Advances in Internet of Things (IoT) have created new epoch in environmental monitoring, facilitating the collection of high-resolution spatiotemporal dataset. Although, IoT technologies present plausible tools to expand current capacity in environmental monitoring, the introduction of low-cost IoT sensors is critical to have wider scope and adoption for this purpose. The application of low-cost sensors in environmental monitoring, however, has raised several concerns, particularly pertaining to their usability, reliability, and data quality.

This research leverages the capabilities of machine learning and AI to address the limitations of low-cost IoT sensor devices in environmental monitoring networks. Specifically, we focus on designing and evaluating methods for efficiently harnessing high-value information from inherently noisy data collected from IoT deployments, web/crowd sourced data and remote sensing of ecologically significant sites.

This work also facilitates the manipulations of large-scale data to extract relationships between peatland characteristics and Green House Gases (GHG) emission trends, providing useful ground-based insights to corroborate remote and satellite-based observations.

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