Browsing by Author "Cikmazel, Rifat Orhan"

Now showing 1 - 3 of 3

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.