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WSNs data acquisition by combining expected network coverage and clustered compressed sensing.

Zhouzhou Liu1, Yangmei Zhang2, Yang Bi2

  • 1School of Computer Science, Xihang University, Xi'an, China.

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|June 17, 2025
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Summary
This summary is machine-generated.

This study introduces a novel data collection scheme for wireless sensor networks (WSNs) using clustered compressive sensing (CS). The method reduces transmission overhead and improves data accuracy, extending network lifetime.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges with extensive data transmission and redundancy.
  • Efficient data collection is crucial for optimizing WSN performance and longevity.

Purpose of the Study:

  • To propose a novel data collection scheme for WSNs.
  • To reduce transmission overhead and enhance data accuracy in WSNs.
  • To extend the operational lifetime of WSNs.

Main Methods:

  • K-medoids clustering algorithm for node organization.
  • Optimized network coverage algorithm for node scheduling.
  • Clustered compressive sensing (CS) with a weakly correlated observation matrix.
  • Sparrow search algorithm for CS data reconstruction.

Main Results:

  • Significantly reduced WSN transmission overhead.
  • Ensured accurate recovery of raw data.
  • Decreased data reconstruction error.
  • Extended network lifetime.

Conclusions:

  • The proposed scheme effectively addresses data transmission and redundancy challenges in WSNs.
  • The integration of clustered CS and optimized coverage enhances WSN efficiency and longevity.
  • This approach offers a promising solution for advanced WSN data collection.