# Terracorder: Sense Long and Prosper

arXiv: https://arxiv.org/abs/2408.02407 · HTML: https://mlsanity.com/paper/2408.02407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning](https://mlsanity.com/paper/1909.01968/) (2019)

## Built on by (most cited first, 1 of 1)

- [acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices](https://mlsanity.com/paper/2501.17841/) (2025)

## Abstract (arXiv)

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.