# Resource efficient data transmission on animals based on machine learning

arXiv: https://arxiv.org/abs/2503.10277 · HTML: https://mlsanity.com/paper/2503.10277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 63 references that are in the map.
- First posted on arXiv 13 Mar 2025.

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

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)

## Abstract (arXiv)

Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research. Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbing the animals. This study aims to explore how selective data transmission, guided by machine learning, can reduce the energy consumption of bio-loggers, thereby extending their operational lifespan without requiring hardware modifications.