Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 11Citation - Scopus: 17The Impact of Error Control Schemes on Lifetime of Energy Harvesting Wireless Sensor Networks in Industrial Environments(Elsevier, 2020-06) Tekin, Nazli; Gungor, Vehbi CagriDue to the harsh channel conditions of the industrial environments, the data transmission over the wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of great importance for battery-limited Wireless Sensor Networks (WSNs) in industrial environments. In this paper, the lifetime analysis of error control schemes, i.e., Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ), is presented under different industrial environment channel conditions. Furthermore, the impact of energy harvesting methods on the network lifetime is investigated. A novel Mixed Integer Programming (MIP) framework is developed to maximize the network lifetime while meeting application reliability. Performance results show that utilizing HARQ-II error control scheme for Mica2 and BCH(31,21,5) for Telos improves the network lifetime while meeting the desired application reliability rate.Article Citation - WoS: 31Citation - Scopus: 41QoSRP: A Cross-Layer QoS Channel-Aware Routing Protocol for the Internet of Underwater Acoustic Sensor Networks(MDPI, 2019-11-02) Faheem, Muhammad; Butt, Rizwan Aslam; Raza, Basit; Alquhayz, Hani; Ashraf, Muhammad Waqar; Shah, Syed Bilal; Gungor, Vehbi CagriQuality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.Article Citation - WoS: 8Citation - Scopus: 9On the Performance of LTE Downlink Scheduling Algorithms: A Case Study on Edge Throughput(Elsevier, 2018-08) Deniz, Coskun; Uyan, O. Gokhan; Gungor, Vehbi CagriRadio resource allocation is a crucial task in the LTE networks. To increase the overall user experience, an efficient radio resource allocation algorithm should be utilized. In this work, a new scheduling algorithm has been proposed to increase the edge throughput without sacrificing system throughput. Comparative performance results indicate that the proposed scheduler increases the edge throughput and fairness while limiting degradation in the cell throughput between 0 to 2 percent with respect to the other schedulers.Article Citation - WoS: 4Citation - Scopus: 7Network Anomaly Detection Using Deep Autoencoder and Parallel Artificial Bee Colony Algorithm-Trained Neural Network(PeerJ Inc, 2024-10-08) Hacilar, Hilal; Dedeturk, Bilge Kagan; Bakir-Gungor, Burcu; Gungor, Vehbi CagriCyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back- propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSWNB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.Article Citation - WoS: 1Citation - Scopus: 1Intelligent Traffic Light Systems Using Edge Flow Predictions(Elsevier, 2024-01) Thahir, Adam Rizvi; Coskun, Mustafa; Kilic, Sultan Kubra; Gungor, Vehbi CagriIn this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.Article Citation - WoS: 10Citation - Scopus: 8Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission(Wiley-VCH Verlag GmbH, 2024-07-15) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi CagriAcoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes. This article presents a novel ensemble feature selection methodology to rank features relevant to damage modes on acoustic emission signals in carbon fiber-reinforced polymer sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, along with common features, other features, like partial powers, have a robust correlation with damage modes.image (c) 2024 WILEY-VCH GmbHArticle Citation - WoS: 12Citation - Scopus: 13Collecting Smart Meter Data via Public Transportation Buses(Inst Engineering Technology-IET, 2016-10) Bilgin, Bilal Erman; Baktir, Selcuk; Gungor, Vehbi CagriWith advances in technology, wireless sensor networks (WSNs) have found new applications and their popularity has increased dramatically. In several applications, WSNs have the potential to replace wired data communication systems, e.g. in vehicular ad hoc networks (VANETs) they are the natural option for data communication. WSNs are also proposed for data communication in the emerging smart grid. In this study, the authors merge these two application domains, i.e. VANET and smart grids, and propose a novel solution for effective smart grid data communication. The authors' proposed scheme achieves the task of collecting data from smart meters by utilising VANETs. Using network simulator-2 and with different routing protocols, the authors have performed simulations and shown the efficacy of their scheme in terms of average end-to-end delay and delivery ratio.Article Citation - WoS: 28Citation - Scopus: 31Capacity and Spectrum-Aware Communication Framework for Wireless Sensor Network-Based Smart Grid Applications(Elsevier Science Bv, 2017-08) Faheem, Muhammad; Gungor, Vehbi Cagri; Cagri Gungor, VehbiRecently, wireless sensor networks (WSNs) have been widely recognized as a promising technology for enhancing various aspects of smart grid and realizing the vision of next-generation electric power system in a cost-effective and efficient manner. However, recent field tests show that wireless links in smart grid environments have higher packet error rates and variable link capacity because of dynamic topology changes, obstructions, electromagnetic interference, equipment noise, multipath effects, and fading. To overcome these communication challenges, in this paper, we propose a data capacity-aware channel assignment (DCA) and fish bone routing (FBR) algorithm for WSN-based smart grid applications. The proposed DCA framework deals with the channel scarcities by dynamically switching between different spectrum bands and employs a network for organizing WSN into a highly stable connected hierarchy. In addition, the proposed FBR mechanism provides robust loop free data paths and avoids high transmission cost, excessive end-to-end delay and restricts unnecessary multi-hop data transmission from the source to destination in the network. Thus, it significantly reduces the probability of data packet loss and preserves stable link qualities among sensor nodes for load balancing and prolonging the lifetime of wireless sensor networks in harsh smart grid environments. Comparative performance evaluations show that our proposed schemes outperform the existing communication architectures in terms of data packet delivery, communication delay and energy consumption.Article Citation - WoS: 25Citation - Scopus: 41An Efficient Network Intrusion Detection Approach Based on Logistic Regression Model and Parallel Artificial Bee Colony Algorithm(Elsevier, 2024-04) Kolukisa, Burak; Dedeturk, Bilge Kagan; Hacilar, Hilal; Gungor, Vehbi CagriIn recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion and anomaly detection systems to cope with high numbers of attacks and attack variations. In particular, machine learning and meta-heuristic methods have been widely used for network intrusion detection systems (NIDS). However, existing studies on these systems usually suffer from low performance results such as accuracy, F1-measure, false positive rate, and false negative rate, and generally do not use automatic parameter tuning techniques. To address these challenges, this study proposes a novel approach based on a logistic regression model trained using a parallel artificial bee colony (LR-ABC) algorithm with a hyper-parameter optimization technique. The performance of the proposed model is evaluated against state -of-the-art machine learning and deep learning models on two publicly available NIDS datasets. Comparative performance evaluations show that the proposed method achieved satisfactory results with accuracy of 88.25% on the UNSW-NB15 dataset and 90.11% on the NSL-KDD dataset, and F1-measures of 88.26% and 90.15%, respectively. These findings demonstrate the efficacy of the proposed LR-ABC model in enhancing the accuracy and reliability, while providing a scalable solution to adapt to the dynamic and evolving landscape of cybersecurity threats.Article Citation - WoS: 3Citation - Scopus: 3Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems(Wiley, 2025-04-10) Hacilar, Hilal; Dedeturk, Bilge Kagan; Ozmen, Mihrimah; Celik, Mehlika Eraslan; Gungor, Vehbi CagriMetaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.