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In natural hazard warning systems fast decision making is vital to avoidcatastrophes. Decision making at the edge of a wireless sensor network promisesfast response times but is limited by the availability of energy, data transferspeed, processing and memory constraints. In this work we present a realizationof a wireless sensor network for hazard monitoring based on an array ofevent-triggered single-channel micro-seismic sensors with advanced signalprocessing and characterization capabilities based on a novel co-detectiontechnique. On the one hand we leverage an ultra-low power, threshold-triggeringcircuit paired with on-demand digital signal acquisition capable of extractingrelevant information exactly and efficiently at times when it matters most andconsequentially not wasting precious resources when nothing can be observed. Onthe other hand we utilize machine-learning-based classification implemented onlow-power, off-the-shelf microcontrollers to avoid false positive warnings andto actively identify humans in hazard zones. The sensors' response time andmemory requirement is substantially improved by quantizing and pipelining theinference of a convolutional neural network. In this way, convolutional neuralnetworks that would not run unmodified on a memory constrained device can beexecuted in real-time and at scale on low-power embedded devices. A field studywith our system is running on the rockfall scarp of the Matterhorn H\"ornligratat 3500 m a.s.l. since 08/2018.

Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge