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Coalition Formation Based Compressive Sensing in Wireless Sensor Networks.

Alireza Masoum1, Nirvana Meratnia2, Paul J M Havinga3

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Summary
This summary is machine-generated.

This study introduces a distributed compressive sensing method for wireless sensor networks. It enhances energy efficiency and data accuracy by grouping nodes into coalitions for localized sensing.

Keywords:
belief propagationcoalitioncompressive sensingjoint sparse recoverysparsity

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Area of Science:

  • Signal Processing
  • Wireless Sensor Networks
  • Data Gathering

Background:

  • Compressive sensing is increasingly applied to energy-efficient data gathering in wireless sensor networks.
  • Existing methods face challenges in balancing data accuracy and energy consumption.

Purpose of the Study:

  • To propose an energy-efficient distributed compressive sensing solution for sensor networks.
  • To improve data gathering performance by optimizing energy consumption and data accuracy.

Main Methods:

  • Utilizing signal sparsity to form sensor node coalitions.
  • Implementing localized compressive sensing within these coalitions.
  • Optimizing data transmission costs and reducing the number of measurements through node scheduling and adaptive sampling frequency.

Main Results:

  • Demonstrated significant improvements in network performance.
  • Achieved minimized energy costs for data gathering.
  • Enhanced overall data accuracy.

Conclusions:

  • The proposed coalition-based distributed compressive sensing approach effectively enhances energy efficiency and data accuracy in wireless sensor networks.
  • This method offers a viable solution for reducing data transmission costs and measurement overhead.