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: 16Citation - Scopus: 23Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Al-Abri, Tariq; Onen, Ahmet; Al-Abri, Rashid; Hossen, Abdulnasir; Al-Hinai, Amer; Jung, Jaesung; Ustun, Taha SelimWith the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.Book Part Citation - Scopus: 12Realizing the Wireless Technology in Internet of Things (IoT)(Springer Singapore, 2018) Κogias, DImitrios G.; Michailidis, Emmanouel T.; Tuna, Gürkan; Güngör, Vehbi Çağrı; Kogias, Dimitrios G.The evolution of the Internet of Things (IoT) has been highly based on the advances on wireless communications and sensing capabilities of smart devices, along with a, still increasing, number of applications that are being developed which manage to cover various small and more important aspects of every people's life. This chapter aims at presenting the wireless technologies and protocols that are used for the IoT communications, along with the main architectures and middleware that have been proposed to serve and enhance the IoT capabilities and increase its efficiency. Finally, since the generated data that are spread in an IoT ecosystem might include sensitive information (e.g., personal medical data by sensors), we will also discuss the security and privacy hazards that are introduced from the advances in the development and application of an IoT environment. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 8Citation - Scopus: 9Priceless: Privacy Enhanced AI-Driven Scalable Framework for IoT Applications in Serverless Edge Computing Environments(John Wiley & Sons Ltd, 2024-02-14) Golec, Muhammed; Golec, Mustafa; Xu, Minxian; Wu, Huaming; Gill, Sukhpal Singh; Uhlig, SteveServerless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time-sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain-based AI-driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk-based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state-of-the-art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.Article Citation - WoS: 17Citation - Scopus: 21Machine Learning Approaches for Underwater Sensor Network Parameter Prediction(Elsevier, 2023-05) Uyan, Osman Gokhan; Akbas, Ayhan; Gungor, Vehbi CagriUnderwater Acoustic Sensor Networks (UASNs) have recently attracted scientists due to its wide range of real -world applications. However, there are design challenges in UASNs, such as limited network lifetime and low communication reliability provoked by the constrained battery supply of sensors and harsh channel conditions in the underwater environments. To meet communication reliability requirements, packet-duplication and multi -path routing algorithms have been recommended in the literature. Furthermore, underwater sensors may convey sensitive data, which must be masked to avoid eavesdropping attempts. To improve network security, cryptographic encryption is the most widely used method. Nevertheless, data encryption needs computations to cipher the data, which consumes extra energy, resulting in a cutback in the life span of the network. To address these challenges, an optimization model has been proposed to evaluate the impacts of multi-path routing, packet duplication, encryption, and data fragmentation on the lifetime of the UASNs. 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 network param-eters and energy consumptions of underwater nodes as supplementary methods to optimization models. Per-formance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.Article Citation - WoS: 21Citation - Scopus: 27Blockchain-Based Energy Applications: The DSO Perspective(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Yagmur, Ahmet; Dedeturk, Beyhan Adanur; Soran, Ahmet; Jung, Jaesung; Onen, AhmetThis paper discusses blockchain-based energy applications from the distribution system operator (DSO) perspective. Blockchain has a potential impact on newly emergent actors, such as electric vehicles (EVs) and the charging facility units (CFUs) of the electricity grid. Although Blockchain offers magnificent decentralized solutions, the central management of DSOs still plays a significant, non-negligible role, owing to the reality of the existing grid structure. Numerous related studies of proposed blockchain-based EV systems have investigated the energy costs of EVs, fast and efficient charging, privacy and security, P2P energy trading, sharing economy, the selection of appropriate CFUs location, and scheduling. However, cooperation with DSO organizations has not been adequately addressed. Blockchain-based solutions mainly suggest an entirely distributed and decentralized approach for energy trading; however, converting the entire power system infrastructure is considerably expensive. Building a thoroughly decentralized electricity network in a short time is nearly impossible, particularly at the national grid level. In this regard, the applicability of the solutions is as significant as their appropriateness, especially from the DSO perspective, and must be examined closely. We searched and analyzed the blockchain literature related to EVs, CFUs, DERs, microgrids, marketing, and DSOs to define the DSO-based requirements for potential blockchain applications in the energy sector, specifically EV evolution.Article Citation - WoS: 11Citation - Scopus: 12A Reliable and Secure Multi-Path Routing Strategy for Underwater Acoustic Sensor Networks(Elsevier, 2022-07) Uyan, Osman Gokhan; Akbas, Ayhan; Gungor, Vehbi CagriUnderwater Acoustic Sensor Networks (UASNs) have nowadays become an attractive topic in scientific studies and commercial applications. An important challenge in UASN's design is the limited network lifetime and low reliability caused by the limited battery energy of sensor nodes and harsh channel conditions in underwater environments. In addition, sensor nodes may generate sensitive data, which needs to be concealed. To this end, cryptographic encryption is a commonly used method to cipher a data before transmission to maintain security. However, encryption methods require additional computation and extra energy, which causes a decrease in the network lifetime. To this end, transmitting fragmented data through multiple paths can be used as a security countermeasure, in conjunction with encryption against silent listening attacks. To address these challenges, in this study, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption and data fragmentation on network lifetime. In addition to an optimal solution, Simulated Annealing, Golden Section Search and Genetic Algorithm-based heuristic methods have been developed. Performance results show that the proposed approach jointly solves the problem of UASN lifetime maximization, while providing network reliability and security.