• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Fog supported wireless sensor networks for forest fire detection

      Thumbnail
      View / Download
      515.3 Kb
      Author
      Amira, Fouad
      Advisor
      Ulusoy, Özgür
      Date
      2018-09
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
      209
      views
      266
      downloads
      Abstract
      Fog 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.
      Keywords
      Wireless Sensor Networks
      Forestfire Detection
      Forestfire Prediction
      Machine Learning
      Fog Computing
      Permalink
      http://hdl.handle.net/11693/47880
      Collections
      • Dept. of Computer Engineering - Master's degree 511
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy