Harnessing Environmental Data at the Edge of the Cloud
Global warming is a defining challenge of our time with devastating consequences for local habitats. High mountain areas are particularly affected by global warming leading to a decline of their cryosphere (glaciers, snow cover and permafrost). In high-alpine steep bedrock, permafrost thaw decreases...
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
ETH Zurich
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.11850/536692 https://doi.org/10.3929/ethz-b-000536692 |
| Summary: | Global warming is a defining challenge of our time with devastating consequences for local habitats. High mountain areas are particularly affected by global warming leading to a decline of their cryosphere (glaciers, snow cover and permafrost). In high-alpine steep bedrock, permafrost thaw decreases the stability of mountain slopes leading to an increase of rockfalls and landslides and thereby putting life and built infrastructure at risk. Monitoring these environmental changes is important for natural hazard warning and understanding the geophysical processes leading to such hazards. Moreover, by providing evidence from large-scale, long-term measurements, environmental monitoring helps to bolster scientific findings and can call attention to the immediate impacts of climate change. The rise of wireless sensor networks offers a range of possibilities for environmental monitoring enabling large-scale deployments with high spatial-temporal resolution using many different sensor types. The cheap and diverse sensors can be installed at hard to reach places with little available networking or power infrastructure. However, the resulting datasets (often heterogenous and long-term measurements) require a complex data analysis. Moreover, networking or power failures often lead to an error-prone data collection and a fragmented and noisy datasets. Analyzing these datasets typically requires dedicated domain-expert knowledge which can not be scaled to long-term monitoring datasets. Machine learning provides options to extract information automatically but these techniques usually require a clean dataset for training and their performance is strongly affected by differences in the distribution of training and test data. In this dissertation, we consequently develop tools and methods applicable to heterogeneous, long-term, noisy datasets originating in wireless sensor network deployments. The main contributions of the dissertation are - A methodology to work with fragmented and noisy data from a real-world sensor network ... |
|---|