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Welcome to CRAWDAD

CRAWDAD is the Community Resource for Archiving Wireless Data At Dartmouth, a wireless network data resource for the research community. This archive has the capacity to store wireless trace data from many contributing locations, and staff to develop better tools for collecting, anonymizing, and analyzing the data. We work with community leaders to ensure that the archive meets the needs of the research community.

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We have recently made changes to the crawdad.org website that affect ALL current users. The website has been physically moved to a new server, and we have made changes to our user registration process. Together, these changes require ALL previous users to re-register.

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Latest News

New CRAWDAD Data Set - Dataset of mobility traces of buses in Rio de Janeiro, Brasil - February 20, 2018 - February 23, 2018

A new data set has been added to CRAWDAD:

coppe-ufrj/RioBusesorg/coppe-ufrj20180220/>

Dataset of mobility traces of buses in Rio de Janeiro, Brasil.

Contributed by Daniel Dias, Luís Henrique Maciel Kosmalski Costa.

Real-time position data reported by buses, updated every minute, from the city of Rio de Janeiro, Brazil. The file is CSV, containing the date, time(24h format), bus ID, bus line, latitude, longitude and speed of more than 12,000 buses.

If you use these data, please let us know, and you can use the DOI 10.15783/C7MP7S to cite. BibTeX and RIS are provided on the website.

New CRAWDAD Data Set - Data concerning social interaction and propinquity based on wireless and bluetooth. - March 10, 2017

A new data set has been added to CRAWDAD:

copelabs/usense 2017-01-27

Data concerning social interaction and propinquity based on wireless and bluetooth.

Contributed by S. Firdose, L. Lopes, W. Moreira, R. Sofia, P. Mendes.

This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: - SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName - DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName - Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) - PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING There are two tracesets. A first traceset has been collected relying on a first NSense version in 2015. Then, a second traceset has been collected in 2016, with a refined version of NSense. In all tracesets, devices have been carried around by people that share the same affiliation during their individual daily routines (24 hour periods).

If you use these data, please let us know, and you can use the DOI 10.15783/C7D885 to cite. BibTeX and RIS are provided on the website.

new CRAWDAD dataset - wireless contacts traces from Android smartphones - October 30, 2016

A new dataset has been added to CRAWDAD. org/upb

Trace of wireless contacts, social connections, and user interests, performed in an academic environment for 63 days, with 72 participants

Contributed by Radu I. Ciobanu, Ciprian Dobre.

Wireless contacts trace collected at the University Politehnica of Bucharest in the spring of 2012, using an application entitled HYCCUPS Tracer (http://hyccups.hpc.pub.ro), with the purpose of collecting contextual data from Android smartphones. It was run in the background and collected availability and mobile interaction information such as usage statistics, user activity, battery statistics, or sensor data, but it also gathered information about a device's encounters with other nodes or with wireless access points. Encounter collection was performed using AllJoyn. The data was collected by constructing and deleting wireless sessions using the AllJoyn framework based on WiFi. Tracing was executed asynchronously. The duration of the tracing experiment was 63 days, between March and May 2012, and had 72 participants, out of which only 42 had at least one contact. By analyzing the participants' Facebook profiles, the social connections matrix was extracted, as well as the users' interests. The trace (and others from the CRAWDAD collection) is parsed within the MobEmu simulator (used in all UPB's papers), publicly available at https://github.com/raduciobanu/mobemu.

If you use these data, please let us know, and you can use the DOI 10.15783/C7TG7K to cite. BibTeX and RIS are provided on the website.