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.Article Citation - WoS: 6Citation - Scopus: 6Sleep-Aware Wavelength and Bandwidth Assignment Scheme for TWDM PON(Springer, 2021-06) Butt, Rizwan Aslam; Faheem, Muhammad; Ashraf, M. Waqar; Arfeen, Asad; Memon, Kamran Ali; Khawaja, AttaullahThe energy efficiency and delay performance of PON are two inversely related phenomena. Higher sleep time of the Optical Network Units (ONUs) results in higher upstream (US) delays due to increased traffic queues during the ONU Asleep state. Although an efficient dynamic bandwidth and wavelength assignment (DWBA) scheme can decrease US delays by minimizing the bandwidth waste and improving the fairness of bandwidth distribution among the ONUs. However, the conventional DWBA schemes are not designed to work with cyclic sleep mode (CSM) and they keep on assigning bandwidth to ONUs even if the ONU is in Asleep state leading to wastage of bandwidth and degraded CSM performance. Therefore, in this work a sleep aware DWBA scheme for TWDM PON is presented to coordinate with CSM mode. It only assign bandwidth to Active ONUs during the guaranteed phase, surplus phase and excess phase allocation phases which minimizes the bandwidth waste and the bandwidth lost at the ONU end. The wavelength switching process is also improved by only considering the Active state ONUs to balance the traffic load on all the wavelengths. The simulation results support our claim as the SA-DWBA scheme on average achieves DWBA schemes due to up to 50% to 65% higher energy savings compared to other due to longer ONU Asleep times. However, the increased upstream delays of all the traffic classes in SA-DWBA scheme remain within the set delay limit of 50 ms.Conference Object Citation - WoS: 7Citation - Scopus: 10PI-Controlled ANN-Based Energy Consumption Forecasting for Smart Grids(SciTePress, 2015) Gezer, Gülsüm; Tuna, Gürkan; Κogias, DImitrios G.; Gülez, Kayhan; Güngör, Vehbi Çağrı; Kogias, DimitrisAlthough Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications. © 2022 Elsevier B.V., All rights reserved.Article Citation - Scopus: 54EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments(Elsevier Ltd, 2024-02) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal SinghTo meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.Article Citation - WoS: 12Citation - Scopus: 13All-Surface Induction Heating With High Efficiency and Space Invariance Enabled by Arraying Squircle Coils in Square Lattice(IEEE-Inst Electrical Electronics Engineers Inc, 2018-08) Kilic, Veli Tayfun; Unal, Emre; Yilmaz, Namik; Demir, Hilmi VolkanThis paper reports an all-surface induction heating system that enables efficient heating at a constant speed all over the surface independent of the specific location on the surface. In the proposed induction system, squircle coils are placed tangentially in a two-dimensional square lattice as opposed to commonly used hexagonal packing. As a proof-of-concept demonstration, a simple model setup was constructed using a 3 x 3 coil array along with a steel plate to be inductively heated. To model surface heating, a set of six locations for the plate was designated considering symmetry points. For all of these cases, power dissipated by the system and the plate's transient heating were recorded. Independent from the specific plate position, almost equal heating speeds were measured for the similar levels of dissipated energies in the system. Using full three-dimensional electromagnetic solutions, the experimental results were also verified. The findings indicate that the proposed system is proved to enable energy efficient space-invariant heating in all-surface induction hobs.Conference Object Citation - Scopus: 29A Hybrid Energy Harvesting Framework for Energy Efficiency in Wireless Sensor Networks Based Smart Grid Applications(Institute of Electrical and Electronics Engineers Inc., 2018-06) Yildiz, Huseyin Ugur; Güngör, Vehbi Çağrı; Tavli, BülentIn smart grid applications, Wireless Sensor Net-works (WSNs) which consist of battery limited sensor nodes are used on critical equipments of power distribution grids for monitoring purposes. WSN nodes have tight energy constraints hence it is important to reduce the energy consumption of sensor nodes due to harsh propagation characteristics of smart grid environment. One possible way to reduce the energy consumption is to utilize transmission power control where transmission powers are adjusted according to channel conditions. Another technique is to employ energy harvesting schemes to provide additional power for nodes by using environmental energy sources. Solar and electromagnetic energies are two possible environmental energy sources in outdoor substation environments. Solar energy can be efficiently exploited in a sunny day. On the other hand, electromagnetic energy can be used at any time. In this work, we propose a hybrid energy harvesting model that exploits both solar and electromagnetic energies and develop a Mixed Integer Programming (MIP) method to minimize the energy dissipation of sensor nodes. By using the MIP framework, we quantify the impact of the proposed hybrid energy harvesting model as well as transmission power control on the energy saving of nodes. © 2018 Elsevier B.V., All rights reserved.