Spectrum scarcity has surfaced as a prominent concern in wireless radio communications with the emergence of new technologies over the past few years. As a result, there is growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. In this paper, we present a framework to capture and model the traffic behavior of short-time spectrum occupancy for IoT applications in the shared bands to determine the existing interference. The proposed capturing method utilizes a software defined radio to monitor the short bursts of IoT transmissions by capturing the time series data which is converted to power spectral density to extract the observed occupancy. Furthermore, we propose the use of an unsupervised machine learning technique to enhance conventionally implemented energy detection methods. Our experimental results show that the temporal and frequency behavior of the spectrum can be well-captured using the combination of two models, namely, semi-Markov chains and a Poisson-distribution arrival rate. We conduct an extensive measurement campaign in different urban environments and incorporate the spatial effect on the IoT shared spectrum.
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）
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）
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）
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）
