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Research on PoW Mining Flow Monitoring Based on Multidimensional Feature Analysis

Published: 29 July 2024 Publication History

Abstract

In blockchain networks, the main purpose of proof of work (PoW) algorithms is to select the next block that is added to blockchain and ensure that nodes on blockchain can reach consensus, also it consumes a large amount of computing resources to modify data that has already been added to the blockchain. Therefore, the blockchain networks based on workload proof algorithms require a lot of consumption, So we propose a multidimensional feature analysis method for PoW mining monitoring. In this method, we integrate mining pool protocol, mining instructions, currency, pool communication, mining software model, account, computing power, energy consumption and other information which are obtained through experience and knowledge calculation and analysis. Through this method we can achieve better effect on currencies based on PoW. Finally, we analyze the proposed solution on BTC, ETHW. The analysis results show that our proposed scheme can effectively improve the accuracy of PoW mining flow monitoring.

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CNIOT '24: Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things
May 2024
668 pages
ISBN:9798400716751
DOI:10.1145/3670105
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2024

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Author Tags

  1. mining flow monitoring
  2. multidimensional feature analysis
  3. proof of work

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CNIOT 2024

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Overall Acceptance Rate 39 of 82 submissions, 48%

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