July 29, 2010
Rhee Receives Award to Study Mobility Patterns
Dr. Injong Rhee, professor of computer science at NC State University, has been awarded $298,356 by the National Science Foundation (NSF) to support his research proposal titled “Investigation of Human Mobility: Measurement, Modeling, Analysis, Applications and Protocols.”
The award will run from August 1, 2010 through July 31, 2013.
Abstract - Simulating realistic mobility patterns of mobile devices is important for the performance study of mobile networks because deploying a real testbed of mobile networks is extremely difficult, and furthermore, even with such a testbed, constructing repeatable performance experiments using mobile devices is not trivial. Humans are a big factor in simulating mobile networks as most mobile nodes or devices (cell phones, PDAs and cars) are attached to or driven by humans. Emulating the realistic mobility patterns of humans can enhance the realism of simulation-based performance evaluation of human-driven mobile networks.
Our NSF-funded research that ends this year has studied the patterns of human mobility using GPS traces of over 100 volunteers from five different sites including university campuses, New York City, Disney World, and State Fair. This research has revealed many important fundamental statistical properties of human mobility, namely heavy-tail flight distributions, self-similar dispersion of visit points, and least-action principle for trip planning. Most of all, it finds that people tend to optimize their trips in a way to minimize their discomfort or cost of trips (e.g., distance). No existing mobility models explicitly represent all of these properties.
Our results are very encouraging and the proposed research will extend the work well beyond what has been accomplished so far. We will perform a measurement study tracking the mobility of 100 or 200 students in a campus simultaneously, and analyze the mobility patterns associated with geo-physical and social contexts of participants including social networks, interactions, spatio-temporal correlations, and meetings. We will cast the problem of mobility modeling as an optimization problem borrowing techniques from AI and Robotics that will make it easy to incorporate the statistical properties of mobility patterns commonly arising from group mobility traces. The realism of our models in expressing human mobility will surpass any existing human mobility models. We will develop new routing protocols leveraging the researched statistical properties found in real traces to optimize delivery performance.
The end products of the proposed research is (a) a new human mobility model that is capable of realistically expressing mobility patterns arising from reaction to social and geo-physical contexts, (b) their implementation in network simulators such as NS-2/3 and GloMoSim, (c) mobility traces that contain both trajectories of people in a university campus and contact times, and (d) new efficient routing protocols for mobile networks.
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