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A Practical Data-Gathering Algorithm for Lossy Wireless Sensor Networks Employing Distributed Data Storage and

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  • 1National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China. cezhang@foxmail.com.

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Summary
This summary is machine-generated.

This study introduces a compressive sensing (CS) and distributed data storage (DDS) scheme for wireless sensor networks (WSNs). It balances energy efficiency and reliability in lossy networks, reducing transmissions and improving data accuracy.

Keywords:
CSWSNsdistributed data storageenergy efficiencypacket loss rate

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Compressive sensing (CS) in wireless sensor networks (WSNs) often overlooks packet loss, prioritizing energy efficiency over reliability.
  • Existing CS schemes struggle to maintain data accuracy in lossy WSN environments.

Purpose of the Study:

  • To introduce a novel compressive sensing and distributed data storage (CS-DDSG) scheme for WSNs.
  • To balance the trade-off between performance and energy consumption in lossy WSNs.
  • To enhance data gathering reliability and energy efficiency.

Main Methods:

  • The proposed CS-DDSG scheme integrates CS with distributed data storage (DDS).
  • It leverages broadcast properties to mitigate packet loss impacts and imposes process constraints to reduce transmission and reception volumes.
  • A mobile sink randomly queries nodes and constructs a measurement matrix to avoid lossy nodes, ensuring the restricted isometry property is met.

Main Results:

  • The scheme demonstrates high reconstruction accuracy even with unreliable links.
  • It significantly reduces the total number of transmissions, receptions, and data fusions.
  • Analysis using random geometric graph theory provides an expression for transmissions and receptions.

Conclusions:

  • The CS-DDSG approach effectively balances energy consumption and reconstruction accuracy in lossy WSNs.
  • It offers a robust solution for reliable data gathering in challenging network conditions.
  • This method enhances the practicality of CS-based data gathering in real-world WSN deployments.