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A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless

Xiangping Gu1,2, Xiaofeng Zhou3, Yanjing Sun4

  • 1College of Computer and Information, Hohai University, Naijing 210098, China. xpgu2010@hyit.edu.cn.

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Summary

This study introduces a novel compressive sensing (CS) method using modified diffusion wavelets for wireless sensor networks (WSNs). The approach significantly cuts energy use and balances node load, extending network lifespan.

Keywords:
ant colony algorithmcompressive sensingdata gatheringdiffusion waveletswireless sensor networks (WSNs)

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Area of Science:

  • Wireless Sensor Networks
  • Signal Processing
  • Network Optimization

Background:

  • Compressive sensing (CS) reduces energy in wireless sensor networks (WSNs) but often requires many nodes, increasing consumption and unbalancing loads.
  • Existing CS methods in WSNs lack efficiency and load balancing, limiting network lifetime.

Purpose of the Study:

  • To develop a novel CS-based data gathering scheme for WSNs that enhances energy efficiency and load balancing.
  • To improve data reconstruction performance and network longevity through optimized routing and CS techniques.

Main Methods:

  • A modified diffusion wavelet transform is employed to exploit spatial correlations in sensor readings, enabling sparser signal representation.
  • A joint routing and CS scheme is proposed, utilizing a modified ant colony algorithm that considers residual energy and path length for node selection.
  • An improved pheromone impact factor is introduced to enhance coverage rate and avoid algorithm local optima.

Main Results:

  • Theoretical proof confirms the generated sensing matrix satisfies the restricted isometric property (RIP).
  • Modified diffusion wavelets demonstrate superior signal sparsity and better reconstruction performance compared to Discrete Fourier Transform (DFT).
  • The proposed joint routing and CS scheme significantly reduces WSN energy consumption and balances network load.

Conclusions:

  • The novel CS approach with modified diffusion wavelets and joint routing effectively reduces energy consumption in WSNs.
  • The method achieves better load balancing and prolongs network lifetime compared to existing CS-based techniques.
  • This research offers a promising solution for efficient and sustainable data gathering in WSNs.