DBPBFT:适用于物联网的双区块链分层 PBFT 共识算法

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-07-08 DOI:10.1016/j.future.2024.07.007
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引用次数: 0

摘要

物联网(IoT)由连接到网络的智能设备组成,这些设备可以与其他设备发送和接收大量数据,从而产生大量数据供处理和分析。由于区块链中的每笔交易都会被记录下来,放入一个数据块中,并添加到不可变的安全数据链中,因此区块链正成为增强物联网安全问题的最有前途的解决方案之一。随着越来越多的设备实现智能化,物联网系统(包括住宅物联网和工业物联网)的规模也在不断扩大。因此,因系统通信开销不断增加而产生的资源消耗问题日益突出。为了提高住宅物联网共识过程的效率,减少共识过程带来的开销,本文提出了一种分层 PBFT 共识算法--DBPBFT(Dual Blockchain for IoT)。与工业物联网相比,DBPBFT 更适用于范围小、数据分类清晰的居民物联网。DBPBFT 分离了双链的责任,提高了系统的可扩展性,同时也增强了区块链的安全性。一条链被划分为若干个小组,每个小组负责一类数据,从而减少了系统开销和通信开销。为了尽可能避免不必要的视图变更,在共识开始前,每个小组将根据声誉值选择当前视图的主节点。仿真结果表明,DBPBFT 算法优于传统算法。在减少通信开销方面,与 EPBFT 和 DPNPBFT 相比,DBPBFT 分别增加了 73.8% 和 53.1%。在共识效率方面,DBPBFT 比 DPNPBFT 提高了 34%。
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DBPBFT: A hierarchical PBFT consensus algorithm with dual blockchain for IoT

The Internet of Things (IoT) is composed of smart devices connected to a network that can send and receive large amounts of data with other devices, generating a lot of data for processing and analysis. Due to the fact that every transaction in blockchain is recorded, placed in a data block, and added to an immutable and secure data chain, blockchain is becoming one of the most promising solutions for enhancing IoT security issues. As more devices become intelligent, the scale of IoT systems, including residential IoT and industrial IoT, is on the rise. Consequently, the issue of resource consumption, stemming from the escalating system communication overhead, is becoming more pronounced. In order to improve the efficiency of the consensus process for residential IoT and reduce the overhead caused by the consensus process, this paper proposes a hierarchical PBFT consensus algorithm With Dual Blockchain for IoT (DBPBFT). Compared to industrial IoT, DBPBFT is more suitable for residential IoT with small scope and clear data classification. DBPBFT separates the responsibilities of dual chains, improving system scalability while also enhancing blockchain security. A chain is divided into several small groups, each responsible for a type of data, reducing system overhead and communication overhead. To avoid unnecessary view-change as much as possible, before consensus begins, each group will select the current view primary node based on reputation values. The simulation results show that the DBPBFT algorithm is superior to traditional algorithms. In terms of reducing communication overhead, compared with EPBFT and DPNPBFT, DBPBFT has increased by 73.8% and 53.1%, respectively. In terms of consensus efficiency, DBPBFT has improved by 34% compared to DPNPBFT.

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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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