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.
@article{arxiv.2503.10277,
title = {Resource efficient data transmission on animals based on machine learning},
author = {Wilhelm Kerle-Malcharek and Karsten Klein and Martin Wikelski and Falk Schreiber and Timm A. Wild},
journal= {arXiv preprint arXiv:2503.10277},
year = {2025}
}
Comments
Submitted to Scientific Reports but not published, 23 pages, 5 figures, 3 tables