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CİCİOĞLU, MURTAZA

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CİCİOĞLU

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MURTAZA

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Now showing 1 - 10 of 14
  • Publication
    Multi-criteria handover management using entropy-based saw method for sdn-based 5g small cells
    (Springer, 2021-04-28) Cicioglu, Murtaza; CİCİOĞLU, MURTAZA; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    The high data traffic requirements of the new generation 5G networks will be satisfied with effective and efficient mobility and handover management. However, dense or ultra-dense small cell (eNB) placements in 5G networks may lead to some problems, such as latency, handover failures, frequent handover, ping-pong effect, etc. In this study, we proposed an Entropy-based simple additive weighting decision-making method for multi-criteria handover in software-defined networking (SDN) based 5G small cells for the solution of the aforementioned problems. This method provides the connection of the mobile node to the most suitable eNB using bandwidth, user density and SINR parameters. The proposed handover method is compared with conventional LTE handover and distributed approach in terms of delay, block ratio, handover failure and throughput according to the varying number of mobile users. The scalability of handovers for both approaches according to the user number are also analysed. According to the simulation results, the proposed approach achieved 15%, 48% and 22% improvement in handover delay, blocking probability and throughput, respectively, compared to the conventional LTE handover.
  • Publication
    Iot-based gps assisted surveillance system with inter-wban geographic routing for pandemic situations
    (Elsevier, 2021-03-18) Sen, Seda Savascı; Cicioğlu, Murtaza; Çalhan, Ali; CİCİOĞLU, MURTAZA; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    Background: Worldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Methods: We propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with ?maskwearing status? of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana).Results: The geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture.Conclusion: The results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.
  • Publication
    A new platform for machine-learning-based network traffic classification
    (Elsevier, 2023-06-06) Bozkır, Ramazan; Çalhan, Ali; Togay, Cengiz; CİCİOĞLU, MURTAZA; Mühendislik Fakültesi; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    This study provides a new platform for classifying encrypted network traffic based on machine learning (ML) techniques. The architecture of the platform is designed for real-world network traffic classification problems with performance-oriented, practical, and up-to-date software technologies. In addition, this study introduces a new feature extraction method to the literature. The proposed platform applies ML techniques with flowbased statistical features of encrypted network traffic and new feature extraction. It takes network traffic packets as input and passes them through feature extraction, data preparation, and ML stages. In the feature extraction stage, network flows are extracted from the network traffic data by calculating their features with the NFStream tool. During the data preparation stage, the dataset is transformed into a processable state for the ML algorithm with the Apache Spark framework. This stage also includes the feature selection operation. The ML stage runs GBTree, LightGBM, and XGBoost algorithms. Moreover, we use the MLflow framework in the proposed process management to observe the ML lifecycle, including experimentation, reproducibility, and deployment. The experimental results show that the XGBoost algorithm achieves the best result with an F1 score of above 99%.
  • Publication
    An effective routing algorithm for spectrum allocations in cognitive radio based internet of things
    (Wiley, 2022-10-09) Cicioğlu, Murtaza; Çalhan, Ali; Miah, Md Sipon; CİCİOĞLU, MURTAZA; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    The Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad-hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR-based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc on-demand distance vector routing protocol is proposed for CR-based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.
  • Publication
    Ehealth monitoring testb e d with fuzzy based early warning score system
    (Elsevier, 2021-02-25) Calhan, Ali; Cicioğlu, Murtaza; Ceylan, Arif; CİCİOĞLU, MURTAZA; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    Background and objective: EHealth monitoring systems are able to save the persons' lives and track some vital physiological signs of patients, sportsmen, and soldiers for some purposes. Instant data tracking enables appropriate clinical interventions. The early warning score concept defines that specific vital human body signs that are considered together and gives the persons' health score. The patient's vital signs are periodically recorded with the Early Warning Score (EWS) system and the illness severity score of the patient is decided manually. The aim of the study is to monitor a person's health data continuously and calculate the EWS score thanks to the fuzzy logic. Therefore, the simulation as a testbed is constructed for real-time applications with ISO/IEEE 11073 Health informatics -Medical/health device communication standard.Methods: In our paper, a fuzzy-based early warning score system in the EHealth monitoring testbed is proposed. Real-time data are obtained from Riverbed Modeler simulation software with socket programming and stored in the InfluxDB using Node-Red and monitored on the remote desktop with Grafana.Results: Heart rate, body temperature, systolic blood pressure, respiratory rate, and SPO2 are taken into consideration in the fuzzy-based evaluation system for EWS. The data produced in the Riverbed has been provided in a realistic manner because the real human vital sign values are considered during generating vital signs.Conclusions: Using real-time Riverbed Modeler health data with fuzzy-based EWS, a more realistic testbed platform is constructed in this study.
