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.

翻译：本文提出SudokuSens，一种用于机器学习物联网应用中训练数据自动生成的生成式框架，使生成的合成数据能够模拟实际传感器数据采集过程中未遇到过的实验配置。该框架提升了深度学习模型的鲁棒性，特别适用于数据采集成本高昂的物联网应用场景。研究动机源于物联网时间序列数据中，观测对象特征与周围环境固有效应及动态环境干扰的混杂纠缠特性。为向物联网训练数据注入充分多样性，需考虑训练案例的组合爆炸问题——其复杂程度随观测对象数量与可能遇到的环境条件数量呈乘积式增长。我们的框架大幅降低了这种乘积式训练需求。为解耦对象特征与环境条件，我们采用条件变分自编码器（CVAE），将数据采集需求从乘积式降至（近似）线性，同时合成生成缺失条件的数据。针对动态干扰的鲁棒性，我们采用会话感知时序对比学习方法。通过集成上述两种方法，SudokuSens显著提升了物联网应用中深度学习的鲁棒性。我们探究了SudokuSens在不同数据集上对下游推理任务的促进程度，并讨论了该框架特别有效的应用条件。