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Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks.

Wei Liu1, Yu Xia1, Daqing Zheng1

  • 1School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China.

Sensors (Basel, Switzerland)
|September 22, 2020
PubMed
Summary

Hardware-based link quality estimators (LQEs) in wireless sensor networks perform differently across environments. Link Quality Indicator (LQI) and Signal-to-Noise Ratio (SNR) based LQEs offer the best environmental adaptability for reliable packet reception ratio estimation.

Keywords:
communication distanceenvironmental impactlink quality estimationlink quality indicatorphysical layer parametersreceived signal strength indicatorsignal-to-noise ratiowireless sensor networks

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Area of Science:

  • Wireless Sensor Networks
  • Network Performance Monitoring
  • Physical Layer Communications

Background:

  • Hardware-based link quality estimators (LQEs) in wireless sensor networks (WSNs) leverage physical layer parameters for packet reception ratio (PRR) estimation.
  • Existing LQEs often overlook environmental impacts on their performance and applicability.
  • Current Signal-to-Noise Ratio (SNR) calculation methods in WSN studies may be problematic.

Purpose of the Study:

  • To compare the performance of typical hardware-based LQEs under diverse environmental conditions.
  • To propose a more accurate method for calculating SNR in WSNs.
  • To evaluate the environmental adaptability and accuracy of different LQEs.

Main Methods:

  • Comparative analysis of hardware-based LQEs (RSSI, SNR, LQI) in various environments.
  • Development and application of a refined SNR calculation methodology.
  • Assessment of PRR estimation accuracy against communication distance and environmental changes.

Main Results:

  • Estimating PRR solely by communication distance is inaccurate and environment-dependent.
  • Received Signal Strength Indicator (RSSI) based LQEs performance degrades with environmental changes, primarily due to background noise variations.
  • Link Quality Indicator (LQI) and SNR based LQEs demonstrate superior environmental adaptability, remaining largely unaffected by environmental shifts. LQI offers higher accuracy in transitional regions but requires larger smoothing windows due to its wider fluctuation range.

Conclusions:

  • LQI and SNR based LQEs provide the most robust performance across different environments in wireless sensor networks.
  • While LQI offers better accuracy in transitional zones, its vendor-specific nature and wider fluctuations necessitate careful consideration of smoothing.
  • The choice of LQE involves a trade-off between accuracy, agility, and implementation convenience, with SNR and LQI being the preferred metrics for environmental resilience.