The advent of cheap, ubiquitous, portable, multi-radio devices means that mobile wireless networking will be the predominant future method for network access and edge network formation. Many of these devices will have mobility that is dictated by the movement of the humans who carry them. Hence, capturing real-world human mobility patterns is essential for improving our understanding of the new networks and communication opportunities that will arise. However, even small-scale measurements carry a significant organizational overhead in preparing, distributing and collecting contact loggers, not to mention non-technical issues related to legality and participant consent. This research program addresses two key issues: the recovery of mobility information from contact traces, and the transformation of traces collected under one set of circumstances to realistic alternative scenarios. The basis for the recovery work is a dynamic, force-based heuristic to recover plausible mobility from a contact trace. The basis for the transformation techniques is the combination of the original trace and the plausible mobility, plus models for real-world alternative settings. In both cases, the work addresses fundamental questions about efficient computational methods and the limits to computational capability for recovery and transformation. Answers to these two questions will allow researchers to collect and transform traces so as to extract maximum value from expensive measurement methods, enabling trace-driven simulation of mobile wireless networks to play a central role beside analysis and direct experimentation. In addition, the trace collection experiments used in validation will provide a valuable resource to researchers in mobile networking.

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Georgia Tech Research Corporation
United States
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