WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Lifetime Analysis of Underwater Wireless Networks Concerning Privacy With Energy Harvesting and Compressive Sensing(IEEE, 2019-04) Uyan, O. Gokhan; Gungor, V. CagriUnderwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime.Conference Object Citation - WoS: 1Analysis of Battery-Powered Sensor Node Lifetime for Smart Grid Applications(IEEE, 2016) Eris, Cigdem; Gungor, V. Cagri; Boluk, Pinar SarisarayWireless Sensor Networks (WSNs) enable smart grids where sensor nodes monitor and control the important parameters of power grid components. However, energy-aware communication protocols should be developed to extend network lifetime of WSNs in smart grid environments. In this study, the lifetime of wireless sensor nodes has been analyzed for various smart grid environments, such as 500 kV substation, main power control room, and underground network transformer vaults. In addition, the effects of different operation modes of sensor nodes on node lifetime have been reviewed.Conference Object Citation - WoS: 1Citation - Scopus: 3Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri(IEEE, 2019-04) Tekin, Nazli; Gungor, V. CagriDue to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.Conference Object Citation - WoS: 5Enerji Hasadı Yapan Sualtı Kablosuz Duyarga Düğümlerinin Yaşam Ömrü Analizi(IEEE, 2017) Erdem, H. Emre; Gungor, V. CagriThe application of Wireless Sensor Networks (WSNs) in underwater environments poses various challenges. One of the most important problems is the limited lifetime of underwater sensor nodes. Considering how challenging and costly it is to change the batteries of sensor nodes in underwater environments, energy harvesting methods arc rendered as a promising solution. In this study, the contributions of energy harvesting via turbine and hydrophone harvesters as well as schedule and trigger driven energy management methods on node lifetime have been analyzed. Performance evaluations have been conducted considering real-life conditions, e.g. flow rates, of Istanbul Bosphorus Strait.Conference Object Citation - WoS: 1Citation - Scopus: 5Credit Risk Analysis Based on Hybrid Classification: Case Studies on German and Turkish Credit Datasets(IEEE, 2018-05) Cetiner, Erkan; Kocak, Taskin; Gungor, V. CagriIn finance sector, credit risk analysis plays a major role in decision process. Banks and finance institutions gather large amounts of raw data from their customers. Data mining techniques can be employed to obtain useful information from this raw data. Several data mining techniques, such as support-vector machines (SVM), neural networks, naive-bayes, have already been used to classify customers. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. Furthermore, we compare these approaches' performance with respect to their classification accuracy. We work with two diverse datasets; namely, German credit dataset and Turkish bank dataset. The goal of using such diverse dataset is to show generalization capabality of our approaches. Experimental results provide three important consequences. First, feature selection stage has a major role both on result accuracy and calculation complexity. Second, hybrid approaches have better generalability over single classifiers. Third, using SVM-Radial Basis Function (RBF) as the base classifier and a hybrid model member gives the best accuracy and type-1 accuracy results among others.