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

arXiv:2404.02461v1 Announce Type: new
Abstract: 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, …

abstract application applications arxiv case case study class classification cs.lg data eess.sp efficiency foundation inference iot paper robustness sensing study type