基于区块链技术的边缘计算网络数据完整性保障方法研究

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
I. R. Fedorov, A. A. Kovalev
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引用次数: 0

摘要

鉴于边缘计算的快速发展和连接设备数量的增加,本文解决了确保数据完整性的紧迫问题。重点是区块链技术的使用,它为解决这个问题提供了独特的机会。本文简要介绍了现有的完整性保障方法,并提出了一种基于区块链技术的完整性保障新方法。提出了在多活动节点情况下使用Hot Stuff共识算法,保证了系统的快速运行和可扩展性。特别注意使用基于以太坊的辅助区块链来提供额外的安全性并在大型区块链网络中创建检查点。这使得简化边缘服务器的工作成为可能,并确保根据用户的请求使用智能合约进行数据完整性验证。所提出的确保数据完整性和使用区块链技术的新方法不仅可以应用于边缘计算领域,还可以应用于物联网的各种变体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of a Method for Ensuring Data Integrity in Edge Computing Networks Based on Blockchain Technology

Development of a Method for Ensuring Data Integrity in Edge Computing Networks Based on Blockchain Technology

In light of the rapid development of edge computing and the increase in the number of connected devices, this paper addresses the pressing issue of ensuring data integrity. The main focus is on the use of blockchain technology, which offers unique opportunities to solve this problem. This paper provides a brief overview of the existing methods and a new method for ensuring integrity based on blockchain technology. It is proposed to use the Hot Stuff consensus algorithm in the case of multiple active nodes, which ensures fast operation and scalability of the system. Particular attention is paid to the use of a secondary blockchain based on Ethereum to provide additional security and create checkpoints in large blockchain networks. This makes it possible to simplify the work of edge servers and ensure data integrity verification upon the user’s request using smart contracts. The proposed new approaches to ensure data integrity and use blockchain technology can be applied not only in the field of edge computing but also in various variations of the Internet of Things.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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