  Details of Monarch butterfly migration from the U.S. to Mexico remain a
mystery due to lack of a proper localization technology to accurately localize
and track butterfly migration. In this paper, we propose a deep learning based
butterfly localization algorithm that can estimate a butterfly's daily location
by analyzing a light and temperature sensor data log continuously obtained from
an ultra-low power, mm-scale sensor attached to the butterfly. To train and
test the proposed neural network based multi-sensor fusion localization
algorithm, we collected over 1500 days of real world sensor measurement data
with 82 volunteers all over the U.S. The proposed algorithm exhibits a mean
absolute error of <1.5 degree in latitude and <0.5 degree in longitude Earth
coordinate, satisfying our target goal for the Monarch butterfly migration
study.
