  This paper introduces SudokuSens, a generative framework for automated
generation of training data in machine-learning-based Internet-of-Things (IoT)
applications, such that the generated synthetic data mimic experimental
configurations not encountered during actual sensor data collection. The
framework improves the robustness of resulting deep learning models, and is
intended for IoT applications where data collection is expensive. The work is
motivated by the fact that IoT time-series data entangle the signatures of
observed objects with the confounding intrinsic properties of the surrounding
environment and the dynamic environmental disturbances experienced. To
incorporate sufficient diversity into the IoT training data, one therefore
needs to consider a combinatorial explosion of training cases that are
multiplicative in the number of objects considered and the possible
environmental conditions in which such objects may be encountered. Our
framework substantially reduces these multiplicative training needs. To
decouple object signatures from environmental conditions, we employ a
Conditional Variational Autoencoder (CVAE) that allows us to reduce data
collection needs from multiplicative to (nearly) linear, while synthetically
generating (data for) the missing conditions. To obtain robustness with respect
to dynamic disturbances, a session-aware temporal contrastive learning approach
is taken. Integrating the aforementioned two approaches, SudokuSens
significantly improves the robustness of deep learning for IoT applications. We
explore the degree to which SudokuSens benefits downstream inference tasks in
different data sets and discuss conditions under which the approach is
particularly effective.
