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: Tags: Article Source Type: research