  Distributed acoustic sensing (DAS) is a novel enabling technology that can
turn existing fibre optic networks to distributed acoustic sensors. However, it
faces the challenges of transmitting, storing, and processing massive streams
of data which are orders of magnitude larger than that collected from point
sensors. The gap between intensive data generated by DAS and modern computing
system with limited reading/writing speed and storage capacity imposes
restrictions on many applications. Compressive sensing (CS) is a revolutionary
signal acquisition method that allows a signal to be acquired and reconstructed
with significantly fewer samples than that required by Nyquist-Shannon theorem.
Though the data size is greatly reduced in the sampling stage, the
reconstruction of the compressed data is however time and computation
consuming. To address this challenge, we propose to map the feature extractor
from Nyquist-domain to compressed-domain and therefore vibration detection and
classification can be directly implemented in compressed-domain. The measured
results show that our framework can be used to reduce the transmitted data size
by 70% while achieves 99.4% true positive rate (TPR) and 0.04% false positive
rate (TPR) along 5 km sensing fibre and 95.05% classification accuracy on a
5-class classification task.
