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An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions.

Sebastián Gutiérrez1, Hiram Ponce2

  • 1Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes, Aguascalientes 20290, Mexico. jsgutierrez@up.edu.mx.

Sensors (Basel, Switzerland)
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces artificial hydrocarbon networks (AHN) for wireless sensor networks (WSN). AHN accurately detects, identifies, and accommodates sensor failures, improving data reliability in remote monitoring.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Data Science

Background:

  • Wireless sensor networks (WSN) are susceptible to data inaccuracies from environmental factors and sensor degradation.
  • Effective functioning of WSNs necessitates intelligent failure detection mechanisms.

Purpose of the Study:

  • To propose and validate a supervised learning method, Artificial Hydrocarbon Networks (AHN), for predicting remote temperatures and detecting sensor failures.
  • To enhance the reliability and accuracy of data collected by WSNs.

Main Methods:

  • Implementation of a supervised learning approach using Artificial Hydrocarbon Networks (AHN).
  • Integration with a web service for remote temperature prediction and comparison with field sensors.
  • Experimental validation using a small-scale WSN to assess failure detection, identification, and accommodation.
Keywords:
artificial hydrocarbon networksartificial organic networksdistributed services architecturefailure detectioninternet-of-thingsmachine learningsensor networksweather web services

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Main Results:

  • The proposed AHN-based approach demonstrated accurate detection, identification, and accommodation of sensor failures.
  • Experiments showed that 94.18% of testing data were recovered and accommodated, validating the method's effectiveness.

Conclusions:

  • Artificial Hydrocarbon Networks (AHN) provide an effective solution for intelligent failure detection in wireless sensor networks (WSN).
  • The method significantly improves data integrity and network reliability by accurately managing sensor failures.