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Blockchain enhances edge AI computing by integrating K-means for fault prediction and secure resource sharing. This approach improves efficiency and trust in decentralized systems.

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Area of Science:

  • Decentralized Systems
  • Artificial Intelligence
  • Blockchain Technology

Background:

  • Edge AI computing faces challenges in efficiency, scalability, and trust among distributed devices.
  • Blockchain offers immutability, traceability, and consensus mechanisms crucial for decentralized systems.
  • Integrating idle computing resources forms distributed platforms, necessitating secure identity and resource management.

Purpose of the Study:

  • To explore the integration of blockchain and K-means for edge AI computing.
  • To establish a secure and trustworthy framework for decentralized edge AI resource sharing.
  • To develop an efficient K-means algorithm for multi-data fault prediction in edge environments.

Main Methods:

  • Utilizing blockchain's distributed ledger for secure identity registration and transaction management.
  • Implementing a K-means algorithm optimized for edge environments to predict equipment degradation stages using multi-type data.
  • Designing a blockchain-based resource allocation scheme with legality verification.
  • Introducing a control node for global network information to support user decisions.

Main Results:

  • The proposed K-means algorithm demonstrated an average energy consumption reduction of 14.6% compared to the Genetic Algorithm (GA).
  • Blockchain integration successfully established trust and security in the decentralized edge AI platform.
  • A verifiable resource allocation scheme was implemented, ensuring reliable resource utilization.

Conclusions:

  • Blockchain technology provides a robust solution for trust and security in edge AI computing.
  • The optimized K-means algorithm effectively predicts equipment faults using diverse data sources.
  • The research presents a viable framework for efficient and secure decentralized edge AI platforms.