  In natural hazard warning systems fast decision making is vital to avoid
catastrophes. Decision making at the edge of a wireless sensor network promises
fast response times but is limited by the availability of energy, data transfer
speed, processing and memory constraints. In this work we present a realization
of a wireless sensor network for hazard monitoring based on an array of
event-triggered single-channel micro-seismic sensors with advanced signal
processing and characterization capabilities based on a novel co-detection
technique. On the one hand we leverage an ultra-low power, threshold-triggering
circuit paired with on-demand digital signal acquisition capable of extracting
relevant information exactly and efficiently at times when it matters most and
consequentially not wasting precious resources when nothing can be observed. On
the other hand we utilize machine-learning-based classification implemented on
low-power, off-the-shelf microcontrollers to avoid false positive warnings and
to actively identify humans in hazard zones. The sensors' response time and
memory requirement is substantially improved by quantizing and pipelining the
inference of a convolutional neural network. In this way, convolutional neural
networks that would not run unmodified on a memory constrained device can be
executed in real-time and at scale on low-power embedded devices. A field study
with our system is running on the rockfall scarp of the Matterhorn H\"ornligrat
at 3500 m a.s.l. since 08/2018.

Machine Learning;Networking and Internet Architecture;Machine Learning