Resource efficient data transmission on animals based on machine learning
It addresses energy efficiency for bio-loggers in wildlife research, but appears incremental as it applies existing ML methods to a known bottleneck.
This study tackled the problem of limited storage, processing, and data transmission in bio-loggers for wildlife research by using machine learning to guide selective data transmission, aiming to reduce energy consumption and extend operational lifespan without hardware changes.
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.