Sensors, Vol. 24, Pages 2416: Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring
Sensors, Vol. 24, Pages 2416: Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring
Sensors doi: 10.3390/s24082416
Authors:
Thomas Decorte
Steven Mortier
Jonas J. Lembrechts
Filip J. R. Meysman
Steven Latré
Erik Mannens
Tim Verdonck
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 imp...
Source: Sensors - Category: Biotechnology Authors: Thomas Decorte Steven Mortier Jonas J. Lembrechts Filip J. R. Meysman Steven Latr é Erik Mannens Tim Verdonck Tags: Article Source Type: research