Low-power sensing infrastructure for biodiversity
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.
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.
ideasRelated News
Terracorder: Sense Long and Prosper / Aug 2024
Josh Millar, Sarab Sethi, Hamed Haddadi and Anil Madhavapeddy.
Working paper at arXiv.
papersRemote Sensing of Nature / Jan 2023
Measuring the world's forest carbon and biodiversity is made possible by remote sensing instruments, ranging from satellites in space (Landsat, Sentinel, GEDI) to ground-based sensors (ecoacoustics, camera traps, moisture sensors) that take regular samples and are processed into time-series metrics and actionable insights for conservation and human development. However, the algorithms for processing this data are challenging as the data is highly multimodal (multispectral, hyperspectral, synthetic aperture radar, or lidar), often sparsely sampled spatially, and not in a continuous time series. I work on various algorithms and software and hardware systems we are developing to improve the datasets we have about the surface of the earth. […904 words]
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