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Embedded data imputation for environmental intelligent sensing: A case study

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journal contribution
posted on 2022-11-11, 17:15 authored by L Erhan, M Di Mauro, A Anjum, O Bagdasar, W Song, A Liotta
Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsus-tainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demon-strate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

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Citation

Erhan, Laura, Mario Di Mauro, Ashiq Anjum, Ovidiu Bagdasar, Wei Song, and Antonio Liotta. 2021. "Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study" Sensors 21, no. 23: 7774. https://doi.org/10.3390/s21237774

Author affiliation

School of Computing and Mathematic Sciences

Version

  • VoR (Version of Record)

Published in

Sensors

Volume

21

Pagination

7774

Publisher

MDPI AG

issn

1424-8220

eissn

1424-8220

Acceptance date

2021-11-20

Copyright date

2021

Available date

2022-11-11

Language

en

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