We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

PeopleRank: Social Opportunistic Forwarding

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)
Mtibaa, A. ; Thomson, Paris, France ; May, M. ; Diot, C. ; Ammar, M.

In opportunistic networks, end-to-end paths between two communicating nodes are rarely available. In such situations, the nodes might still copy and forward messages to nodes that are more likely to meet the destination. The question is which forwarding algorithm offers the best trade off between cost (number of message replicas) and rate of successful message delivery. We address this challenge by developing the PeopleRank approach in which nodes are ranked using a tunable weighted social information. Similar to the PageRank idea, PeopleRank gives higher weight to nodes if they are socially connected to important other nodes of the network. We develop centralized and distributed variants for the computation of PeopleRank. We present an evaluation using real mobility traces of nodes and their social interactions to show that PeopleRank manages to deliver messages with near optimal success rate (close to Epidemic Routing) while reducing the number of message retransmissions by 50% compared to Epidemic Routing.

Published in:

INFOCOM, 2010 Proceedings IEEE

Date of Conference:

14-19 March 2010