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A learning-based efficient query model for blockchain in internet of medical things

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Abstract

This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices.

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Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author, upon reasonable request.

Notes

  1. https://www.who.int/publications/i/item/9789241565257..

  2. https://github.com/hyperledger/fabric/tree/v0.6, Dec. 2021.

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Funding

This work is supported by the National Key Research and Development Program of China (Grant Nos. 2021YFB3300900, 2020YFE0201100 and 2022YFB4500800), the Artificial Intelligence Technology Innovation Project of Liaoning Province (Grant No. 2023JH26/10300019), the Funds of the National Natural Science Foundation of China (Grant Nos. 92267206, 61621004, U1908213 and 62072089), the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries (Grant No. 2018ZCX03), the Key Scientific Research Project of Liaoning Provincial Department of Education (Grant No. LZD202002), the Fundamental Research Funds for the Central Universities (Nos. N2116016, N2104001 and N2019007), the Open Program of Neusoft Corporation (No. NCBETOP2102), Ministry of Education Industry-University Cooperative Education Project (Grant No. 220701160215318), Program (No. JCKY2021211B017).

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Authors and Affiliations

  1. School of Information, Liaoning University, Shenyang, 110036, China

    Dayu Jia

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Guanghong Yang & Min Huang

  3. State Key Laboratory of Synthetical Automation of Process Industries, Northeastern University, Shenyang, 110819, China

    Guanghong Yang & Min Huang

  4. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Junchang Xin

  5. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Guoren Wang

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  1. Dayu Jia
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  2. Guanghong Yang
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Correspondence to Dayu Jia.

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Cite this article

Jia, D., Yang, G., Huang, M. et al. A learning-based efficient query model for blockchain in internet of medical things. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06106-9

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