  • Publication
    Handover management in software-defined 5G small cell networks via long short-term memory
    (Wiley, 2022-01-19) Cicioğlu, Murtaza; Calhan, Ali; CİCİOĞLU, MURTAZA; Mühendislik Fakültesi; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    5G and beyond communication technologies have started to spread around the world. Higher frequencies lead 5G base stations to have small coverage areas. Besides, the wireless network users have mobility and may move fast among the base stations. Software-defined networking (SDN) is a promising network solution for dynamic and dense networks such as 5G networks. The handover process defines the transfer of mobile users' connections among the base stations and the handover has to happen frequently in ultra-dense networks. In this study, we aim to construct a more robust handover based on long short-term memory (LSTM) with SDN in terms of the number of handover and handover failures. LSTM, linear regression, support vector machine, and tree algorithms performances have been investigated for handover. According to the R-2 values of LSTM, SVM, tree, linear regression results are obtained as 0.998, 0.980, 0.980, and 0.75, respectively. Root mean square error, coefficient of determination (R), mean squared error, and mean absolute deviation statistics prove the improvement of the handover mechanism. In the proposed approach, approximately 30% reduction in the HO failure ratio and 22.22% reduction number of handover have been observed.
  • Publication
    Drone-assisted smart data gathering for pandemic situations
    (Pergamon-Elsevier Science Ltd, 2022-01-25) Çalhan, Ali; Cicioğlu, Murtaza; CİCİOĞLU, MURTAZA; Mühendislik Fakültesi; Bilgisayar Mühendisliği Bölümü; AAL-5004-2020
    In this study, a new approach is proposed based on drone-assisted smart data gathering for pandemic situations. Drones can play important roles in highly dynamic and dense disaster areas for the data gathering process. Under these conditions, if big data gathering is necessary, the network traffic can be lightened and balanced with smart techniques. For these reasons, the drones construct the aerial network and scan the frequency bands in their coverage area. Then the collected data on the related drone is processed in terms of importance and priority levels. The drones take on fog computing capabilities for the specific duties. So, the unnecessary data will not be transmitted to the related destinations and the most priority data will be transferred immediately to the related units. The proposed mechanism is developed and examined with various scenarios. The throughput, delay and energy consumption performance metrics are considered for performance evaluation.
  • Publication
    Fuzzy logic based handover management in small cell networks
    (IEEE, 2021) Cicioğlu, Murtaza; CİCİOĞLU, MURTAZA; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    In order to meet high data traffic demands in fifth generation (5G) networks, many small cells (eNBs) are needed. This approach is used in next-generation wireless communication to increase network capacity and coverage in dense networks. However, this approach can cause handover delays and unnecessary handover (ping-pong effect). In order to overcome these problems, fuzzy logic-based handover management has been proposed in small cell networks. In fuzzy logic-based handover management, mobile nodes select the most suitable small cells with RSSI, SNR, and Jitter parameters. Thanks to this approach, mobile nodes determine the most suitable small cells with fuzzy logic decision mechanism in next generation 5G small cell networks. Fuzzy logic-based handover management and traditional handover algorithm have been compared using delay and handover numbers. According to the results, it has been observed that the proposed approach gives more successful results than the traditional handover algorithm.
  • Publication
    Cnn-based automatic modulation recognition for index modulation systems
    (Pergamon-elsevier Science Ltd, 2023-11-21) Leblebici, Merih; Çalhan, Ali; Cicioğlu, Murtaza; CİCİOĞLU, MURTAZA; Mühendislik Fakültesi; Bilgisayar Mühendisliği Bölümü; 0000-0002-5657-7402; AAL-5004-2020
    Automatic modulation recognition (AMR) has garnered significant attention in both civilian and military domains, with applications ranging from spectrum sensing and cognitive radio (CR) to the deterrence of adversary communication. Index modulation (IM) represents an innovative digital modulation technique that exploits the indices of parameters of communication systems to transmit extra information bits. This paper aims to examine the performance of a convolutional neural network (CNN)-based AMR across various IM systems, including spatial modulation (SM), quadrature spatial modulation (QSM), and generalized spatial modulation (GSM) with eight digital modulation schemes. In this study, we leverage confusion matrices, receiver operating characteristic (ROC) curves, and F1 scores to illustrate the recognition model's outputs.
  • Publication
    Real-time internet of medical things framework for early detection of covid-19
    (Springer London Ltd, 2022-07-24) Yıldırım, Emre; Çalhan, Ali; Cicioğlu, Murtaza; CİCİOĞLU, MURTAZA; AAL-5004-2020
    The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.