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The nus/bluetooth dataset (v. 2007-09-03)  >  the sql traceset

There is 1 trace in this traceset
download the Anonymized_BT_Logs_20070903_1833.sql.gz file
from a CRAWDAD mirror:  US UK AU
size="3.9MB" type="gz" md5="88ceea1702e96e4c70934f235df1440c"
last modified
2007-12-04
reason for most recent change
the initial version.
short description

Traceset of Bluetooth contact traces collected in Singapore from end 2005 to early 2006.

description

This traceset contains Bluetooth contact traces collected in Singapore. 12 contact probes-3 static and 9 mobile-collected data from end 2005 to early 2006. We discovered over 10,000 unique devices and recorded over 350,000 contacts in this duration.

release date
2007-09-03
date/time of measurement start
2005-10-31
date/time of measurement end
2006-06-21
methodology

To allow us to get a wide variety of data we chose 12 probes. Of these 3 were static and 9 were mobile. The static devices were customized, line powered, Bluetooth access points running on embedded Linux and these were placed in three of the busiest lecture theaters on National University of Singapore campus. The 9 mobile probes were chosen to get as diverse a sampling of various social behavior patterns. 5 students on campus, 2 faculty members and 2 students who lived off campus carried mobile phones with the software that logged the Bluetooth device discoveries.

After collecting the data we did realize that our choices did give us a varied set of behaviors. As expected, the 2 students living off campus logged the most contacts, logging around 170 distinct devices for every man day logged. Interestingly, the static probes discovered the least number of distinct devices per day. The maximum was 13.2 distinct devices per day. This clearly highlights the importance of mobility to increasing the potential for opportunistic data relay algorithms.

disruptions to data collection
The main challenge faced in collecting the data was the finite battery life. Due to Bluetooth device discovery being an energy consuming process, phones would run out of power and the logging would stop. Often phones needed to be recharged every day in order to log continuously. Despite our persistent attempts to remind the probes to keep the logging program switched on at all times, the participants had a tendency to switch it on in crowded areas which skewed the data. The logging program would also crash from time to time. This error could occur a few minutes or a few days after the logging program was switched on. Despite our best efforts we were unable to avoid this error which seems to have originated from the OS of the phone. On some of the phones when the program crashed an audible beep was made which reminded volunteers to turn on the program. Due to the format in which the data was logged we were unable to ascertain the exact times for the occurence of these errors. However, we estimate from our data that on average the mobile probes were not logging for 24.5% of the time. From interviews with our probes, these outages seem to have been random and uniformly distributed over time. While we did miss potential contacts, our logs clearly mark the beginning and ending of any period when logging was performed. During these periods all potential contacts were recorded.
sanitization

We anonymized the 'Address' field and 'Person' field by using the MD5 checksum function provided by MySQL.

 the nus/bluetooth/sql/anon_logdata trace
 how to cite this traceset