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Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm.

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A new deep learning algorithm, CNNMDA, enhances wireless sensor network data fusion efficiency and accuracy. It reduces energy consumption and transmission, prolonging network life while optimizing parallel processing.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Traditional data fusion algorithms in wireless sensor networks (WSNs) suffer from low efficiency and struggle with high-dimensional data.
  • Parameter synchronization in synchronous parallel processing can be time-consuming, hindering overall performance.

Purpose of the Study:

  • To propose a novel deep learning-based algorithm (CNNMDA) for efficient and accurate data fusion in WSNs.
  • To introduce a dynamic training data allocation algorithm to optimize multimachine synchronous parallel processing.

Main Methods:

  • Developed a Convolutional Neural Network (CNN) feature extraction model (CNNM) for data processing at the sink and terminal nodes.
  • Implemented a dynamic data allocation strategy adjusting sample data based on node computing efficiency during parallel processing.

Main Results:

  • CNNMDA significantly reduces network energy consumption and data transmission compared to similar algorithms.
  • The dynamic allocation algorithm minimizes waiting times for gradient updates in parallel processing, improving synchronization efficiency.
  • Experimental validation on the Tianhe-2 supercomputer confirmed the effectiveness of the proposed optimization mechanism.

Conclusions:

  • The CNNMDA algorithm offers a superior approach to data fusion in WSNs, enhancing efficiency, accuracy, and network longevity.
  • The dynamic training data allocation effectively addresses parameter synchronization bottlenecks in large-scale parallel computing environments.