Probabilistic Models for Quality Control in Environmental Sensor Networks

Networks of distributed, remote sensors are providing ecological scientists with a view
of our environment that is unprecedented in detail. However, these networks are subject
to harsh conditions, which lead to malfunctions in individual sensors and failures in
network communications. This behavior manifests as corrupt or missing measurements
in the data. Consequently, before the data can be used in ecological models, future experiments,
or even policy decisions, it must be quality controlled (QC’d) to flag affected
measurements and impute corrected values. This dissertation describes a probabilistic
modeling approach for real-time automated QC that exploits the spatial and temporal
correlations in the data to distinguish sensor failures from valid observations. The model
adapts to a site by learning a Bayesian network structure that captures spatial relationships
among sensors, and then extends this structure to a dynamic Bayesian network
to incorporate temporal correlations. The final QC model contains both discrete and
continuous variables, which makes inference intractable for large sensor networks. Consequently,
we examine the performance of three approximate methods for inference in this
probabilistic framework. Two of these algorithms represent contemporary approaches to
inference in hybrid models, while the third is a greedy search-based method of our own
design. We demonstrate the results of these algorithms on synthetic datasets and real
environmental sensor data gathered from an ecological sensor network located in western
Oregon. Our results suggest that we can improve performance over networks with less
sensors that use exhaustive asynchronic inference by including additional sensors and
applying approximate algorithms.