Pehlivan, Buse
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Araş.Gör.
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01.01.09.02. Elektrik- Elektronik Mühendisliği
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| Journal | Count |
|---|---|
| 2020 Innovations in Intelligent Systems and Applications Conference ASYU 2020 | 1 |
| Journal of Information and Telecommunication | 1 |
| Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications | 1 |
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Article Citation - Scopus: 10Multi-Channel Subset Iteration with Minimal Loss in Available Capacity (MC-SIMLAC) Algorithm for Joint Forecasting-Scheduling in the Internet of Things(Innovative Information Science and Technology Research Group, 2022) Arif Kerem Dayı; Volkan Rodoplu; Mert Nakıp; Buse Pehlivan; Cüneyt Güzeliş; Dayı, Arif Kerem; Rodoplu, Volkan; Güzeliş, Cüneyt; Pehlivan, Buse; Nakip, MertThe Massive Access Problem of the Internet of Things (IoT) refers to the problem of scheduling the uplink transmissions of a massive number of IoT devices in the coverage area of an IoT gateway. Joint Forecasting-Scheduling (JFS) is a recently developed methodology in which an IoT gateway forms predictions of the future uplink traffic generation pattern of each IoT device in its coverage area via machine learning algorithms and uses these predictions to schedule the uplink traffic of all of the IoT devices in advance. In this paper we develop a novel algorithm which we call “Multi-Channel Subset Iteration with Minimal Loss in Available Capacity” (MC-SIMLAC) for multi-channel joint forecasting-scheduling. Our multi-channel scheduling algorithm iterates over subsets of all of the bursts of IoT device traffic and selects channel-slot pairs by targeting the minimization of loss in total available capacity. In this regard our algorithm contrasts sharply with Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL) which is the best-performing heuristic that has been developed so far for multi-channel JFS. In the general case our algorithm outperforms MC-LAPAL especially when wireless links operate in the power-limited regime and the number of devices is large. For the special case of identical channels our algorithm achieves a performance that is closer than MC-LAPAL to that of the optimal scheduler. Furthermore we prove that the time complexity and the space complexity of MC-SIMLAC in the worst case are polynomial in each of the system parameters which indicates practical feasibility. These results pave the way to the widespread use of multi-channel joint forecasting-scheduling at IoT gateways in the near future. © 2022 Elsevier B.V. All rights reserved.Master Thesis Yazılım tanımlı ağlar için hizmet kalitesi tabanlı öngörücü rotalama(2024) Pehlivan, Buse; Rodoplu, VolkanIn this thesis, we develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) software-defined networks (SDNs). First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic. Second, we develop our Predictive Dynamic Multi-Flow Routing (PD-MFR) algorithm for QoS flows based on this optimization model. In PD-MFR, first, the network forms predictions of the aggregate eMBB traffic flow generation rate between each source-destination pair over a routing window and makes reservations for each such flow on the upcoming routing window. Second, delay-tolerant mMTC flows are scheduled to be routed to fill up the residual capacities that remain after the reservations for the eMBB flows have been made. Third, URLLC flows are routed reactively. We demonstrate the performance of our PD-MFR algorithm when Autoregressive Integrated Moving Average (ARIMA) and Multi-Layer Perceptron (MLP) models are used in order to forecast the eMBB flow generation rates. Furthermore, we measure the performance of PD-MFR against the benchmark QoS-Shortest Path Algorithm (QoS-SPA) in which all of the QoS flows are routed reactively. Our results show that PD-MFR outperforms QoS-SPA significantly with respect to the percentage of flows delivered. This work represents an advance in QoS routing algorithms based on network traffic prediction geared towards the design of next-generation SDNs.Conference Object Citation - Scopus: 3Real-Time Implementation of Mini Autonomous Car Based on MobileNet - Single Shot Detector(Institute of Electrical and Electronics Engineers Inc., 2020) Buse Pehlivan; Ceren Kahraman; Deniz Kurtel; Mert Nakıp; Cüneyt Güzeliş; Kurtel, Deniz; Kahraman, Ceren; Guzelis, Cuneyt; Pehlivan, Buse; Nakip, MertIn this paper in order to realize a prototype of an autonomous vehicle we present a framework that consists of convolutional neural networks and image processing methods. The study is comprised of two main parts as software and hardware. In the hardware part a small-sized smart video car kit is used as the prototype of the autonomous car. This programmable tool consists of Raspberry Pi servo motors and a USB webcam whose angle of vision is equal to 120°. In the software part we propose an algorithm in which we use Convolutional Neural Networks to detect the objects (vehicles pedestrians and traffic signs) and Hough transformation to detect the road lanes. Based on the outputs of the object and lane detections the system decides the speed and the direction of the car in real-time. In our results the vehicle performs autonomous driving in the scaled real-world application. © 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 1Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks(Taylor and Francis Ltd., 2025) Buse Pehlivan; Volkan Rodoplu; Engincan Tunçay; Dilara Eraslan; Rodoplu, Volkan; Pehlivan, Buse; Tunçay, Engincan; Eraslan, DilaraWe develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) SDNs. First we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC) enhanced Mobile Broadband (eMBB) and massive Machine-Type Communication (mMTC) traffic. Second we develop our Predictive Dynamic Multi-Flow Routing (PD-MFR) algorithm for QoS flows based on this optimization model. In PD-MFR first the network forms predictions of the aggregate eMBB traffic flow generation rates and makes reservations for the flows on the upcoming routing window. Second delay-tolerant mMTC flows are scheduled to be routed to fill up the residual capacities that remain after the eMBB flow reservations. Third URLLC flows are routed reactively. We demonstrate the performance of our PD-MFR algorithm when Autoregressive Integrated Moving Average (ARIMA) and Multi-Layer Perceptron (MLP) models are used in forecasting the eMBB flow generation rates. We measure the performance of PD-MFR against the benchmark QoS-Shortest Path Algorithm (QoS-SPA) in which all of the QoS flows are routed reactively and show that PD-MFR outperforms QoS-SPA significantly. This work advances the state of the art in QoS routing algorithms based on network traffic prediction geared towards next-generation SDNs. © 2025 Elsevier B.V. All rights reserved.
