Experimental Evaluation of Empirical NB-IoT Propagation Modelling in a Deep-Indoor Scenario
Jakob Thrane, Krzysztof Mateusz Malarski, Henrik Lehrmann Christiansen, Sarah Ruepp
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6 pages, 6 figures, submitted to Globecom2020 conference, Selected Areas in Communications Symposium, Track on Internet of Things and Smart Connected Communities
Path-loss modelling in deep-indoor scenarios is a difficult task. On one hand, the theoretical formulae solely dependent on transmitter-receiver distance are too simple; on the other hand, discovering all significant factors affecting the loss of signal power in a given situation may often be infeasible. In this paper, we experimentally investigate the influence of deep-indoor features such as indoor depth, indoor distance and distance to the closest tunnel corridor and the effect on received power using NB-IoT. We describe a measurement campaign performed in a system of long underground tunnels, and we analyse linear regression models involving the engineered features. We show that the current empirical models for NB-IoT signal attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor distance and penetration depth do not explain the signal attenuation well and increase the error of the prediction by 2-12 dB using existing models, and 2) a promising feature of average distance to the nearest corridor is identified.
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）
People effects on IoT indoor wireless channel characterization
Sidia Institute of Science and Technology（西迪亚科学与技术研究所）；Centre for Research and Development in Telecommunications (CPqD)（电信研发中心）；Federal University of Juiz de Fora（茹伊斯德福拉联邦大学）；Federal University of Rio Grande do Norte (UFRN)（北里奥格兰德联邦大学）
Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning
Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Electronics, National Chiao Tung University（国立交通大学电子研究所）
