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dc.contributor.advisorUlusoy, Özgür
dc.contributor.authorAmira, Fouad
dc.date.accessioned2018-09-14T09:32:41Z
dc.date.available2018-09-14T09:32:41Z
dc.date.copyright2018-09
dc.date.issued2018-09
dc.date.submitted2018-09-19
dc.identifier.urihttp://hdl.handle.net/11693/47880
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 38-42).en_US
dc.description.abstractFog computing is a new paradigm that aims to extend the concept of cloud computing to the edge of the network, providing the end users network with extra storage and processing power. One big contribution of Fog computing is in the context of Wireless Sensor Networks (WSNs). WSNs consist of cheap, battery powered and simple processing devices that make it fall short in handling relatively complex processes.Therefore, applying fog computing to WSNs will fill the gap between the cloud and the network and by that, it will enable computationally extensive operations which were earlier possible only at the cloud side. In our work, we exploit the processing power provided by the Fog to minimize the power consumption of WSNs for forest fire detection through the use of data mining techniques. The Fog layer uses the data generated by the network such as temperature, humidity, rain, etc., to train a model that predicts the probability of forest fires. Next, the Fog layer uses this model to predict the mode of operation of the network based on the current condition of the environment. While a high predicted probability of forest fire results in an increased activity of the WSN, a low fire probability results in a reduced network activity. As a result, our proposed model optimizes the energy consumption within the WSN and improves the detection time of forest fires.en_US
dc.description.statementofresponsibilityby Fouad Amira.en_US
dc.format.extentix, 42 leaves : charts (some color) ; 30 cm.en_US
dc.language.isoen_USen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWireless Sensor Networksen_US
dc.subjectForestfire Detectionen_US
dc.subjectForestfire Predictionen_US
dc.subjectMachine Learningen_US
dc.subjectFog Computingen_US
dc.titleFog supported wireless sensor networks for forest fire detectionen_US
dc.title.alternativeOrman yangınlarını algılamak için sis destekli kablosuz sensör ağlaren_US
dc.typeTechnical Reporten_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB154858


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