  In-situ sensing devices need to be deployed in remote environments for long
periods of time; minimizing their power consumption is vital for maximising
both their operational lifetime and coverage. We introduce Terracorder -- a
versatile multi-sensor device -- and showcase its exceptionally low power
consumption using an on-device reinforcement learning scheduler. We prototype a
unique device setup for biodiversity monitoring and compare its battery life
using our scheduler against a number of fixed schedules; the scheduler captures
more than 80% of events at less than 50% of the number of activations of the
best-performing fixed schedule. We then explore how a collaborative scheduler
can maximise the useful operation of a network of devices, improving overall
network power consumption and robustness.
