Browsing by Author "Rodoplu, Volkan"

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A Machine Learning Based Energy-Efficient Indoor Multiple IoT Device Tracking Algorithm Based on Correlated Group Determination
(IEEE, 2023) Alp Erel; Emre Molla; Volkan Rodoplu; Rodoplu, Volkan; Erel, Alp; Molla, Emre
We develop a novel algorithm for energy-efficient indoor multiple IoT device tracking based on Artificial Intelligence (AI). Our algorithm is comprised of two phases: First we jointly forecast the future positions of the mobile IoT devices. Second we develop a novel algorithm that determines groups of IoT devices whose forecast trajectories are correlated with each other over a future time window. Our simulations demonstrate that our algorithm results in significant energy savings by keeping only the leader of the correlated group active while putting the followers to sleep during the entire duration for which the correlated group persists. This results in low intra-communication energy costs for the correlated group. This work represents a significant advance over single-device tracking algorithms by exploiting the correlations between the trajectories of multiple IoT devices.
Citation - WoS: 16
Citation - Scopus: 26
A Multiscale Algorithm for Joint Forecasting-Scheduling to Solve the Massive Access Problem of IoT
(Institute of Electrical and Electronics Engineers Inc., 2020) Volkan Rodoplu; Mert Nakıp; D. T. Eliiyi; Cüneyt Güzeliş; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
The massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB) priority based on average load (PAL) and enhanced predictive version burst-oriented (E-PRV-BO) protocols and show that MSA significantly outperforms these beyond 3000 devices. Furthermore we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 15
An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Mert Nakip; Kubilay Karakayali; Cuneyt Guzelis; Volkan Rodoplu; Karakayali, Kubilay; Guzelis, Cuneyt; Rodoplu, Volkan; Nakip, Mert
We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based wrapper-based and embedded feature selection methods our architecture enables the automatic selection of features dynamically based on feature importance score calculation and gamma-gated feature selection units that are trained jointly and end-to-end with the forecaster. We compare the performance of our FSF architecture on the problem of forecasting IoT device traffic against the following existing (feature selection forecasting) technique pairs: Autocorrelation Function (ACF) Analysis of Variance (ANOVA) Recurrent Feature Elimination (RFE) and Ridge Regression methods for feature selection and Linear Regression Multi-Layer Perceptron (MLP) Long Short Term Memory (LSTM) 1 Dimensional Convolutional Neural Network (1D CNN) Autoregressive Integrated Moving Average (ARIMA) and Logistic Regression for forecasting. We show that our FSF architecture achieves either the best or close to the best performance among all of the competing techniques by virtue of its dynamic automatic feature selection capability. In addition we demonstrate that both the training time and the execution time of FSF are reasonable for IoT applications. This work represents a milestone for the development of predictive networks for IoT in smart cities of the near future.
Citation - Scopus: 6
ARIMA-Based Traffic Forecasting for Quality of Service (QoS) Flow Routing in Sixth Generation (6G) Networks
(Institute of Electrical and Electronics Engineers Inc., 2022) Ayca Akcapinar; Oyku Gurer; Volkan Rodoplu; Rodoplu, Volkan; Akcapinar, Ayca; Gurer, Oyku
We develop a methodology for Quality of Service (QoS) flow routing in IoT networks targeted at improving the performance of QoS flow optimization. In contrast with the existing schemes the AutoRegressive Integrated Moving Average (ARIMA) model forecasts the Enhanced Mobil Broadband (eMBB) flow traffic periodically and a QoS optimization program makes reservations on the links for the predicted eMBB flows in advance in order to maximize the total end-to-end data rate from sources to destinations. To this end we design an algorithm that delays the transmission of QoS flows that have laxer latency constraints. We compare the performance of our algorithm against a benchmark QoS routing scheme that transmits the flows in an on-demand fashion. Our results show that our algorithm outperforms the reactive approach. This work holds the potential to impact the design of QoS routing in beyond Fifth Generation (5G) networks. © 2022 Elsevier B.V. All rights reserved.
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 - WoS: 15
Citation - Scopus: 21
Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication
(ASSOC COMPUTING MACHINERY, 2019) Mert Nakip; Baran Can Gul; Volkan Rodoplu; Cuneyt Guzelis; Gul, Baran Can; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert
We present a comparative study of Autoregressive Integrated Moving Average (ARIMA) Multi-Layer Perceptron (MLP) 1-Dimensional Convolutional Neural Network (1-D CNN) and Long-Short Term Memory (LSTM) models on the problem of forecasting the traffic generation patterns of individual Internet of Things (IoT) devices in Machine-to-Machine (M2M) communication. We classify IoT traffic into four classes: Fixed-Bit Periodic (FBP) Variable-Bit Periodic (VBP) Fixed-Bit Aperiodic (FBA) and Variable-Bit Aperiodic (VBA). We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error (sMAPE) measure for devices in the VBP class in our simulations. Furthermore we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN. While the training time per IoT device is the highest for LSTM all of the forecasting models have reasonable training times for practical implementation. Our results suggest an architecture in which an IoT Gateway predicts the future traffic of IoT devices in the FBP VBP and FBA classes and pre-allocates the uplink wireless channel for these classes in advance in order to alleviate the Massive Access Problem of M2M communication.
Citation - Scopus: 2
Data 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, Erdem
The 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.
Citation - WoS: 1
Citation - Scopus: 3
Design 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 Ahmet
In 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.
