Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Doctoral Thesis A reliable and secure communication design for underwater sensor networks concerning energy efficiency(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) UYAN, Osman Gökhan; Güngör, Vehbi ÇağrıUnderwater Acoustic Sensor Networks (UASNs) recently attract scientists because of its wide range of applications and emerging technology. A design challenge in UASN's is the limited network lifetime and poor reliability caused by limited battery supply of sensors and harsh channel conditions in underwater environment. Moreover, sensors might transmit sensitive data that must be disguised against eavesdropping attacks. To maintain a reliability level, packet-duplication and multi-path routing method are suggested, which renders eavesdropping attacks easier. For data security, cryptographic encryption is the most acclaimed method. However, encryption needs extra computations, which consume extra energy and cause a decrease in the network lifetime. As a countermeasure along with encryption against silent listening, fragmenting data and transmitting in pieces over different paths has been proposed. To address these challenges, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption, and data fragmentation on network lifetime. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict the energy consumptions of underwater nodes as supplementary methods to optimization models. Performance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.Conference Object Citation - Scopus: 1Traffic Light Management Systems Using Reinforcement Learning(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Can, Sultan Kubra; Thahir, Adam Rizvi; Cos¸kun, Mustafa; Güngör, Vehbi Çağrı; Coskun, MustafaWhile reducing traffic congestion and decrease the number of traffic accidents in the intersections, most of the traffic light management approaches cannot adapt well to fast changing traffic dynamics and growing demands of the intersections with modern world developments. To overcome this problem, adaptive traffic controllers are developed, and detectors and sensors are added to systems to enable adoption and dynamism. Recently, reinforcement learning has shown its capability to learn the dynamics of complex environments, such as urban traffic. Although it was studied in single junction systems, one of the problems was the lack of consistency with how the real world system works. Most of the systems assume that the environment is fully observable or actions would be freely executed using simulators. This study aims to merge usefulness of reinforcement learning methods with real-world traffic constraints. Comparative performance evaluations show that the reinforcement learning algorithm (Advantage Actor-Critic (A2C)) converges well while staying stable under changing traffic dynamics. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 8Short Term Electricity Load Forecasting: A Case Study of Electric Utility Market in Turkey(Institute of Electrical and Electronics Engineers Inc., 2015-04) Ishik, Muhammed Yasin; Göze, Tolga; Ozcan, Ihsan; Güngör, Vehbi Çağrı; Aydin, Zafer; Yasin, MuhammedWith the recent developments in energy sector, the pricing of electricity is now governed by the spot market where a variety of market mechanisms are effective. After the new legislation of market liberalization in Turkey, competition-based on hourly price has received a growing interest in the energy market, which necessitated generators and electric utility companies to add new dimensions to their scope of operation: short-term load and price forecasting. The field has several opportunities though not free from challenges. The dynamic behavior of the market price has caused the electric load to become variable and non-stationary. Furthermore, the number of nodes, in which the load must be predicted, is not constant anymore and can no longer be estimated by experts alone. In this competitive scenario, statistical forecasting methods that can automatically and accurately process thousands of data samples are essential. The purpose of this study is to demonstrate the importance of short-term load forecasting, how it has received a growing interest in Turkey and to propose an artificial neural network that can forecast the short term electricity load. Through detailed performance evaluations, we demonstrate that our forecasting method is capable of predicting the hourly load accurately. © 2017 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 3Citation - Scopus: 4Seamless Mobile Data Offloading in Heterogeneous Wireless Networks Based on IEEE 802.21 and User Experience(Institute of Electrical and Electronics Engineers Inc., 2014-04) Tüzünkan, Firat A.; Güngör, Vehbi Çağrı; Zeydan, Engin; Ileri, Ömer; Ergüt, SalihThe increase on smartphone usage has brought the burden of data traffic with it. Operators are looking for cost-effective solutions to overcome the problem of 3G infrastructure for high contention traffic scenarios. Several schemes were offered to save the moment, and they brought some extra costs including deploying femtocell or WiMax, LTE, LTE-Advanced systems along with their expensive equipment. On the other hand, operators are expanding their networks with 802.11 technologies such that they can exploit the free-band communication. Meaning the data traffic can handover between WLAN and UMTS interchangeably. By using NS-2 simulator, we implemented IEEE 802.21 WG's Media Independent Handover (MIH) module by combining with Channel Quality Indicator (CQI) values collected from user equipment (UE) and observed a recovered throughput for both medium. We found that there is a tradeoff among energy efficiency, delay tolerance and cost. Furthermore, in this study, we integrated a Quality of Experience (QoE) metric during real-time handover decision process so that with this type of collaborative solution, an operator will be unique in terms of user happiness and heterogeneous network management. