Understood. I guess I didn't make that leap because I find that embedded ML isn't what I imagined in this type of scenario. I would assume you'd invest more hea

Understood. I guess I didn't make that leap because I find that embedded ML isn't what I imagined in this type of scenario. I would assume you'd invest more heavily in power usage to make the mesh sensor network fast/resilient. A "base station" with connectivity for alerting and utility power at the edge of the sensor network is where I imagine any ML to run, and that could power your typical CUDA-capable GPU if necessary. I guess that's still edge ML if the point is to not backhaul everything to a datacenter somewhere, but I wouldn't call it embedded. That brought mind low powered devices with many sensors or sensors that capture more local data (i.e. microphones, cameras) vs. just an anemometer, GPS, and chemical ppm.