Realistic Traces for DTN research
Dartmouth
This connectivity data set has been inferred from traces collected in the Wi-Fi access network of Dartmouth College.The traces track users’ sessions in the wireless network, notingthe time at which nodes associate and dissociate from access points. Although the Dartmouth data is not from a DTN network, we use it because it is perhaps the richest data set publicly available that tracks users in a campus setting, and because of its quality.
iMotes
Chaintreau et al. used iMotes to acquire proximity contacts that occurred between participants in the student workshop at the Infocom 2005 research conference. Students were asked to carry one of these sensors in their pocket at all times. Due to Bluetooth’s short range, authors logged instances when people were close to each other (typically within 10 meters). They collected data from 41 iMotes over 3 days. The devices performed Bluetooth inquiry scans every 2 minutes. For each pair of nodes (i, j), we considered that i and j were in contact if either one saw the other.
MIT Data
The Reality Mining experiment [3] conducted at MIT captured proximity information from 97 subjects over the course of an academic year. Each participant had an application running on their mobile phone to record proximity with others through periodic Bluetooth scans (every 5 minutes) in a similar fashion to that of the iMote experiment. We used the first 95 days of data.
References:
[1], Characterizing Pairwise Inter-contact Patterns in Delay Tolerant Networks
This connectivity data set has been inferred from traces collected in the Wi-Fi access network of Dartmouth College.The traces track users’ sessions in the wireless network, notingthe time at which nodes associate and dissociate from access points. Although the Dartmouth data is not from a DTN network, we use it because it is perhaps the richest data set publicly available that tracks users in a campus setting, and because of its quality.
iMotes
Chaintreau et al. used iMotes to acquire proximity contacts that occurred between participants in the student workshop at the Infocom 2005 research conference. Students were asked to carry one of these sensors in their pocket at all times. Due to Bluetooth’s short range, authors logged instances when people were close to each other (typically within 10 meters). They collected data from 41 iMotes over 3 days. The devices performed Bluetooth inquiry scans every 2 minutes. For each pair of nodes (i, j), we considered that i and j were in contact if either one saw the other.
MIT Data
The Reality Mining experiment [3] conducted at MIT captured proximity information from 97 subjects over the course of an academic year. Each participant had an application running on their mobile phone to record proximity with others through periodic Bluetooth scans (every 5 minutes) in a similar fashion to that of the iMote experiment. We used the first 95 days of data.
References:
[1], Characterizing Pairwise Inter-contact Patterns in Delay Tolerant Networks
