Extracting a Mobility Model from Real User Traces

By Minkyong Kim, David F. Kotz, S. Kim. Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications, 2006

619 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Transportation

View Paper: https://www.semanticscholar.org/paper/927c9a31820eb1e3d2b2d9d39101ceecb1b73477

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Citations in the corpus

Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016, methodology): “The lightweight triangulation method [273] can be used to estimate the next location of each phone user.”
Modeling Time-Variant User Mobility in Wireless Mobile Networks (2007, background): “This is along the same line suggested in [18]: Time-variant behavior has to be explicitly modeled in order to capture the changes of behavior across time.”
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, methodology): “As proposed in [14], we apply a 2-D Gaussian distribution weighted by the residence time at each GPS point.”
Recruitment Framework for Participatory Sensing Data Collections (2010, background): “There has been a significant amount of work in regards to coming up with clustering algorithms to summarize the most significant destinations of a user based on location traces [12,13,14].”
Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing (2007, positive): “Through trace-driven simulations of epidemic routing, we .nd that the epidemic performance predicted by traces generated with this .ner-grained route-level model is much closer to the actual performance that would be realized in the operational system than traces generated using the…”
SLAW: A New Mobility Model for Human Walks (2009, methodology): “Like SLAW, ORBIT explicitly models the heterogeneously bounded walkabout areas of different people (i.e., F4).”
Mobility models for vehicular ad hoc networks: a survey and taxonomy (2009, methodology): “[15] describe a measurement technique for extracting user mobility characteristics also from coarse-grained wireless traces.”
Human mobility modeling at metropolitan scales (2012, methodology): “Daily range, or the “diameter of a convex hull,” has been shown to be a useful tool for characterizing human mobility patterns [18, 20].”
BreadCrumbs: forecasting mobile connectivity (2008, background): “Yoon [28] concentrated, as did Kim et al [14], on deriving realistic mobility models from actual user mobility traces.”
DP-WHERE: Differentially private modeling of human mobility (2013, background): “Daily range, or the maximum distance between any two points a person visits in a day, has proven useful for characterizing human mobility patterns [17, 18, 21].”
On the Levy-Walk Nature of Human Mobility (2008, methodology): “At Dartmouth [33] and UCSD [40], mobility traces of users are collected based on the association information of mobile handheld devices (e.g., PDAs and VoIP phones) that access wireless LAN access points (APs).”