2005-27 - UB-CSE TECHNICAL REPORT1On Profiling Mobility and...

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Unformatted text preview: UB-CSE TECHNICAL REPORT1On Profiling Mobility and Predicting Locations ofCampus-wide Wireless Network UsersJoy Ghosh, Matthew J. Beal, Hung Q. Ngo, Chunming QiaoDepartment of Computer Science and EngineeringUniversity at Buffalo, The State University of New York201 Bell Hall, Buffalo, NY 14260-2000Email:{joyghosh, mbeal, hungngo, qiao}@cse.buffalo.eduAbstract In this paper, we analyze a year long wirelessnetwork users mobility trace data collected on ETHZurich campus. Unlike earlier work in [9], [21], [35],we profile the movement pattern of wireless users andpredict their locations. More specifically, we show thateach network user regularly visits a list of places, such as abuilding (also referred to as hubs) with some probability.The daily list of hubs, along with their corresponding visitprobabilities, are referred to as amobility profile. We alsoshow that over a period of time (e.g., a week), a usermay repeatedly follow a mixture of mobility profiles withcertain probabilities associated with each of the profiles.Our analysis of the mobility trace data not only validate theexistence of our so-called sociological orbits [13], but alsodemonstrate the advantages of exploiting it in performinghub-level location predictions. Moreover, such profile basedlocation predictions are found not only to be more precisethan a common statistical approach based on observedhub visitation frequencies, but also shown to incur amuch lower overhead. We further illustrate the benefitof profiling users mobility by discussing relevant workand suggesting applications in different types of wirelessnetworks, including mobile ad hoc networks.Index Terms WLAN mobility trace analysis, Sociolog-ical orbits, Mobility profiles, Location prediction, Mobilewireless networksI. INTRODUCTIONThe mobility of users forming a mobile wirelessnetwork causes changes in the network connectivity andmay even lead to intermittently connected networks.On one hand, nodal mobility may increase the overallnetwork capacity [15]. On the other hand, it may makeit challenging to locate users and route messages withinthe network.Many researchers have tried to model practical mo-bility in various ways to achieve different goals. Earlierwork on mobility modeling [8] was done mostly withMobile Ad hoc NETworks (MANET) in mind. Forexample, some [27] used mobility pattern analysis tominimize radio link changes via appropriate selectionof next hop within radio range. While the authors in[32], [36] performed physical location prediction viacontinuous short-term and short-range tracking of usermovement, we had leveraged on our assumptions onsociological orbits to perform efficient routing withinMANETs [13], [14]. More recently, Intermittently Con-nected Mobile Ad hoc Networks (ICMAN) (or in gen-eral, Delay Tolerant Networks (DTN)) have received alot of interest. For example, researchers [7], [38] havesuggested the concept of controlled mobility to aid inmobile ad hoc routing. Literature has also proposedmobile ad hoc routing....
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This note was uploaded on 05/27/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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2005-27 - UB-CSE TECHNICAL REPORT1On Profiling Mobility and...

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