On the Efficiency and Robustness of Vibration-based Foundation Models
  for IoT Sensing: A Case Study
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. A case study is presented
featuring a vehicle classification application using acoustic and seismic
sensing. The work is motivated by the success of foundation models in the areas
of natural language processing and computer vision, leading to generalizations
of the FM concept to other domains as well, where significant amounts of
unlabeled data exist that can be used for self-supervised pre-training. One
such domain is IoT applications. Foundation models for selected sensing
modalities in the IoT domain can be pre-trained in an environment-agnostic
fashion using available unlabeled sensor data and then fine-tuned to the
deployment at hand using a small amount of labeled data. The paper shows that
the pre-training/fine-tuning approach improves the robustness of downstream
inference and facilitates adaptation to different environmental conditions.
More specifically, we present a case study in a real-world setting to evaluate
a simple (vibration-based) FM-like model, called FOCAL, demonstrating its
superior robustness and adaptation, compared to conventional supervised deep
neural networks (DNNs). We also demonstrate its superior convergence over
supervised solutions. Our findings highlight the advantages of vibration-based
FMs (and FM-inspired selfsupervised models in general) in terms of inference
robustness, runtime efficiency, and model adaptation (via fine-tuning) in
resource-limited IoT settings.