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Geometric Abstractions for Information Processing in Sensor Networks

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dc.contributor.advisor Gao, Jie en_US
dc.contributor.author Sarkar, Rik en_US
dc.contributor.other Department of Computer Science en_US
dc.date.accessioned 2012-05-15T18:06:48Z
dc.date.accessioned 2015-04-24T14:53:09Z
dc.date.available 2012-05-15T18:06:48Z
dc.date.available 2015-04-24T14:53:09Z
dc.date.issued 2010-05-01
dc.identifier Sarkar_grad.sunysb_0771E_10099.pdf en_US
dc.identifier.uri http://hdl.handle.net/1951/55616 en_US
dc.identifier.uri http://hdl.handle.net/11401/72663 en_US
dc.description.abstract Computerized 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. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Computer Science en_US
dc.subject.other Computational Geometry, Information Processing, Routing, Sensor Network en_US
dc.title Geometric Abstractions for Information Processing in Sensor Networks en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Joseph Mitchell en_US
dc.contributor.committeemember Samir R. Das en_US
dc.contributor.committeemember Alon Efrat. en_US


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