Browsing by Author "Erel-Özçevik M."
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Item OFaaS: OpenFlow Switch as a Service for Multi Tenant Slicing in SD-CDN(Institute of Electrical and Electronics Engineers Inc., 2021) Erel-Özçevik M.; Canberk B.In Software Defined-Content Delivery Networks (SD-CDN), the policies of tenants such as Youtube, Netflix, Office 365, etc. are not the same due to having different 5G traffic requirements for such contents as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency (URLLC) and massive machine-type communication (mMTC). This leads SD-CDN multi-tenant slicing to provide different services with limited network resources, where each tenant can functionally manage their own virtual slice of a physical component according to service level agreements (SLAs). However, they are not permitted to dynamically configure their own components. Therefore, the physical end-to-end configuration of all edge devices causes extra hardware and bandwidth costs. Although software as a service (SaaS) is more preferred to handle cost-efficiency on a switch configuration that increases forwarding throughput (Mbps) with a less number of physical components in SD-CDN, the edge devices can be only served as infrastructure as a service (IaaS) currently. This motivation leads us to serve the switch as a service that includes both IaaS and SaaS characteristics. Therefore, we propose an OpenFlow as a service (OFaaS) design where each tenant has flow management and switch configuration permissions on their own virtual slice. In flow management, we define a novel Service Oriented Architecture (SOA) to orchestrate OFaaS driven topology by isolating each tenant from physical complexity. Here, each tenant can dynamically alter QoS on a flow and load balance between a sub-set of contents via OpenFlow protocol. In switch configuration; a novel OFaaS Management Algorithm for a multi-tenant slicing increases the number of tenants served per OpenFlow switch thanks to OFaaS design. It enables an end-to-end configuration via the NETCONF protocol with a novel YANG model of OFaaS. According to performance evaluation; OFaaS has the same forwarding throughput as conventional IaaS based OpenFlow switch for a homogenous content, whereas it has 71% more forwarding throughput (Mbps) and it has 40% more cost-efficient than conventional one for a heterogeneous content with $17 savings per tenant. © 2021 IEEE.Item Grant-Free SCMA Design Using SDN/NFV with Physical Layer Security(Institute of Electrical and Electronics Engineers Inc., 2023) Erel-Özçevik M.; Tekçe F.In 5G and beyond, the end-user broadcasts data into the radio domain; therefore, protecting the physical layer security (PLS) is a more challenging issue. Here, Sparse Code Multiple Access (SCMA) handles PLS naturally. Because multiple physical resources are encoded in different codes, these codes cannot be easily decoded without sharing a mother codebook and mapping matrix. However, there are a few studies about security implementation in the physical layer against the challenges of the three subdomains of end users, base stations, and the core network. Moreover, there are two new research questions for resource allocation in SCMA: How do codebooks affect the Bit Error Rate (BER) performance of end-users? In the presence or absence of a jammer or interference, how should codebooks be assigned to users? We believe that it can only be overcome by using some new paradigms together: Software-defined radio (SDR), Software-defined networking (SDN), and network function virtualization (NFV). We consider the PLS implementation on SCMA-based 5G by investigating codebook assignment in the presence of noise/jammer with a novel Quality of Service (QoS) parameter (φ), which is the ratio of the received power by the user to overloading (λ). In the SDN/NFV controller, the QoS metric-based codebook assignment algorithm prioritizes users into newly defined PEACE, NORMAL, and WAR states. In the OpenFlow table of SDR, grant-free access can be easily implemented. As a result, the BERs for different overloading are the same for the same ratio of energy rate per bit and spectral noise density. The BER is lower when the mother codebook is 150% than when the codebook is 200%. An existing codebook of one eMBB can be utilized to serve one URLLC user and two MTC users with an acceptable BER. © 2004-2012 IEEE.Item Proof of Evaluation-based energy and delay aware computation offloading for Digital Twin Edge Network(Elsevier B.V., 2023) Bozkaya E.; Erel-Özçevik M.; Bilen T.; Özçevik Y.The increasing availability of Internet of Things (IoT) applications has led to the development of new technologies. Specifically, the deployment of edge servers close to IoT devices has strengthened the edge computing paradigm. With the collaboration of Mobile Edge Computing (MEC) and cloud computing, delay-sensitive and computation-intensive tasks can be offloaded to the edge/cloud servers to improve system performance in terms of the delay and energy consumption of IoT devices. However, there is a need to schedule the computation tasks for an efficient management. More importantly, the task scheduling strategy can face data tampering attacks to deliberately modify, destroy or manipulate the decisions. To solve the above problems, in this paper, we newly propose to integrate digital twin and blockchain into the edge networks. However, it is unclear (i) how energy and delay-aware computation should be combined, and (ii) which mining computations should be executed for a secure task scheduling. The state-of-the-art focuses on task scheduling and blockchain mining, separately. Therefore, we propose a novel blockchain-based digital twin-edge network architecture where our proposed algorithm solves these two challenges at the same execution. We design a three-layer system architecture, composed of physical entity layer, digital twin edge layer and blockchain layer. In the physical entity layer, we formulate an energy and delay-aware task scheduling problem. In the digital twin edge layer, we propose a novel Proof of Evaluation (PoE)-based secure energy and delay-aware task scheduling algorithm where optimization is executed by the genetic algorithm implementation of Warehouse Location Problem (WLP). In the blockchain layer, the best-found solutions are shared with the topology in a blockchain. Here, each block includes the hash of the previous block, a genetic algorithm-based solution, nonce value, and a hash of whole blocks in the blockchain. Thus, we aim to execute the computation tasks with an acceptable delay in an energy-efficient manner and prevent data tampering attacks against the optimal computation decisions. We validate the outcomes of our PoE-based secure digital twin-edge network model with extensive evaluations. Since the proposed model distributes the task not only to the local device but also to the MEC and cloud server for delay awareness, it reduces the delay but consumes more energy. Nevertheless, the additional energy consumption can be neglected against the delay reduction. The proposed scheme is also more scalable to compare with the conventional solution. The numerical results clearly show that the proposed model provides energy and delay awareness, maintaining both data integrity and trustworthiness at the same execution of algorithm. © 2023 Elsevier B.V.Item A Genetic Optimized Federated Learning Approach for Joint Consideration of End-to-End Delay and Data Privacy in Vehicular Networks(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Erel-Özçevik M.; Özçift A.; Özçevik Y.; Yücalar F.In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing. © 2024 by the authors.Item Markov Chain-based Data Privacy Prioritizing for Task Scheduling in Digital Twin Edge Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Bozkaya-Aras E.; