Summary of On the Efficiency and Robustness Of Vibration-based Foundation Models For Iot Sensing: a Case Study, by Tomoyoshi Kimura et al.
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
by Tomoyoshi Kimura, Jinyang Li, Tianshi Wang, Denizhan Kara, Yizhuo Chen, Yigong Hu, Ruijie Wang, Maggie Wigness, Shengzhong Liu, Mani Srivastava, Suhas Diggavi, Tarek Abdelzaher
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed foundation models can improve the robustness of run-time inference in Internet of Things (IoT) applications. The models are pre-trained with unlabeled sensing data and fine-tuned for specific applications using a small amount of labeled data. A case study is presented featuring a vehicle classification application using acoustic and seismic sensing, which demonstrates the superiority of foundation models over conventional supervised deep neural networks (DNNs) in terms of inference robustness, runtime efficiency, and model adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models can improve IoT applications by providing robustness to environmental conditions. A vibration-based model called FOCAL is proposed, which outperforms DNNs in a real-world setting. The pre-training/fine-tuning approach improves the convergence of the model, making it suitable for resource-limited settings. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Inference * Supervised
