Browsing by Author "Demirbas, Ali Furkan"

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    LEAN: A Multi-Cell Smart City Simulator for the Massive Internet of Things Medium Access Control Layer
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ali Furkan Demirbas; Oyku Gurer; Zeynep Yurdasan; Ayca Akcapinar; Volkan Rodoplu; Demirbas, Ali Furkan; Yurdasan, Zeynep; Gurer, Oyku; Akcapinar, Ayca; Rodoplu, Volkan
    We develop a multi-cell smart city simulator called LEAN that is targeted at the development and testing of Medium Access Control (MAC) layer protocols for massive Internet of Things (mIoT). Our simulator supports any number of cells each of which is centered around an IoT gateway over any geographic area which is displayed on a Graphical User Interface (GUI). In addition to all of the static IoT devices that appear on the GUI our simulator keeps track of the associations of all mobile IoT devices across time and furnishes these associations to any back-end MAC protocol that is developed in the simulation environment. In contrast with general-purpose smart city simulators LEAN has relatively low time and space complexity in the number of IoT devices and is well-suited for quick testing of novel MAC protocols for massive IoT. © 2022 Elsevier B.V. All rights reserved.
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    Network Failure and Anomaly Prediction to Achieve Quality of Service (QoS) on Software-Defined Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Yaren Cilek; Ali Furkan Demirbas; Volkan Rodoplu; Demirbas, Ali Furkan; Rodoplu, Volkan; Cilek, Yaren
    We develop a predictive optimization program to resolve anomalies and failures on Software Defined Networks (SDN) proactively in order to prevent such failures before they render important services like health security and production unavailable. The previous studies on preventing network anomalies or failures took a reactive approach by which the anomalies are resolved after they occur. Our program predicts if the incoming 5G flows will cause an anomaly on the nodes by using machine learning and then leverages a linear optimization program to find the best routes for such flows to be admitted safely. Our program is network topology agnostic, hence it can be run on any topology. Since our approach resolves such anomalies proactively and makes sure the important services are always continuous and available for the communities it holds the potential to impact the design of SDNs in the near future. © 2022 Elsevier B.V. All rights reserved.