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Abstract. 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.
Authors. Josh Millar, Sarab Sethi, Hamed Haddadi and Anil Madhavapeddy
See Also. This publication was part of my Interspatial OS and Remote Sensing of Nature projects.
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» New project on building species models of the whole planet.» Preprint on Terracorder ground sensing / «» Discussion on the nature-based credits article.» Chaired session at ACM COMPASS 2024 and attended CoRE stack RIC.
» Opened the 17th William Pitt Seminar at Pembroke College on climate change.