By Topic

Stumbl: Using Facebook to collect rich datasets for opportunistic networking research

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hossmann, T. ; Commun. Syst. Group, ETH Zurich, Zurich, Switzerland ; Legendre, F. ; Nomikos, G. ; Spyropoulos, Thrasyvoulos

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices to disseminate data in a peer-to-peer manner. Designing appropriate algorithms and protocols for such networks is challenging as it requires understanding patterns of (1) mobility (who meets whom), (2) social relations (who knows whom) and (3), communication (who communicates with whom). To date, apart from few small test setups, there are no operational opportunistic networks where measurements could reveal the complex correlation of these features of human relationships. Hence, opportunistic networking research is largely based on insights from measurements of either contacts, social networks, or communication, but not all three combined. In this paper we report an experiment called Stumbl, as a step towards collecting rich datasets comprising social, mobility and communication ties. Stumbl is a Facebook application that provides participating users with a user-friendly interface to report their daily face-to-face meetings with other Facebook friends. It also logs user interactions on Facebook (e.g. comments, wall posts, likes). This way the contact graph, social graph, and activity graphs for the same set of users could be compared and analyzed. We report here preliminary results and analyses of a first experiment we have performed.

Published in:

World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE International Symposium on a

Date of Conference:

20-24 June 2011