Rodoplu, Volkan
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01.01.09.02. Elektrik- Elektronik Mühendisliği
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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70
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1385
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76
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184
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2.45
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| Journal | Count |
|---|---|
| IEEE Access | 4 |
| 16th International Conference on INnovations in Intelligent SysTems and Applications INISTA 2022 | 2 |
| 2021 Innovations in Intelligent Systems and Applications Conference ASYU 2021 | 2 |
| 2022 Innovations in Intelligent Systems and Applications Conference ASYU 2022 | 2 |
| 7th IEEE World Forum on Internet of Things WF-IoT 2021 | 2 |
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Article Citation - WoS: 15Citation - Scopus: 21Characterization of Line-of-Sight Link Availability in Indoor Visible Light Communication Networks Based on the Behavior of Human Users(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Volkan Rodoplu; Kemal Hocaoglu; Anil Adar; Rifat Orhan Cikmazel; Alper Saylam; Adar, Anil; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Hocaoglu, KemalWe characterize the line-of-sight (LOS) link availability in indoor visible light communication (VLC) networks based on the behavior of human users. The VLC link availability is impacted by humans in three distinct ways: (1) Users turn the lights on or off in each room. (2) Users may carry mobile devices. (3) Users constitute mobile obstacles that block or shadow the channel between the transmitter and the receiver. First we develop a mathematical framework for VLC link availability and a probabilistic model of the VLC network with respect to human behavior in indoor environments. Second we design a realistic multi-user indoor VLC system simulation with static and mobile VLC devices that are expected to be found in smart home environments. We present the following four sets of results: (1) We report statistics on the blockage durations of VLC links and categorize the links with respect to these statistics. (2) We demonstrate the performance of Selection Diversity versus Maximal Ratio Combining for mobile VLC devices carried by humans in a smart home setting. (3) We show that optimal LED resource allocation policies for multiple users are impacted by the VLC link blockage events caused by humans. (4) We demonstrate that the link blockage events in different rooms become dependent due to humans who transition between rooms. Based on our results we provide new directions for the design of network architectures for indoor VLC systems.Conference Object Citation - WoS: 1Citation - Scopus: 3Design of a Low-Cost Visible Light Communication (VLC) System for Music and Video Streaming(Institute of Electrical and Electronics Engineers Inc., 2019) Kemal Hocaoglu; Anil Adar; Yigit Ahmet Arıkök; Volkan Rodoplu; Adar, Anil; Hocaoglu, Kemal; Rodoplu, Volkan; Arikok, Yigit AhmetIn this paper we describe our design of an end-to-end Visible Light Communication (VLC) system prototype that is able to stream music and video. Our system is able to transmit and receive video and audio signals and is capable of communicating at a data rate of 7.14 Mbps at a distance of 6.7 cm at a much lower cost for the transmitter and the receiver circuitry compared with existing VLC designs. The channel attenuation and SNR tests that we have conducted on our design demonstrate the VLC channel characteristics. In order to assess the design for deployment in more realistic settings we also compute the transmit power that will be required if our prototype system were scaled to transmit over the dimensions of a room and show that the required transmit power is reasonable. Furthermore our audio and video fidelity tests quantify the user experience via the Mean Opinion Score (MOS) metric. Finally alternative transmitter and receiver designs are discussed as possible improvements for future work. This prototype constitutes a first step towards wide-scale deployment of VLC technology for downlink video transmission in indoor spaces in the smart cities of the near future. © 2020 Elsevier B.V. All rights reserved.Master Thesis Nesnelerin interneti için altuzay tabanlı uygulamaya özgü hata metriği öykünmesi ile bütünleşik tahminleme-çizelgeleme(2021) Helva, Alperen; Rodoplu, Volkan; Güzeliş, CüneytThe massive access problem refers to the challenge posed in uplink wireless communication from a massive number of Internet of Things (IoT) devices to an IoT gateway, base station or access point. In this thesis, first, we present an Application-Specific Error Function (ASEF), which measures the impact of the