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Performance Evaluations of Next Generation Networks for Smart Grid Applications(Institute of Electrical and Electronics Engineers Inc., 2015-04) Tuna, Gürkan; Ayana, Esra Kaya; Gülez, Kayhan; Kiokes, George C.; Güngör, Vehbi Çağrı; Kaya, EsraSmart Grid (SG) can be described as the concept of modernizing the traditional electrical grid. Through the addition of SG technologies traditional electrical grids become more flexible, robust and interactive, and are able to provide real time feedback by employing innovative services and products together with communication, control, intelligent monitoring, and self-healing technologies. For being fully functional, utility operators deploy various SG applications to handle the key requirements including delivery optimization, demand optimization and asset optimization needs. The SG applications can be categorized into two main classes: grid-focused applications and customer-focused applications. Although these applications differ in terms of security, Quality of Service (QoS) and reliability, their common requirement is a communication infrastructure. In this paper, we focus on the use of Next Generation Networks (NGNs) for SG applications. We also present a detailed analysis of a NGN-based communication infrastructure for SG applications in terms of global network statistics and node-level statistics. © 2018 Elsevier B.V., All rights reserved.Article Citation - Scopus: 52Performance Comparison of IEEE 802.11p and IEEE 802.11b for Vehicle-to Communications in Highway, Rural, and Urban Areas(2013-11-06) Bilgin, Bilal Erman; Güngör, Vehbi ÇağrıCommunication between vehicles has recently been a popular research topic. Generally, the Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Infrastructure (I2I) communications applications can be divided into two sections: (i) safety applications and (ii) nonsafety applications. In this study, we have investigated the performance of IEEE 802.11p and IEEE 802.11b based on real-world measurements and radio propagation models of V2V networks in different environments, including highway, rural, and urban areas. Furthermore, we have investigated the most used V2V mobility models and simulation tools. Comparative performance evaluations show that the IEEE 802.11p achieves higher network throughput, low end-to-end delay, and higher delivery ratio compared to IEEE 802.11b. Overall, our main objective is to describe potential advantages, research challenges, and applications of V2V networks and show how IEEE 802.11p and IEEE 802.11b will perform under different radio propagation environments. © 2013 B. E. Bilgin and V. C. Gungor. © 2013 Elsevier B.V., All rights reserved.Article Citation - Scopus: 6Network Intrusion Detection Based on Machine Learning Strategies: Performance Comparisons on Imbalanced Wired, Wireless, and Software-Defined Networking (SDN) Network Traffics(Turkiye Klinikleri, 2024-07-26) Hacilar, Hilal; Aydin, Zafer; Güngör, Vehbi ÇağrıThe rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, and SMOTETomek are used to handle imbalanced datasets. Additionally, eXtreme Gradient Boosting (XGBoost) identifies key features, and an autoencoder (AE) assists in feature extraction for the classification task. The study evaluates datasets such as AWID, UNSW, and InSDN, yielding the best results with different numbers of selected features. Bayesian optimization fine-tunes parameters, and diverse machine learning algorithms (SVM, kNN, XGBoost, random forest, ensemble classifiers, and autoencoders) are employed. The optimal results, considering F1-measure, overall accuracy, detection rate, and false alarm rate, have been achieved for the UNSW-NB15, preprocessed AWID, and InSDN datasets, with values of [0.9356, 0.9289, 0.9328, 0.07597], [0.997, 0.9995, 0.9999, 0.0171], and [0.9998, 0.9996, 0.9998, 0.0012], respectively. These findings demonstrate that combining Bayesian optimization with oversampling techniques significantly enhances classification performance across wired, wireless, and SDN networks when compared to previous research conducted on these datasets. © 2024 Elsevier B.V., All rights reserved.Conference Object Linear Vs. Non-Linear Embedding Methods in Recommendation Systems(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Gurler, Kerem; Cos¸kun, Mustafa; Karagenc, Safak; Orun, Gokhan; Kuleli Pak, Burcu Kuleli; Güngör, Vehbi Çağrı; Coskun, Mustafa; Pak, Burcu KuleliPredicting customer interest in items is very crucial in direct marketing as it can potentially boost sales. Data mining techniques are developed to predict which items a particular user might be interested in based on their purchase history or explicit feedback in form of ratings or comments. Recently, non-linear and linear methods have been developed for this purpose. In this study, we applied Neighborhood based Collaborative Filtering (CF), Matrix Factorization (MF), Singular Value Decomposition (SVD), Neural Graph CF (NGCF) and Light Graph Convolutional Network (LightGCN) on explicit user product rating data which is acquired from the online gaming and mobile entertainment platform called HADI. We compared the results of node embedding methods in terms of Precision@k, Recall@k and NDCG@k values. SVD and LightGCN showed the best test performance and SVD was significantly superior to LightGCN in terms of training speed. To further increase predictive performance of SVD, we have applied classification with Logistic Regression and Deep Random Forest on user and item embeddings created by the SVD. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 7Generating Emergency Evacuation Route Directions Based on Crowd Simulations With Reinforcement Learning(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Unal, Ahmet Emin; Gezer, Cengiz; Kuleli Pak, Burcu Kuleli; Güngör, Vehbi Çağrı; Pak, Burcu KuleliIn an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation plan-ning ensures that the evacuation is safe and optimal in terms of evacuation time for all of the people in evacuation. To this end, the computer-enabled evacuation simulation systems are used to generate optimal routes for the evacuees. In this paper, a dynamic emergency evacuation route generator has been proposed based on indoor plans of the building and the locations of the evacuees. To generate the optimal routes in real-time, a reinforcement learning algorithm (proximal policy optimization) is presented. Comparative performance results show that the proposed model is successful for evacuating the individuals from the building in different scenarios. © 2022 Elsevier B.V., All rights reserved.Article Citation - Scopus: 63Energy Efficient and Reliable Data Gathering Using Internet of Software-Defined Mobile Sinks for WSNS-Based Smart Grid Applications(Elsevier B.V., 2019-10) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıThe smart grid is an emerging concept that introduces innovative ways to handle the power quality and reliability issues for both service provider and consumers. The key aims of the smart grid (SG) in smart cities (SCs) is to preserve a certain level of residents’ life quality and support the entire spectrum of their economic activities. In this paper, we present a novel Energy Efficient and Reliable Data Gathering Routing Protocol (ODGRP) for wireless sensor networks (WSNs)-based smart grid applications. The developed scheme employs a software-defined centralized controller and multiple mobile sinks for energy efficient and reliable data gathering from WSNs in the SG. The extensive simulation results conducted through the EstiNet 9.0 show that the designed scheme outperforms existing approaches and achieves its defined goals for event-driven applications in the SG. © 2019 Elsevier B.V., All rights reserved.