Teach your sensors new tricks with Machine Learning

Many LoRaWAN devices are very simple: just a radio sending raw sensor values to the cloud. But this limits the usefulness of a deployment. A sensor can send that it saw movement in front of it, but not what it saw. Or a sensor might notice that it’s being moved around, but not whether it’s attached to a vehicle or is just being carried around. The reason is simple: for knowing what happens in the real world you’ll need lots of data, and sending all that data over LoRaWAN quickly diminishes your downlink budget and battery. How can we do better?

In this hands-on workshop you’ll get your first taste of tiny machine learning: from data collection to model building to deploying on your LoRaWAN end-devices.

Many LoRaWAN devices are very simple: just a radio sending raw sensor values to the cloud. But this limits the usefulness of a deployment. A sensor can send that it saw movement in front of it, but not what it saw. Or a sensor might notice that it’s being moved around, but not whether it’s attached to a vehicle or is just being carried around. The reason is simple: for knowing what happens in the real world you’ll need lots of data, and sending all that data over LoRaWAN quickly diminishes your downlink budget and battery. How can we do better?

In this hands-on workshop you’ll get your first taste of tiny machine learning: from data collection to model building to deploying on your LoRaWAN end-devices.