Finally, the future blockchain consensus algorithms are summarized and prospected.

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.

Teegraph: A Blockchain consensus algorithm based on TEE and DAG for data sharing in IoT

This chapter theoretically and experimentally analyzes the consensus algorithms in blockchains. Consensus is one of the key problems in blockchains, and there are several candidate consensus algorithms for public and consortium/private blockchains with different security and performance levels.The first part of this chapter analyzes the proof‐of‐work (PoW) consensus algorithm. In PoW‐based public blockchains, miners can conduct different mining strategies to selfishly increase their own revenues. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining and conducting some malicious mining strategies. In particular, the mining problem can be formulated as a Markov decision process (MDP), which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model‐based solution. In the first part of this chapter, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multi‐dimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multi‐dimensional RL mining algorithm can still achieve optimal performance over time‐varying blockchain networks.The second part of this chapter analyzes the Raft consensus algorithm that is usually adopted in the consortium/private blockchains. There are many articles analyzing the performance of threat models for blockchains. However, the issue of network stability has not received much attention, which in fact affects the blockchain performance. This part studies the performance of Raft in networks with non‐negligible packet loss rate. In particular, we propose a simple but accurate analytical model to analyze the distributed network split probability. At a given time, we explicitly present the network split probability as a function of the network size, the packet loss rate, and the election timeout period. To validate our analysis, we implement a Raft simulator and the simulation results coincide with the analytical results. With the proposed model, one can predict the network split time and probability in theory and optimize the parameters in Raft consensus algorithm.

The blockchain technology revolution and the use of blockchains in various applications have resulted in many companies and programmers developing and customizing specific fit-for-purpose consensus algorithms. Security and performance are determined by the chosen consensus algorithm; hence, the reliability and security of these algorithms must be assured and tested, which requires an understanding of all the security assumptions that make such algorithms correct and byzantine fault-tolerant.This paper studies the "security ingredients" that enable a given consensus algorithm to achieve safety, liveness, and byzantine fault tolerance (BFT) in both permissioned and permissionless blockchain systems. The key contributions of this paper are the organization of these requirements and a new taxonomy that describes the requirements for security. The CAP Theorem is utilized to explain important tradeoffs between consistency and availability in consensus algorithm design, which are crucial depending on the specific application of a given algorithm. This topic has also been explored previously by De Angelis. However, this paper expands that prior explanation and dilemma of consistency vs. availability and then combines this with Buterin's Trilemma to complete the overall exposition of tradeoffs.

INTRODUCTION: Blockchain technology has gained significant attention across various sectors as a distributed ledger solution. To comprehend its applicability and potential, a comprehensive understanding of blockchain's essential elements, functional traits, and architectural design is imperative. Consensus algorithms play a critical role in ensuring the proper operation and security of blockchain networks. Consensus algorithms play a vital role in maintaining the proper operation of a blockchain network, and their selection is crucial for optimal performance and security. OBJECTIVES: The objective of this research is to analyse and compare various consensus algorithms based on their performance and efficiency in mining blocks. METHODS: To achieve this, an experimental model was developed to measure the number of mined blocks over time for different consensus algorithms. RESULTS: The results provide valuable insights into the effectiveness and scalability of these algorithms. The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. CONCLUSION: The findings of this study contribute to the understanding of consensus algorithm selection and its impact on the overall performance of blockchain systems. By enhancing our knowledge of consensus algorithms, this research aims to facilitate the development of more secure and efficient blockchain applications.

The article investigates the process of block formation in blockchain networks and the impact of node network architecture and consensus algorithms on their scalability and performance. Analysis of blockchain system scalability is important due to problems that arise when network load increases, particularly the increase in the number of block forks and transaction confirmation times. The research focuses on studying the impact of network delays and the choice of consensus algorithm on the performance and scalability of blockchain networks. The main attention is devoted to mathematical models that describe block formation, as well as the analysis of factors affecting transaction processing speed and throughput. The primary consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), are considered, and their impact on scalability in implementations based on the Ethereum Virtual Machine (EVM) and Bitcoin is compared. Experimental studies using Geth and Amazon cloud services revealed that the application of the Proof of Stake (PoS) consensus algorithm increases network performance by reducing the complexity of the block formation process in blockchain networks by 99% and accelerates consensus achievement by 70% compared to Proof of Work (PoW). It was also established that increasing the number of nodes from 5 to 50 reduces the network's throughput by almost 10%, and the average confirmation time doubles. The obtained results are aimed at solving the scalability issue by reducing transaction confirmation times for the implementation of decentralized technologies in the Internet of Things (IoT) sphere, where processing speed and storage of large volumes of data are critically important. Keywords: blockchain, block formation, consensus algorithms, decentralized technologies, Ethereum Virtual Machine (EVM), Internet of Things (IoT), mathematical modeling, network delays, scalability.