Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study
Journal article
Authors | Erhan, Laura, Di Mauro, Mario, Anjum, Ashiq, Bagdasar, Ovidiu, Song, Wei and Liotta, Antonio |
---|---|
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 | Electrical and Electronic Engineering; Biochemistry; Instrumentation; Atomic and Molecular Physics, and Optics; Analytical Chemistry |
Year | 2021 |
Journal | Sensors |
Journal citation | 21 (23), p. 7774 |
Publisher | MDPI AG |
ISSN | 1424-8220 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s21237774 |
Web address (URL) | https://www.mdpi.com/1424-8220/21/23/7774 |
https://creativecommons.org/licenses/by/4.0/ | |
hdl:10545/626252 | |
Output status | Published |
Publication dates | 23 Nov 2021 |
Publication process dates | |
Deposited | 25 Jan 2022, 14:28 |
Accepted | 20 Nov 2021 |
Contributors | University of Derby, University of Salerno, 84084 Fisciano, Italy, University of Leicester, University of Alba Iulia, 510009 Alba Iulia, Romania, Shanghai Ocean University, Shanghai 200090, China and Free University of Bozen-Bolzano, 39100 Bolzano, Italy |
File | File Access Level Restricted |
File | License File Access Level Open |
https://repository.derby.ac.uk/item/9220q/embedded-data-imputation-for-environmental-intelligent-sensing-a-case-study
Download files
190
total views32
total downloads4
views this month2
downloads this month