Browsing by Author "Saylam, Alper"

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Citation - WoS: 15
Citation - Scopus: 21
Characterization 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, Kemal
We 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.
Citation - Scopus: 3
Dynamic Positioning Interval Based on Reciprocal Forecasting Error (DPI-RFE) Algorithm for Energy-Efficient Mobile IoT Indoor Positioning
(Institute of Electrical and Electronics Engineers Inc., 2021) Alper Saylam; Nur Kelesoglu; Rifat Orhan Cikmazel; Mert Nakıp; Volkan Rodoplu; Saylam, Alper; Cikmazel, Rifat Orhan; Rodoplu, Volkan; Kelesoglu, Nur; Nakip, Mert; M.S. Obaidat , S. Bilgen , K.-F. Hsiao , P. Nicopolitidis , S. Oktug , Y. Guo
We develop an algorithm called "Dynamic Positioning Interval based on Reciprocal Forecasting Error (DPIRFE)"for energy-efficient mobile Internet of Things (IoT) Indoor Positioning (IP). In contrast with existing IP algorithms DPIRFE forecasts the future trajectory of a mobile IoT device by using machine learning and dynamically adjusts the positioning interval based on the reciprocal instantaneous forecasting error thereby dynamically trading off transmit energy consumption against forecasting error. We compare the performance of DPIRFE with respect to the total transmit energy consumption and the average forecasting error against Constant Positioning Interval (CPI) and Positioning Interval based on Displacement (PID) algorithms. Our results show that DPI-RFE significantly outperforms both of these benchmark algorithms with respect to transmit energy consumption while achieving a competitive average forecasting error performance. These results open the way to the design of machine learning based trajectory forecasting algorithms that can be utilized for energy-efficient positioning in next-generation wireless networks. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 4
Energy-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, Mert
We 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.
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, Alper
Indoor 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.
Mobil nesnelerin interneti için makine öğrenimine dayalı enerji verimli iç mekanda konumlandırma
(2022) Saylam, Alper; Rodoplu, Volkan; Güzeliş, Cüneyt
Yapay 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.
Multi-Resolution Inter-Level Refinement (MR-ILR) Architecture for Anomaly Prediction in IoT Data
(Institute of Electrical and Electronics Engineers Inc., 2022) Rifat Orhan Cikmazel; Alper Saylam; Volkan Rodoplu; Cüneyt Güzeliş; Orhan Cikmazel, Rifat; Saylam, Alper; Rodoplu, Volkan; Guzelis, Cuneyt; R. Chbeir , T. Yildirim , L. Bellatreche , Y. Manolopoulos , A. Papadopoulos , K.B. Chaaya
We develop a novel architecture called "Multi-Resolution Inter-Level Refinement (MR-ILR)"architecture for anomaly prediction in Internet of Things (IoT) data. Our architecture is comprised of three modules: First the Inter-Level OR module takes the logical OR of the vector that represents the past anomaly states in IoT data and represents the occurrence of an anomaly state at increasingly coarse resolutions. Second a Multi-Layer Perceptron (MLP) predicts the occurrence of anomaly states at any given resolution. Third the anomaly predictions at successive resolutions are combined in Refiner modules to produce more accurate anomaly predictions. Our architecture provides the flexibility to produce anomaly predictions at distinct temporal resolutions. We compare the performance of our MR-ILR architecture against MLP and Long Short-Term Memory (LSTM) benchmark models. The results show that our architecture significantly outperforms both of these benchmark models with respect to the F1-score. This work represents an important advance in solving the challenging problem of anomaly prediction in IoT data and has the potential to be applied to a much wider range of problems that target anomaly prediction. © 2022 Elsevier B.V. All rights reserved.