1. Mobility Modeling Capturing Key Correlations of Measured Data Christoph Lindemann University of Leipzig Department of Computer Science Johannisgasse 26 04103 Leipzig, Germany http://rvs.informatik.uni-leipzig.de/ cl@rvs.informatik.uni-leipzig.de

2. Computer Networks and Distributed SystemsUniversity of Leipzig2 Current research activities Leipzig wireless mesh testbed –Miniature testbed for wireless mesh networks with multiple radios Effective data transport protocols for multihop wireless networks –Software prototype of TCP-AP for testbed P2P applications for multihop wireless networks –Cooperative content delivery w/ and w/o mobility –Content distribution by epidemic data dissemination Mobility modeling capturing key correlations of measured data –...

3. Computer Networks and Distributed SystemsUniversity of Leipzig3 Motivation for another mobility model (1) All known mobility models are defined by construction of the sample path –Method for determining next waypoint or path –Distribution of pause times independent from previous and next path For most known mobility models –node intercontact times and duration of node contacts are derived quantitities, even if they can be explicitly measured –node intercontact times and duration of node contacts are memory-less and follow an exponential-like distribution

4. Computer Networks and Distributed SystemsUniversity of Leipzig4 Motivation for another mobility model (2) Recent studies showed: Realistic pause times of nodes follow a heavy- tailed distribution –long-range dependence issues have to considered –Distribution of pause times may well depend on previous and next paths Realistic node intercontact times follow a power-law distribution –Known mobility models based on sample path construction do not capture this effect

5. Computer Networks and Distributed SystemsUniversity of Leipzig5 Motivation for another mobility model (3) Quantities for specifying mobility pattern of individual mobile nodes –Distribution of next path (ie destination and duration) –Distribution of pause times Quantities making most impact to design of DTN and epidemic applications –Distribution of node intercontact time –Distribution of duration of node intercontacts –Time distance: shortest time a node i can pass a message to node j via several relay hops for a given mobility pattern

6. Computer Networks and Distributed SystemsUniversity of Leipzig6 Anticipated key contributions Aggregated model for node intercontacts Model node intercontacts and contact durations by a single-server queue Implicitely model node mobility by arrival and departure Research tasks to be tackled Explore correlation structure of quantities specifying the mobility pattern in measured mobility traces Deriving synthetic “encounter generator” capturing key correlations of measured mobility pattern Derive measures of interest such as node intercontact times etc. from analysis of single server queueing model

7. Computer Networks and Distributed SystemsUniversity of Leipzig7 Related previous work Klemm, Lindemann, Lohmann, Modeling IP Traffic Using the Batch Markovian Arrival Process, Performance Eval. 2003. www.ip2bmap.de Klemm, Lindemann, Vernon, Waldhorst, Characterizing the Query Behavior in Peer-to-Peer File Sharing Systems, ACM IMC 2004. (traces available at rvs.informatik.uni-leipzig.de/software/ Lindemann, Waldhorst, Modeling Epidemic Information Dissemination on Mobile Devices with Finite Buffers, ACM SIGMETRICS 2005.