  Run-time domain shifts from training-phase domains are common in sensing
systems designed with deep learning. The shifts can be caused by sensor
characteristic variations and/or discrepancies between the design-phase model
and the actual model of the sensed physical process. To address these issues,
existing transfer learning techniques require substantial target-domain data
and thus incur high post-deployment overhead. This paper proposes to exploit
the first principle governing the domain shift to reduce the demand on
target-domain data. Specifically, our proposed approach called PhyAug uses the
first principle fitted with few labeled or unlabeled source/target-domain data
pairs to transform the existing source-domain training data into augmented data
for updating the deep neural networks. In two case studies of keyword spotting
and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled
data collected from the target microphones, PhyAug recovers the recognition
accuracy losses due to microphone characteristic variations by 37% to 72%. In a
case study of seismic source localization with TDoA fngerprints, by exploiting
the frst principle of signal propagation in uneven media, PhyAug only requires
3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting
approach in achieving the same localization accuracy.
