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Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

Cuicui Lv1, Linchuang Yang1, Xinxin Zhang2

  • 1School of Computer and Control Engineering, Yantai University, Yantai 264005, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces a novel unmanned aerial vehicle (UAV)-assisted algorithm using data compression and compressive sensing (CS) to reduce energy consumption in wireless sensor networks (WSNs). The method efficiently gathers and reconstructs environmental data, lowering power demands for sensor nodes.

Keywords:
data compressionenvironmental monitoringunmanned aerial vehiclewireless sensor networks

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Area of Science:

  • Environmental monitoring
  • Wireless Sensor Networks (WSNs)
  • Unmanned Aerial Vehicle (UAV) technology

Background:

  • Increasing environmental data volumes strain WSNs with limited storage and battery power.
  • High energy consumption for data transmission is a critical challenge in WSNs.
  • Efficient data handling and reduced energy usage are paramount for sustainable WSN operation.

Purpose of the Study:

  • To develop an energy-efficient data acquisition algorithm for WSNs facing large data volumes.
  • To reduce the energy consumption of sensor nodes through data compression and optimized data gathering.
  • To leverage UAVs as mobile base stations for effective data collection in WSNs.

Main Methods:

  • Employed data compression to eliminate redundant environmental data, reducing sensor node energy use.
  • Introduced compressive sensing (CS) to decrease network data volume.
  • Developed a UAV-assisted compressed data acquisition algorithm utilizing an optimized greedy routing strategy.

Main Results:

  • The proposed UAV-assisted algorithm demonstrated significantly lower energy consumption compared to existing methods.
  • Experimental results confirmed the algorithm's effectiveness in reducing energy demands for WSN data transmission.
  • Various data reconstruction algorithms were tested, showing efficient data recovery in shorter times.

Conclusions:

  • The integration of data compression and CS with UAV-based data collection offers a viable solution for energy-efficient WSNs.
  • The optimized greedy algorithm enhances UAV data gathering efficiency.
  • The approach effectively balances data volume reduction with accurate sensory data reconstruction.