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dc.contributor.advisorChamberland, Jean-Francois
dc.creatorOats, Mandel S
dc.date.accessioned2018-09-21T15:37:17Z
dc.date.available2018-09-21T15:37:17Z
dc.date.created2017-12
dc.date.issued2017-12-08
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/169603
dc.description.abstractThe emergence of smartphones and other highly portable Wi-Fi enabled devices offers unprecedented amounts of information leaked through Wi-Fi metadata. The constantly connected nature of Wi-Fi devices together with the intimate relationship between users and their device presents an opportunity for using a user’s device to gain information about the user themselves. Through passive data collection, without interference or the possibility of being detected, it is possible to harvest large datasets. This work looks at the possibility of inferring underlying social networks through the analysis of these metadata traces. Using spatiotemporal proximity as an indicator of friendship, findings demonstrate the ability to accurately predict underlying social structures in various simulated settings.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectSocial Networksen
dc.subjectWi-Fien
dc.subjectInferenceen
dc.subjecten
dc.titleINFERRING SOCIAL NETWORKS FROM PASSIVELY COLLECTED WI-FI METADATAen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberNarayanan, Krishna
dc.contributor.committeeMemberCantrell, Pierce
dc.contributor.committeeMemberGautam, Natarajan
dc.type.materialtexten
dc.date.updated2018-09-21T15:37:18Z
local.etdauthor.orcid0000-0002-8027-6036


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