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dc.identifier.urihttp://hdl.handle.net/1951/55616
dc.identifier.urihttp://hdl.handle.net/11401/72663
dc.description.sponsorshipThis work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.en_US
dc.formatMonograph
dc.format.mediumElectronic Resourceen_US
dc.language.isoen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dc.typeDissertation
dcterms.abstractComputerized devices are becoming smaller and more ubiquitous. Equally importantly, they are becoming more interconnected. A Sensor Network is a model for such interconnected systems. Each sensor device obtains and stores information that is potentially useful to others. The challenge is to efficiently search and deliver the important information to the relevant parties. Given the large number of devices and corresponding quantities of data, this is not easy. Fortunately for us, communication is efficient and fast when addressing nearby devices. This permits us to utilize their relative locations to construct efficient methods.The proximity and location information can be leveraged through the use of geometry. The complexity of a network and data hide simpler geometric structures that are not obvious at first sight. The objective in this dissertation is to identify such concealed structures that can be useful and can be computed in the network. An abstract structure or Abstraction helps us to understand and represent the network and data in more convenient ways. This approach is useful in managing the data in the network, as well as in managing the network itself. Its utility is demonstrated through accompanying algorithms in each part of the dissertation.
dcterms.available2012-05-15T18:06:48Z
dcterms.available2015-04-24T14:53:09Z
dcterms.contributorGao, Jieen_US
dcterms.contributorJoseph Mitchellen_US
dcterms.contributorSamir R. Dasen_US
dcterms.contributorAlon Efrat.en_US
dcterms.creatorSarkar, Rik
dcterms.dateAccepted2012-05-15T18:06:48Z
dcterms.dateAccepted2015-04-24T14:53:09Z
dcterms.dateSubmitted2012-05-15T18:06:48Z
dcterms.dateSubmitted2015-04-24T14:53:09Z
dcterms.descriptionDepartment of Computer Scienceen_US
dcterms.formatMonograph
dcterms.formatApplication/PDFen_US
dcterms.identifierhttp://hdl.handle.net/1951/55616
dcterms.identifierSarkar_grad.sunysb_0771E_10099.pdfen_US
dcterms.identifierhttp://hdl.handle.net/11401/72663
dcterms.issued2010-05-01
dcterms.languageen_US
dcterms.provenanceMade available in DSpace on 2012-05-15T18:06:48Z (GMT). No. of bitstreams: 1 Sarkar_grad.sunysb_0771E_10099.pdf: 8673779 bytes, checksum: a9640f936b15b5238821afcd1a889162 (MD5) Previous issue date: 1en
dcterms.provenanceMade available in DSpace on 2015-04-24T14:53:09Z (GMT). No. of bitstreams: 3 Sarkar_grad.sunysb_0771E_10099.pdf.jpg: 1894 bytes, checksum: a6009c46e6ec8251b348085684cba80d (MD5) Sarkar_grad.sunysb_0771E_10099.pdf.txt: 433637 bytes, checksum: d3506128e7b148f43c95ff843873024f (MD5) Sarkar_grad.sunysb_0771E_10099.pdf: 8673779 bytes, checksum: a9640f936b15b5238821afcd1a889162 (MD5) Previous issue date: 1en
dcterms.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.
dcterms.subjectComputer Science
dcterms.subjectComputational Geometry, Information Processing, Routing, Sensor Network
dcterms.titleGeometric Abstractions for Information Processing in Sensor Networks
dcterms.typeDissertation


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