Wi-Fi measurement projects (2001-2008, 2019-2023)

This project is no longer active; this page is no longer updated.

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.

This work was described in a 2003 talk at Microsoft Research and a 2003 talk at Intel Research. (Apologies for the poor video quality -- they were transferred from VHS.)

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].

Along the way, we published methods for network measurement and analysis [henderson:measuring, henderson:esm].

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 then applied the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool in the detection and root-cause analysis of network anomalies. As a case study, we applied it a seven-year trace (from 2012 to 2018) of the Dartmouth Wi-Fi network's activity, with approximately 3,000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations [camacho:networkmetrics-j], with earlier versions of that work appearing as [camacho:networkmetrics, camacho:networkmetrics-tr, camacho:networkmetrics-tr2].

In 2004 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 also mined the original 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].

In 2021, 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.

Funding and acknowledgements

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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Papers (tagged 'wifi')

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[Kotz research]