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MTEC: A Multi-tier Blockchain Storage Framework Using Erasure Coding for IoT Application

  • Xinwei Xu
  • , Yongkang Yu
  • , Jinchun He
  • , Qinnan Zhang
  • , Hongwei Zheng*
  • , Binghui Guo*
  • , Jin Dong
  • , Wangjie Qiu
  • *此作品的通讯作者
  • Beihang University
  • Beijing Academy of Blockchain and Edge Computing
  • Zhongguancun Laboratory

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Blockchain technology is widely used in the Internet of Things (IoT) with its decentralization, traceability, and tamper-resistant features. Unfortunately, the massive data volumes in IoT systems, combined with the prevalent full-replication strategy employed by conventional blockchain, exert considerable storage demands on IoT edge computing devices and present a formidable challenge for resource-constrained nodes to join blockchain networks. Although some existing solutions improve storage scalability by reducing the number of redundant copies, they still pose issues of high storage complexity and significant cross-tier query costs. In this paper, we first propose a multi-tier storage framework using erasure coding for blockchain, called MTEC, tailored for IoT scenarios with an exponentially decaying data access pattern. Specifically, MTEC employs Reed-Solomon(RS) coding to build a tiered blockchain storage framework. Each tier is equipped with an encoding scheme and threshold time to minimize storage overhead. To enhance query efficiency, we design a block pre-loading strategy and node collaborative query strategy. Finally, we implement a prototype of MTEC and compare it with baseline blockchain systems. Extensive experiments demonstrate that MTEC achieves a substantial 86.3% decrease in storage overhead compared to conventional full-replication blockchain solutions. It further outperforms state-of-the-art MLDC scheme by an additional 15.8% storage optimisation and reduces the cost of cross-tier to 7.35%.

源语言英语
主期刊名Web and Big Data - 8th International Joint Conference, APWeb-WAIM 2024, Proceedings
编辑Wenjie Zhang, Zhengyi Yang, Xiaoyang Wang, Anthony Tung, Zhonglong Zheng, Hongjie Guo
出版商Springer Science and Business Media Deutschland GmbH
278-293
页数16
ISBN(印刷版)9789819772407
DOI
出版状态已出版 - 2024
活动8th Asia-Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data, APWeb-WAIM 2024 - Jinhua, 中国
期限: 30 8月 20241 9月 2024

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14964 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议8th Asia-Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data, APWeb-WAIM 2024
国家/地区中国
Jinhua
时期30/08/241/09/24

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