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Related projects: [CRAWDAD], [DIST], [MAP], [Mobility-models], [NetSANI]
Related keywords: [wifi]
Wireless 802.11 (Wi-Fi) networks have become universal. In 2001, however, there were few large deployments and Dartmouth was one of the first universities to deploy a campus-wide Wi-Fi network. In 2001-02 we conducted the largest-ever characterization effort on a wireless network. In the initial effort we captured statistics and network traces from over 476 access points spread over 161 buildings at Dartmouth College, capturing the activity of nearly two thousand users. The original paper [kotz:campus] received the ACM SIGMOBILE Test-of-Time Paper Award in March 2017: "This paper was the first to systematically demonstrate how to measure and understand a production-scale wireless network, which was previously considered an impenetrable black box. This led to an incredible amount of follow-on work, with the measurement methods and analysis mechanisms proposed in this paper still being used. This paper was also the spark for the creation of the CRAWDAD data repository, which has been of immense value to the wireless research community."
Interested readers are directed to the journal paper [kotz:jcampus], however, for a more complete analysis and for some important corrections to the original results.
The earliest (preliminary) report of this work was Pablo Stern's undergraduate thesis [stern:thesis].
We repeated the data-collection effort two years later and were able to measure trends and changes in network activity, as well as adding a new focus on VOIP and P2P traffic and on user mobility [henderson:jvoice, henderson:voice].
Most recently (2019), We described and characterized the largest Wi-Fi network trace ever published: spanning seven years, approximately 3000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations. We described the methods used to capture and process the traces, and characterized the most prominent trends and changes during the seven-year span of the trace (2012-18). The analysis covers the same campus in the above papers, so we were able to comment on changes in patterns of usage, connection, and mobility in Wi-Fi deployments [camacho:longitudinal]. It also allowed us to explore methods for mining social interactions [martinez:poster].
We've since mined this data for many other studies -- see the list of related projects and keywords above, as well as some related projects [lee:thesis, mills:tettey-thesis].
We released the earliest data, and founded CRAWDAD.org, a "Community Resource for Archiving Wireless Data at Dartmouth", to encourage broader sharing of such data across the research community.
We've also published about methods for network measurement and analysis [camacho:networkmetrics, camacho:networkmetrics-tr, henderson:measuring, henderson:esm, martinez:poster].
Recently, we proposed a system that, in a preliminary evaluation, was able to decide with 82% accuracy whether the location of an IoT device is inside or outside of a defined space based on a small number of transmitted Wi-Fi frames. See Paul Gralla's undergraduate thesis for details [gralla:inside-outside].
Ilya Abyzov, Denise Anthony, David Blinn, Rasmus Bro, Elena Cabrera, José Camacho, Guanling Chen, Kobby Essien, Jeff Fielding, Paul Gralla, Tristan Henderson, David Kotz, Eduardo Antonio Mañas Martínez, Nathan Schneider, Pablo Stern, and Katarzyna Wasielewska.
Funded by Cisco Systems, Dartmouth College, DoCoMo USA Labs, and Intel Corporation, and somewhat by Department of Justice (BJA) through ISTS.
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