forecasting error on network performance for Joint Forecasting-Scheduling (JFS). Second, we propose a Neural Network (NN)-based emulation of ASEF on a subspace of forecasting errors, which we call ``Emulation of ASEF'' (E-ASEF), and develop a novel algorithm, ``Motion On a Subspace under Adaptive Learning rate'' (MOSAL), which moves on this subspace of forecasting errors while minimizing the application-specific error metric at the output of MAC-layer scheduling. Our results show that MOSAL improves the performance of the JFS system while achieving a low execution time. This work paves the way to achieving high network performance at an IoT Gateway that has a massive number of IoT devices in its coverage area.Article Citation - WoS: 4Citation - Scopus: 7Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things(Academic Press, 2022) Mert Nakıp; Erdem Çakan; Volkan Rodoplu; Cüneyt Güzeliş; Çakan, Erdem; Rodoplu, Volkan; Güzeliş, Cüneyt; Nakıp, MertThe Massive Access Problem of the Internet of Things (IoT) occurs at the uplink Medium Access Control (MAC) layer when a massive number of IoT devices seek to transfer their data to an IoT gateway. Although recently proposed predictive access solutions that schedule the uplink traffic based on forecasts of IoT device traffic achieve high network performance these solutions depend heavily on the performance of forecasters. Hence the design and selection of forecasting schemes are key to enabling massive access for such predictive access solutions. To this end in this paper first we develop a framework that emulates the relationship between the IoT device class composition in the coverage area of an IoT gateway and the resulting network performance by virtue of an Artificial Neural Network (ANN). Second based on this framework we develop the Dynamic Automatic Forecaster Selection (DAFS) method which selects the best-performing forecasting scheme for predictive access in particular for Joint Forecasting-Scheduling (JFS) in a manner that adapts dynamically to a changing number of IoT devices in each device class in the coverage area. We evaluate the performance of DAFS via simulations and show that our method is able to achieve at least 80% of the best performance that can be attained for both throughput and energy consumption. Furthermore we demonstrate that DAFS is robust with respect to the selection of architectural parameters and has a reasonable computation time for real-time IoT applications. These results imply that DAFS holds the potential for practical implementation at IoT gateways in order to enable massive access under a dynamically changing composition of IoT devices. © 2022 Elsevier B.V. All rights reserved.Conference Object Machine Learning Enabled Sleep Time Estimation (MLE-STE) Architecture for Indoor Positioning in Energy-Efficient Mobile Internet of Things(Institute of Electrical and Electronics Engineers Inc., 2023) Alper Saylam; Cüneyt Güzeliş; Volkan Rodoplu; Guzelis, Cuneyt; Rodoplu, Volkan; Saylam, AlperIndoor positioning and tracking systems require not only accurate position estimates of mobile IoT devices but also energy efficiency in order to maximize the battery life of the mobile IoT device. The contribution of this paper is the design of a machine learning enabled indoor positioning and tracking system in which artificial intelligence is utilized for the estimation of the duration for which a mobile IoT device needs to sleep in order to conserve energy. Our Machine Learning Enabled Sleep Time Estimation (MLE-STE) architecture is comprised of the following stages: First it forms the forecast of the nearfuture trajectory of the mobile IoT device. Second based on these forecasts it determines the optimal sleep duration subject to the constraint of a maximum tolerable forecasting error. We demonstrate that our MLE-STE architecture outperforms both of the following state-of-the-art algorithms in this area: Positioning Interval based on Displacement (PID) and Dynamic Positioning Interval Based on Reciprocal Forecasting Error (DPI-RFE). This work represents a significant advance in the development of accurate indoor positioning and tracking algorithms that target the energy efficiency of mobile IoT devices. © 2024 Elsevier B.V. All rights reserved.Conference Object Citation - Scopus: 1Network 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, YarenWe 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.Conference Object Citation - Scopus: 4Energy-Efficient Indoor Positioning for Mobile Internet of Things Based on Artificial Intelligence(Institute of Electrical and Electronics Engineers Inc., 2021) Alper Saylam; Rifat Orhan Cikmazel; Nur Kelesoglu; Mert Nakıp; Volkan Rodoplu; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Kelesoglu, Nur; Nakip, MertWe develop an energy-efficient indoor positioning system based