SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
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DOI：
10.1145/3625687.3625785
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发表时间：
2023-11
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影响因子：
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通讯作者：
Tianshi Wang;Jinyang Li;Ruijie Wang;Denizhan Kara;Shengzhong Liu;Davis Wertheimer;Antoni Viros-i-Martin;R. Ganti;M. Srivatsa;Tarek F. Abdelzaher
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作者：
Tianshi Wang;Jinyang Li;Ruijie Wang;Denizhan Kara;Shengzhong Liu;Davis Wertheimer;Antoni Viros-i-Martin;R. Ganti;M. Srivatsa;Tarek F. Abdelzaher
本文介绍了 SudokuSens，这是一种在基于机器学习的物联网 (IoT) 应用中自动生成训练数据的生成框架，使得生成的合成数据模拟实际传感器数据收集期间未遇到的实验配置。该框架提高了深度学习模型的稳健性，适用于数据收集成本高昂的物联网应用。这项工作的动机是，物联网时间序列数据将观察到的物体的特征与周围环境的混杂的内在属性和经历的动态环境干扰纠缠在一起。因此，为了将足够的多样性纳入物联网训练数据，需要考虑训练案例的组合爆炸，这些案例的数量与所考虑的对象数量以及可能遇到这些对象的可能环境条件成倍增加。我们的框架大大减少了这些乘法培训需求。为了将对象签名与环境条件解耦，我们采用了条件变分自动编码器（CVAE），它使我们能够将数据收集需求从乘法减少到（接近）线性，同时综合生成缺失条件（的数据）。为了获得针对动态干扰的鲁棒性，采用了会话感知的时间对比学习方法。 SudokuSens 集成了上述两种方法，显着提高了物联网应用深度学习的鲁棒性。我们探讨了 SudokuSens 在不同数据集中对下游推理任务的好处程度，并讨论了该方法特别有效的条件。
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.