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Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study.

Laura Erhan1, Mario Di Mauro2, Ashiq Anjum3

  • 1College of Science and Engineering, University of Derby, Derby DE22 1GB, UK.

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|December 10, 2021
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Summary
This summary is machine-generated.

Machine learning at the edge, using kNN and missForest on Raspberry Pi, efficiently imputes missing sensor data. This edge learning approach cleans pollution data locally, avoiding unsustainable cloud transmissions.

Keywords:
Internet of Thingsdata imputationedge computingedge intelligence

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Area of Science:

  • Computer Science
  • Environmental Science
  • Machine Learning

Background:

  • Smart environments leverage cloud computing and the Internet of Things for monitoring and actuation.
  • Transmitting raw sensor data to the cloud can lead to unsustainable solutions due to data volume.
  • Corrupted or unusable data transmitted from sensor nodes poses a significant challenge.

Purpose of the Study:

  • To advocate for and investigate the use of machine learning at sensor nodes for data cleaning.
  • To reduce the transmission of corrupted data to the cloud by performing essential operations at the edge.
  • To assess the feasibility of embedding machine learning techniques on resource-constrained devices like Raspberry Pi.

Main Methods:

  • Utilized a public pollution dataset for analysis.
  • Implemented and evaluated two machine learning techniques: k-Nearest Neighbors (kNN) and missForest.
  • Embedded these algorithms on a Raspberry Pi for edge data imputation.
  • Assessed computational efficiency and accuracy of the edge learning methods.

Main Results:

  • kNN and missForest demonstrated accuracy in imputing up to 40% of randomly distributed missing values.
  • The distribution of imputed values was indistinguishable from the benchmark data.
  • Edge learning methods showed computational efficiency, with computation times shorter than sampling periods.
  • Analysis confirmed the recoverability of bursty missing data blocks up to 100 samples.

Conclusions:

  • Machine learning at the edge is an effective strategy for data imputation in smart environments.
  • Edge learning on devices like Raspberry Pi can efficiently handle corrupted sensor data, improving data quality.
  • This approach offers a sustainable alternative to solely relying on cloud-based data processing for sensor networks.