JOURNAL ARTICLE

Research on Dynamic Optimization of Industrial Internet of Things Data Transmission Based on BlockChain.

  • Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xia, Zhengpu 3 of 3

Abstract

The existing data transmission methods for industrial Internet of Things (IoT) ignore the tracking of abnormal nodes in the blockchain network, resulting in a low success rate of data transmission in industrial IoT, easy data loss, and low transmission efficiency. Therefore, a blockchain-based optimization method for industrial IoT data transmission is proposed. A B-Markov positioning model is constructed using neural networks to map IoT to consortium chains, and smart contracts are designed to eliminate abnormal nodes. Through dynamic optimization algorithms and centralized transmission models, combined with asymmetric encryption of blockchain, efficient data transmission is achieved. Experimental results show that this method has short-time consumption, high integrity, and a high success rate. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of High Speed Electronics & Systems. 2025/12, Vol. 34, Issue 4, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0129-1564
  • DOI:10.1142/S0129156425403493
  • Accession Number:186254848
  • Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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