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Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression.

Shaoming Qiu1, Ao Li1

  • 1Communication and Network Laboratory, Dalian University, Dalian 116622, China.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces a novel data compression algorithm, the chaotic mutation adaptive sparrow search algorithm (CMASSA), to improve edge computing efficiency. CMASSA enhances data classification accuracy and reconstruction quality while reducing data loss during compression.

Keywords:
chaotic adaptive sparrow search algorithmcomputer application technologyconvolutional auto-encoder networkdata compressionedge computinghyperparameter optimization

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Area of Science:

  • Edge Computing
  • Data Compression
  • Artificial Intelligence

Background:

  • Edge servers generate large datasets, posing challenges for traditional compression methods.
  • Existing compression techniques often lead to reduced data classification accuracy and significant data loss.

Purpose of the Study:

  • To propose an advanced data compression algorithm for edge computing environments.
  • To optimize Convolutional Auto-Encoder Network (CAEN) hyperparameters for efficient data compression.

Main Methods:

  • Developed a chaotic mutation adaptive sparrow search algorithm (CMASSA).
  • Utilized a novel fitness function to optimize CAEN hyperparameters on a cloud service center.
  • Deployed the optimized CAEN model on edge servers for data compression.

Main Results:

  • CMASSA demonstrated superior performance on ten high-dimensional benchmark functions compared to other algorithms.
  • Experiments on the Multi-class Weather Dataset (MWD) showed CMASSA achieved better classification accuracy.
  • The algorithm maintained a high degree of data reconstruction while ensuring a significant compression ratio.

Conclusions:

  • CMASSA offers an effective solution for data compression in edge computing, balancing compression ratio with data integrity.
  • The proposed method significantly improves classification accuracy and data reconstruction quality over existing approaches.