Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring

Author:

Decorte Thomas1ORCID,Mortier Steven2ORCID,Lembrechts Jonas J.3ORCID,Meysman Filip J. R.4ORCID,Latré Steven2ORCID,Mannens Erik2ORCID,Verdonck Tim1ORCID

Affiliation:

1. Department of Mathematics, University of Antwerp-imec, Middelheimlaan 1, 2000 Antwerp, Belgium

2. IDLab, Department of Computer Science, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium

3. Plants and Ecosystems, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium

4. Geobiology, Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Antwerp, Belgium

Abstract

Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.

Funder

Department of Economy, Science, and Innovation

Flemish Institute for Technological Research

Publisher

MDPI AG

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