用机器学习筛选动物数据，降低传输能耗，延长设备使用时间
Resource efficient data transmission on animals based on machine learning
- 通过机器学习判断哪些传感器数据值得上传
- 减少无效数据传输，显著降低功耗
- 无需改硬件，适合长期野外监测项目
生物标签器是通过多种传感器追踪动物行为的重要电子设备。尽管其功能持续提升，但受限于体积和重量，仍面临存储、处理和数据传输能力不足的挑战。本研究探索基于机器学习的有选择性数据传输方法，以降低生物标签器的能耗，从而在不进行硬件改动的前提下延长其工作寿命。
原文摘要 · Abstract (English)
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.
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