Citation - WoS: 4
Citation - Scopus: 7
Dynamic 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, Mert
The 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.
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.
Citation - Scopus: 16
Joint Forecasting-Scheduling for the Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2019) Mert Nakıp; Volkan Rodoplu; Cüneyt Güzeliş; D. T. Eliiyi; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
We present a joint forecasting-scheduling (JFS) system to be implemented at an IoT Gateway in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals. In contrast our JFS system forecasts the traffic generation pattern of each IoT device and schedules the transmissions of these devices in advance. The comparison of the network throughput of Autoregressive Integrated Moving Average (ARIMA) Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) forecasting models reveals that the optimal choice of the forecasting model for JFS depends heavily on the proportions of distinct IoT device classes that are present in the network. Simulations show that our JFS system scales up to 1000 devices while achieving a total execution time under 1 second. This work opens the way to the design of scalable joint forecasting-scheduling solutions at IoT Gateways. © 2020 Elsevier B.V. All rights reserved.
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.
Citation - WoS: 1
Citation - Scopus: 2
Machine Learning Based Multipath Processing Architecture for Mobile IoT Indoor Positioning
(Institute of Electrical and Electronics Engineers Inc., 2023) Berke Kilinc; Berkay Habib; Volkan Rodoplu; Habib, Berkay; Rodoplu, Volkan; Kilinc, Berke
In this work we propose a novel machine learning-based architecture that processes the Channel Impulse Response (CIR) for mobile Internet of Things (IoT) indoor localization. Our architecture is comprised of three stages: First it pre-processes the Channel Impulse Response of the channel from the mobile device to each anchor by lumping the channel tap values at a configurable resolution. Second the Machine Learning-Based Multipath Profile Processing block applies feature selection to the pre-processed channel taps. Third in the Machine Learning Based Feature Fusion block the selected features are combined to estimate the position of the mobile device. In order to test the performance of our architecture we use two distinct datasets that were collected in home and office environments respectively. The results demonstrate that our work can significantly improve in-door localization accuracy. This work paves the way to significant performance improvements in indoor localization by processing the Channel Impulse Response via machine learning algorithms. © 2024 Elsevier B.V. All rights reserved.
Citation - Scopus: 4
Machine Learning Based Seamless Vertical Handoff Mechanism for Hybrid Li-Fi/Wi-Fi Networks
(Institute of Electrical and Electronics Engineers Inc., 2022) Ata Saygin Odabasi; Onur Isci; Volkan Rodoplu; Odabasi, Ata Saygin; Rodoplu, Volkan; Isci, Onur; R. Chbeir , T. Yildirim , L. Bellatreche , Y. Manolopoulos , A. Papadopoulos , K.B. Chaaya
Blockage of Visible Light Communication (VLC) links by mobile obstacles such as humans is one of the key problems to solve in order to achieve the wide-scale deployment of indoor VLC networks. In this work we present a solution to this problem by developing a predictive vertical handoff algorithm between Light Fidelity (Li-Fi) and Wireless Fidelity (Wi-Fi) networks. By using a state-of-the-art machine learning based forecasting model our handoff algorithm predicts the number of time intervals for which blockage will occur in the next time block. Based on this prediction our algorithm proactively hands off from Li-Fi to Wi-Fi and from Wi-Fi to Li-Fi in a manner that trades off the Average Available Data Rate (AADR) and the percentage service interruption. We demonstrate the performance of our algorithm on data collected in a life simulation environment in which humans move about in an indoor setting and block Li-Fi links. We show that our algorithm maintains a high AADR while achieving a very low percentage service interruption. Furthermore we show that by varying the values of the parameters of our algorithm we can achieve a gradual trade-off between AADR and the percentage service interruption. Our algorithm paves the way to high-performance hybrid Li-Fi/Wi-Fi networks that bear the potential to significantly change the landscape of indoor communication in the near future. © 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.
MOSAL: A Subspace-Based Forecasting Algorithm for Throughput Maximization in IoT Networks
(Institute of Electrical and Electronics Engineers Inc., 2022) Mert Nakıp; Alperen Helva; Cüneyt Güzeliş; Volkan Rodoplu; Guzelis, Cneyt; Rodoplu, Volkan; Helva, Alperen; Nakip, Mert
Predictive solution techniques have been developed recently to solve the massive access problem of the Internet of Things (IoT). These techniques forecast the traffic generation patterns of individual IoT devices in the coverage area of an IoT gateway and schedule the Medium Access Control (MAC)-layer resources at the gateway in advance based on these forecasts. Although predictive solutions have achieved high network performance a key problem is that their performance depends highly on the performance of forecasters. In this article to minimize the effects of forecasting errors on the performance of predictive networks we develop a subspace-based forecasting algorithm called 'Motion On a Subspace under Adaptive Learning rate (MOSAL).' First our algorithm trains a forecaster by minimizing the performance loss of an IoT network based on the emulation of an Application-Specific Error Function (ASEF) by an Artificial Neural Network (ANN). Second the algorithm moves close to a subspace of the forecasting errors while aiming to maximize network throughput. Our results show that MOSAL achieves a throughput performance that surpasses the performance of commonly used standard gradient descent training algorithms at a reasonable execution time. These results open the way to the deployment of predictive solutions at IoT gateways in practice in the near future. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 18
Multi-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 Tursel
We 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.
Citation - Scopus: 10
Multi-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, Mert
The 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.