Massive Data Connection Architecture Design of Web Platform Based on Blockchain

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongping Cao, Hongxing Cao, Hongwei Zhou, Yifeng Wu, Siyu Xu
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引用次数: 0

Abstract

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era.
基于区块链的Web平台海量数据连接架构设计
为了提高物联网数据管理的通用性和安全性,避免传统管理架构中的单点故障问题,本文提出了一种基于区块链的海量连接管理架构。根据区块链技术的分层结构对区块链进行重构,并参考基于属性的访问控制机制,采用八卦模式和TLS组网模式设计数据交换模型,保证数据的安全传输。采用对等计算机网络技术、数据签名、共识机制,完成数据交换网络层的设计,实现基于区块链的海量连接管理架构的设计,有效避免了传统管理架构中的单点故障问题,大大减轻了管理中心的负担。对5G应用场景下海量设备的访问请求进行仿真,结果表明,本文设计的基于区块链的访问控制架构能够快速处理收到的请求,信息泄漏率低于10%,提高了数据连接的安全性和处理性能,实现了大规模数据的高效处理和管理,为互联网时代的发展提供了可靠保障。
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
89
期刊介绍: JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.
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