OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
The ONE simulator for DTN protocol evaluation
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Modeling mobility in disaster area scenarios
Performance Evaluation
Understanding urban interactions from bluetooth phone contact traces
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
Tracking of mobile devices through Bluetooth contacts
Proceedings of the ACM CoNEXT Student Workshop
Trace-based mobility modeling for multi-hop wireless networks
Computer Communications
Fast track article: From encounters to plausible mobility
Pervasive and Mobile Computing
Automatic inference of movements from contact histories
Proceedings of the ACM SIGCOMM 2011 conference
On the levy-walk nature of human mobility
IEEE/ACM Transactions on Networking (TON)
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Studies of mobile wireless protocols benefit from real-world traces that measure and record node locations over time. Such traces form an essential part of evaluation studies for many types of mobile and wireless networks, including but not limited to opportunistic networks.Mobility information provides the possibility of richer simulations in such studies, while contact information, irretrievably, loses considerable details like the exact locations of the nod es and their velocities. Despite this, contact information can be measured more easily compared to detailed locations of the nodes. We bridge the gap between relatively more available contact traces and potentially more useful mobility traces by presenting a solution for inferring plausible mobility from contact information. Our technique uses a simple non-linear optimization approach to formulate this as a feasibility problem with fundamental constraints on the mobility of the nodes. We introduce our technique and its underlying assumptions, and evaluate its performance in recovering mobility from synthetic and real traces. We also show that our approach brings orders of magnitude improvements in inference performance compared to state of the art.