Browsing by Author "Cakan, Erdem"

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    Article
    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.
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    Conference Object
    Citation - Scopus: 12
    Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning
    (Institute of Electrical and Electronics Engineers Inc., 2021) Erdem Çakan; Aral Sahin; Mert Nakıp; Volkan Rodoplu; Cakan, Erdem; Sahin, Aral; Rodoplu, Volkan; Nakip, Mert
    We develop a Multi-Layer Perceptron (MLP) Decomposition architecture for mobile Internet Things (IoT) indoor positioning. We demonstrate the performance of our architecture on an indoor system that utilizes ultra-wideband (UWB) positioning. Our architecture outperforms the following benchmark processing techniques on the same data: MLP Linear Regression Ridge Regression Support Vector Regression and the Least Squares Method for indoor positioning. The results show that our architecture can significantly advance the positioning accuracy of indoor positioning systems and enable indoor applications such as navigation proximity marketing asset tracking collision avoidance and social distancing. © 2021 Elsevier B.V. All rights reserved.