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dc.contributor.advisorSamuel Madden.en_US
dc.contributor.authorGil, Thomer M. (Thomer Michael)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2008-09-03T15:04:30Z
dc.date.available2008-09-03T15:04:30Z
dc.date.copyright2007en_US
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/42249
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.en_US
dc.descriptionIncludes bibliographical references (p. 53-56).en_US
dc.description.abstractWe present the design of Scoop, a system that is designed to efficiently store and query relational data collected by nodes in a bandwidth-constrained sensor network. Sensor networks allow remote environments to be monitored at very fine levels of granularity; often such monitoring deployments generate large amounts of data which may be impractical to collect due to bandwidth limitations, but which can easily stored in-network for some period of time. Existing approaches to querying stored data in sensor networks have typically assumed that all data either is stored locally, at the node that produced it, or is hashed to some location in the network using a predefined uniform hash function. These two approaches are at the extremes of a trade-off between storage and query costs. In the former case, the costs of storing data ate low, since no transmissions are required, but queries must flood the entire network. In the latter case, some queries can be executed efficiently by using the hash function to find the nodes of interest, but storage is expensive as readings must be transmitted to some (likely far away) location in the network. In contrast, Scoop monitors changes in the distribution of sensor readings, queried values, and network connectivity to determine the best location to store data. We formulate this as an optimization problem and present a practical algorithm that solves this problem in Scoop. We have built a complete implementation of Scoop for TinyOS mote [1] sensor network hardware and evaluated its performance on a 60-node testbed and in the TinyOS simulator, TOSSIM. Our results show that Scoop not only provides substantial performance benefits over alternative approaches on a range of data sets, but is also able to efficiently adapt to changes in the distribution and rates of data and queries.en_US
dc.description.statementofresponsibilityby Thomer M. Gil.en_US
dc.format.extent56 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleScoop : an adaptive indexing scheme for stored data in sensor networksen_US
dc.title.alternativeAdaptive indexing scheme for stored data in sensor networksen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc231634461en_US


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