Please use this identifier to cite or link to this item: https://doi.org/10.1145/1161089.1161100
DC FieldValue
dc.titleAnalysis and implications of student contact patterns derived from campus schedules
dc.contributor.authorSrinivasan, V.
dc.contributor.authorMotani, M.
dc.contributor.authorWei, T.O.
dc.date.accessioned2013-07-23T09:27:44Z
dc.date.available2013-07-23T09:27:44Z
dc.date.issued2006
dc.identifier.citationSrinivasan, V., Motani, M., Wei, T.O. (2006). Analysis and implications of student contact patterns derived from campus schedules. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM 2006 : 86-97. ScholarBank@NUS Repository. https://doi.org/10.1145/1161089.1161100
dc.identifier.isbn1595932860
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43202
dc.description.abstractCharacterizing mobility or contact patterns in a campus environment is of interest for a variety of reasons. Existing studies of these patterns can be classified into two basic approaches - model based and measurement based. The model based approach involves constructing a mathematical model to generate movement patterns while the measurement based approach measures locations and proximity of wireless devices to infer mobility patterns. In this paper, we take a completely different approach. First we obtain the class schedules and class rosters from a university-wide Intranet learning portal, and use this information to infer contacts made between students. The value of our approach is in the population size involved in the study, where contact patterns among 22341 students are analyzed. This paper presents the characteristics of these contact patterns, and explores how these patterns affect three scenarios. We first look at the characteristics from the DTN perspective, where we study inter-contact time and time distance between pairs of students. Next, we present how these characteristics impact the spread of mobile computer viruses, and show that viruses can spread to virtually the entire student population within a day. Finally, we consider aggregation of information from a large number of mobile, distributed sources, and demonstrate that the contact patterns can be exploited to design efficient aggregation algorithms, in which only a small number of nodes (less than 0.5%) is needed to aggregate a large fraction (over 90%) of the data. Copyright 2006 ACM.
dc.sourceScopus
dc.subjectContact patterns
dc.subjectDelay tolerant networking
dc.subjectMobile social software
dc.subjectVirus spread
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1145/1161089.1161100
dc.description.sourcetitleProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
dc.description.volume2006
dc.description.page86-97
dc.identifier.isiut000282909700009
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