This is an idea proposed in 2024 as a Cambridge Computer Science PhD topic, and is currently being worked on by Josh Millar. It is supervised by Hamed Haddadi and Anil Madhavapeddy as part of the Remote Sensing of Nature project.
In-situ sensing devices need to be deployed in remote environments for long periods of time, and minimizing their power consumption is vital for maximising both their operational lifetime and coverage.
We are exploring the construction of a versatile multi-sensor device (initially based around the ESP32 chipset) and designing an exceptionally low power consumption model by using an on-device reinforcement learning scheduler that can learn to cooperate with other nearby devices.
Our prototype device setup for learning schedules for biodiversity monitoring does pretty well 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. You can read more about this in Terracorder: Sense Long and Prosper.