On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study

no code implementations • 3 Apr 2024 • Tomoyoshi Kimura, Jinyang Li, Tianshi Wang, Denizhan Kara, Yizhuo Chen, Yigong Hu, Ruijie Wang, Maggie Wigness, Shengzhong Liu, Mani Srivastava, Suhas Diggavi, Tarek Abdelzaher

This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a class of) IoT applications.

SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach

no code implementations • 3 Feb 2024 • Tianshi Wang, Jinyang Li, Ruijie Wang, Denizhan Kara, Shengzhong Liu, Davis Wertheimer, Antoni Viros-i-Martin, Raghu Ganti, Mudhakar Srivatsa, Tarek Abdelzaher

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.

Contrastive Learning