on Artificial Intelligence (AI). In our system first at the positioning layer a Multi-Layer Perceptron (MLP) estimates the current indoor position of an IoT device based on positioning indicators obtained from the anchors. Second at the forecasting layer a pair of MLPs estimate the future positions of the device based on the past position estimates obtained when the device woke up as well as the forecast positions of the device during the sleep periods. Third the device is awakened to send a positioning beacon at intervals over which a significant displacement is predicted to occur by the forecasting layer. Our results demonstrate that our indoor positioning system saves significant energy via adaptive sleep cycles whose duration is determined by the prediction of a significant displacement. This work establishes a foundation for indoor positioning that utilizes AI-based positioning and trajectory forecasting. © 2022 Elsevier B.V. All rights reserved.Article Citation - Scopus: 2Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture(Elsevier B.V., 2024) Erdem Çakan; Volkan Rodoplu; Cüneyt Güzeliş; Rodoplu, Volkan; Guzelis, Cuneyt; Cakan, ErdemThe Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA LSTM and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway's coverage zone. © 2024 Elsevier B.V. All rights reserved.Master Thesis Mobil nesnelerin interneti için makine öğrenimine dayalı enerji verimli iç mekanda konumlandırma(2022) Saylam, Alper; Rodoplu, Volkan; Güzeliş, CüneytYapay Zekâ, mobil Nesnelerin İnterneti (IoT) cihazlarının konumlandırma doğruluğu ve enerji tüketimi ile ilgili zorlukların üstesinden gelmede iç mekan konumlandırma ve izleme sistemleri için umut verici bir çözümdür. Konumlandırma literatüründeki geçmiş çalışmaların çoğu, konumlandırma doğruluğunu artırmaya ve reaktif yaklaşımlar yoluyla iletim enerji tüketimi sorununu çözmeye odaklanmıştır. Bu geçmiş yaklaşımların aksine, yaklaşımımız bir mobil IoT cihazının gelecekteki yörüngesini tahmin etmek ve bu yörünge tahminlerine dayalı olarak konumlandırma aralığını belirlemektir. Yaklaşımımız, iç mekan konumlandırma ve izleme sistemlerinde bir mobil IoT cihazının iletim enerji tüketimini azaltmayı amaçlamaktadır. Bu tezde ilk olarak, enerji verimli iç mekan konumlandırma elde etmek için ``Karşılıklı Tahmin Hatasına Dayalı Dinamik Konumlandırma Aralığı (DPI-RFE)'' adlı bir algoritma geliştirilmiştir. Mevcut iç mekan konumlandırma algoritmalarının aksine, DPI-RFE, karşılıklı anlık tahmin hatasına dayalı olarak konumlandırma aralığını uyarlar, böylece iletim enerji tüketimini tahmin hatasına karşı dinamik olarak değiştirir. Toplam iletim enerji tüketimi ve ortalama tahmin hatası açısından DPI-RFE'nin performansı Sabit Konumlandırma Aralığı (CPI) ve Yer Değiştirme tabanlı Konumlandırma Aralığı (PID) ile karşılaştırılmaktadır. Sonuçlarımız, rekabetçi bir ortalama tahmin hatası performansı elde ederken, DPI-RFE'nin iletim enerji tüketimi açısından bu kıyaslama algoritmalarının her ikisinden de önemli ölçüde daha iyi performansa sahip olduğunu göstermektedir. İkincisi, mobil IoT cihazları için enerji verimliliğini daha da artırmak için ``Makine Öğrenimi Etkinleştirilmiş Uyku Süresi Tahmini (MLE-STE)' adlı yeni bir mimari geliştirilmiştir. MLE-STE mimarimiz, mobil cihazın yörüngesini tahmin eder ve hedef maksimum tahmin hatasına tabi olan tahmin pozisyonlarına göre mobil cihazın izin verilen maksimum uyku süresini tahmin eder. MLE-STE mimarimizin performansı, Yer Değiştirmeye Dayalı Konumlandırma Aralığı (PID) ve Karşılıklı Tahmin Hatasına Dayalı Dinamik Konumlandırma Aralığı (DPI-RFE) algoritmalarının performansıyla, iletim enerji tüketimi ve tahmin hatası açısından karşılaştırılmaktadır. Sonuçlarımız, MLE-STE mimarisinin hem PID hem de DPI-RFE'den daha iyi performansa verdiğini göstermektedir. Bu tez, mobil IoT cihazları için yüksek enerji verimliliği sağlayan makine öğrenimi tabanlı iç mekan konumlandırma ve izleme sistemlerinin geliştirilmesine giden yolu açmaktadır.Article Citation - WoS: 12Citation - Scopus: 18Multi-Channel Joint Forecasting-Scheduling for the Internet of Things(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Volkan Rodoplu; Mert Nakip; Roozbeh Qorbanian; Deniz Tursel Eliiyi; Rodoplu, Volkan; Qorbanian, Roozbeh; Nakip, Mert; Eliiyi, Deniz TurselWe develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance thus obviating contention collision and handshaking which are found in reactive protocols. In this paper we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system first we design a heuristic called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL) that solves the general form of the scheduling problem. Second for the special case of identical channels we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB) both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.
