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文献详情 >Weather of the dorm Wi-Fi ecos... 收藏
arXiv

Weather of the dorm Wi-Fi ecosystem at the University of Colorado Boulder: Fall semester 2019 to spring semester 2020 - A case study of Wi-Fi including a campus response to the COVID-19 perturbation

作     者:McGrath, Jake Davis, Armen Curry, James Gartner, Orrie Rodrigues, Glenn Spielman, Seth Massey, Daniel 

作者机构:Aeropace Engineering Applied Mathematics Office of Information Technology Office of Data Analytics Department of Computer Science 

出 版 物:《arXiv》 (arXiv)

年 卷 期:2021年

核心收录:

主  题:Weather forecasting 

摘      要:Growing use of network-enabled technology in Institutions of Higher Education (IHEs) among students, staff, and faculty means that there has been increasing demand to adapt technology platforms and tools that transform student learning strategies, faculty teaching, research modalities, as well as general operations. In fact, many of the new modalities are a necessity for doing IHE business. In August 2019, our research team, at the University of Colorado Boulder, began collecting and analyzing data from the campus Wi-Fi network. A goal of the research was to answer the question of what passive sensing of the IHE s Wi-Fi might be able to tell you about the gross dynamics of the Wi-Fi weather in the IHE ecosystem? Or more generally, what does anonymized data tell us about the dynamics in a IHE s ecosystem. Anonymized data were made available by the University of Colorado Boulder (CU) Office of Information Technology (OIT). Our goal was to understand the campus dynamical ecosystem as a reflection of its collected Wi-Fi data. Those data could then be used to develop forecast models and an understanding of the dynamics of the university ecosystem where the dynamics of Wi-Fi connected device count could be used as a proxy for the ebb and flow of large scale, and small scale, behavior in the ecosystem. The analogy with weather prediction seemed appropriate and a viable strategy. Starting Fall 2019, data were collected in the observational phase (data collection is ongoing). In the analysis phase, briefly touched on here, we applied Singular Spectrum Analysis (SSA) eigen decomposition, to deconstruct Wi-Fi data from dorms, the central campus dining cafeteria, the recreation center, and other buildings on campus. That analysis led to the identification of clusters of buildings that behaved similarly. Several campus buildings are dual use and they are placed in different clusters. Just as in the case of models of the weather, a final component of this research was

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