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. 2021 Nov 23;21(23):7774.
doi: 10.3390/s21237774.

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Laura Erhan  1 , Mario Di Mauro  2 , Ashiq Anjum  3 , Ovidiu Bagdasar  1   4 , Wei Song  5 , Antonio Liotta  6
Affiliations

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Laura Erhan et al. Sensors (Basel). .

Abstract

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 unsustainable 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 demonstrate 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.

Keywords: Internet of Things; data imputation; edge computing; edge intelligence.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Histogram of the ozone measurements in the dataset.
Figure 2
Figure 2
RMSE value in relation to impairment rate (%) for the non-bursty case.
Figure 3
Figure 3
Non-bursty case comparison with 5%, 50%, and 85% impairment rate.
Figure 4
Figure 4
Non-bursty case and bursty case for the same impairment rate (20%).
Figure 5
Figure 5
RMSE value for bursty case with 15% impairment rate.
Figure 6
Figure 6
Colormap showcasing the RMSE in relation to the impairment rate and burst size.
Figure 7
Figure 7
Execution times (s) on laptop and RPI 4B 4GB for the non-bursty case and varying impairment rates.
Figure 8
Figure 8
Colormap showcasing the execution time (s) in relation to the impairment rate and burst size for kNN, missForest and MICE data impuation.
Figure 9
Figure 9
Snapshot of the CPU and RAM memory usage for the non-bursty case with 50% impairment rate on the RPI 4B (4GB of RAM) for each algorithm (1-mean imputation, 2-MICE imputation, 3-missForest imputation, 4-kNN imputation).
See this image and copyright information in PMC

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