Data management scheme for building internet of things based on blockchain sharding

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Intelligent Data Analysis Pub Date : 2023-08-10 DOI:10.3233/ida-220757

Xu Wang, Wenhu Zheng, Jinlong Wang, Xiaoyun Xiong, Yumin Shen, Wei Mu, Zengliang Fan

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引用次数: 0

Abstract

As an important part of digital building, building internet of things (BIoT) plays a positive role in promoting the construction of smart cities. Existing schemes utilize blockchain to achieve trusted data storage in BIoT. However, the full-copy storage mechanism of blockchain and the management requirements of massive data have brought computing and storage challenges to edge nodes with limited resources. Therefore, a data management scheme for BIoT based on blockchain sharding is proposed. The scheme proposes a hybrid storage mechanism, which uses inter-planetary file system (IPFS) to ensure the integrity and availability of data outside the chain, and reduces the storage overhead of edge nodes. Based on the hybrid storage mechanism, the sharding algorithm is designed to divide the blockchain into multiple shards, and the storage overhead and computing overhead are offloaded to each shard, which effectively balances the computing and storage overhead of edge nodes. Finally, comparative analysis was made with existing schemes, and effectiveness of proposed scheme was verified from the perspectives of storage overhead, computation overhead, access delay and throughput. Results show that proposed scheme can effectively reduce storage overhead and computing overhead of edge nodes in BIoT scenario.

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基于区块链分片构建物联网的数据管理方案

建筑物联网(BIoT)作为数字化建筑的重要组成部分，对智慧城市建设具有积极的推动作用。现有方案利用区块链在BIoT中实现可信数据存储。然而，区块链的全复制存储机制和海量数据的管理需求，给资源有限的边缘节点带来了计算和存储方面的挑战。为此，提出了一种基于区块链分片的BIoT数据管理方案。该方案提出了一种混合存储机制，利用星际文件系统(IPFS)保证链外数据的完整性和可用性，降低了边缘节点的存储开销。基于混合存储机制，分片算法将区块链划分为多个分片，将存储开销和计算开销分散到每个分片上，有效平衡边缘节点的计算开销和存储开销。最后，与现有方案进行对比分析，从存储开销、计算开销、访问延迟和吞吐量等方面验证了所提方案的有效性。结果表明，该方案可以有效降低边缘节点在BIoT场景下的存储开销和计算开销。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Data Analysis 工程技术-计算机：人工智能

CiteScore

2.20

自引率

5.90%

发文量

审稿时长

3.3 months

期刊介绍： Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.