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.

该研究论文演示了使用无标签传感数据进行预训练的基于振动的基础模型（FM）在运行时推理中提高鲁棒性的潜力，通过一个基于声学和地震感知的车辆分类应用的案例研究，证明了该预训练 / 微调方法改善了下游推理的鲁棒性，并促进了对不同环境条件的适应。