PUMA publications for /user/jaeschke/peoplerankhttps://puma.uni-kassel.de/user/jaeschke/peoplerankPUMA RSS feed for /user/jaeschke/peoplerank2024-03-19T10:42:34+01:00PeopleRank: Social Opportunistic Forwardinghttps://puma.uni-kassel.de/bibtex/2ecc71dabeaae2b46aef3afee17e3dcdc/jaeschkejaeschke2011-12-06T18:38:00+01:00peoplerank search social web pagerank ranking <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Abderrahmen Mtibaa" itemprop="url" href="/author/Abderrahmen%20Mtibaa"><span itemprop="name">A. Mtibaa</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin May" itemprop="url" href="/author/Martin%20May"><span itemprop="name">M. May</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christophe Diot" itemprop="url" href="/author/Christophe%20Diot"><span itemprop="name">C. Diot</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mostafa H. Ammar" itemprop="url" href="/author/Mostafa%20H.%20Ammar"><span itemprop="name">M. Ammar</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">INFOCOM, 2010 Proceedings IEEE</span>, </em></span><em>Seite <span itemprop="pagination">1--5</span>. </em><em><span itemprop="publisher">IEEE</span>, </em>(<em><span>März 2010<meta content="März 2010" itemprop="datePublished"/></span></em>)Tue Dec 06 18:38:00 CET 2011INFOCOM, 2010 Proceedings IEEEmarch1--5PeopleRank: Social Opportunistic Forwarding2010peoplerank search social web pagerank ranking 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.