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

• URL: http://arxiv.org/abs/2404.02461v1
• Date: Wed, 3 Apr 2024 05:04:06 GMT
• Title: On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
• Authors: Tomoyoshi Kimura, Jinyang Li, Tianshi Wang, Denizhan Kara, Yizhuo Chen, Yigong Hu, Ruijie Wang, Maggie Wigness, Shengzhong Liu, Mani Srivastava, Suhas Diggavi, Tarek Abdelzaher,
• Abstract summary: 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.
• Score: 7.0723456126359245
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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.

Related papers

• Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks [0.0]
  We propose FedVaccine, a novel AMC model aimed at improving generalizability across signals with varying noise levels. FedVaccine overcomes the limitations of existing FL-based AMC models' linear aggregation by employing a split-learning strategy. These findings highlight FedVaccine's potential to enhance the reliability and performance of AMC systems in practical wireless network environments.
  arXiv  Detail & Related papers  (2024-10-16T17:48:47Z)
• Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models [6.118896920507198]
  This paper introduces an innovative regression framework utilizing large language models (LLMs) for RUL prediction. Experiments on the Turbofan engine's RUL prediction task show that the proposed model surpasses state-of-the-art (SOTA) methods. With minimal target domain data for fine-tuning, the model outperforms SOTA methods trained on full target domain data.
  arXiv  Detail & Related papers  (2024-10-04T04:21:53Z)
• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• Learning with Noisy Foundation Models [95.50968225050012]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets. We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [91.15120211190519]
  This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks. We propose a light-weight black-box tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise.
  arXiv  Detail & Related papers  (2023-09-29T06:18:15Z)
• Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments [0.0]
  State-of-the-art solutions heavily rely on external infrastructures and are subject to potential privacy compromises. Recent developments in machine learning (ML) offer solutions to address these limitations relying only on the data from onboard sensors of IoT devices. This paper presents a machine learning-based indoor positioning system, using motion and ambient sensors, to localize a moving entity in privacy concerned factory environments.
  arXiv  Detail & Related papers  (2023-08-22T10:07:19Z)
• Variational Autoencoder Assisted Neural Network Likelihood RSRP Prediction Model [2.881201648416745]
  We study a generative model for RSRP prediction, exploiting MDT data and a digital twin (DT) Our proposed model that uses real-world data demonstrates an accuracy improvement of about 20% or more compared with the empirical model.
  arXiv  Detail & Related papers  (2022-06-27T17:27:35Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.