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20242026
most citedFoundation Models for CPS-IoT: Opportunities and Challenges

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals

Tomoyoshi Kimura, Denizhan Kara, Jinyang Li +6

Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such as human activity analysis, emo…

cs.LG2025

PASS: Private Attributes Protection with Stochastic Data Substitution

Yizhuo Chen, Chun-Fu, Chen +3

The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Vari…

cs.LG2025

SPAR: Self-supervised Placement-Aware Representation Learning for Distributed Sensing

Yizhuo Chen, Tianchen Wang, You Lyu +7

We present SPAR, a framework for self-supervised placement-aware representation learning in distributed sensing. Distributed sensing spans applications where multiple spatially dis…

cs.LG2025★ 1 cited

Foundation Models for CPS-IoT: Opportunities and Challenges

Ozan Baris, Yizhuo Chen, Gaofeng Dong +9

Methods from machine learning (ML) have transformed the implementation of Perception-Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of Things…

cs.LG2024

MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective

Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu +3

The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage…

cs.LG2024

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

Tomoyoshi Kimura, Jinyang Li, Tianshi Wang +9

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 cla…