On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；Shanghai Jiao Tong University（上海交通大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
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.
Decentralized Indoor Localization Based on A Sparse Gaussian Process with Reduced-Dimensional Inputs for Real-Time Sensing and Training on IoT Devices
Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院网络空间安全研究所）；Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology（浙江工业大学滨江人工智能研究院）；School of Artificial Intelligence, Suzhou Institute of Industrial Technology（苏州工业职业技术学院人工智能学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；University of Liverpool（利物浦大学）；D5 Data Co., Ltd.（D5数据有限公司）
Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey
College of Life Science and Technology, Huazhong University of Science and Technology（华中科技大学生命科学与技术学院）；Department of Computing and Software, McMaster University（麦克马斯特大学计算与软件系）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Vibe2Spike: Batteryless Wireless Tags for Vibration Sensing with Event Cameras and Spiking Networks
Department of EECS, The University of Tennessee, Knoxville（电子工程与计算机科学系，田纳西大学）；Department of ECE, Oklahoma State University（电气与计算机工程系，俄克拉荷马州立大学）；Accenture Labs, Accenture（Accenture实验室，Accenture）
Vibe2Spike 提出一种无电池无线振动感知框架，利用压电片、齐纳二极管和 LED 组成的超低成本标签采集振动能量并发射可见光脉冲，结合事件相机与演化优化的脉冲神经网络实现活动识别，在五类设备上达到 94.9% 的平均分类适应度。
Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review
International Water Research Institute (IWRI)（国际水研究所（IWRI））；Mohammed VI Polytechnic University (UM6P)（穆罕默德六世理工大学（UM6P））；Research and Development Unit in Agriculture and Agri-food of Abitibi-Témiscamingue (URDAAT)（阿比蒂比-泰米斯卡曼格农业与农业食品研发部（URDAAT））；Université du Québec en Abitibi-Témiscamingue（魁北克大学阿比蒂比-泰米斯卡曼格分校）
