Browsing by Author "Kelesoglu, Nur"

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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 - WoS: 6
Citation - Scopus: 6
Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization
(Elsevier Ltd, 2024) Mert Nakıp; Nur Kelesoglu; Cüneyt Güzeliş; Guzelis, Cueneyt; Kelesoglu, Nur; Nakip, Mert
We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination facilitating the integration of l1 regularization into the SVR method thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires benchmarking against 15 machine learning models including state-of-the-art techniques such as Recurrent Trend Predictive Neural Network (rTPNN) Long-Short Term Memory (LSTM) Multi-Layer Perceptron (MLP) Gated Recurrent Unit (GRU) and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire enabling early preventive measures. Notably it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR facilitated by flattening-samples based augmented regularization allows for high detection performance even with smaller training sets. © 2024 Elsevier B.V. All rights reserved.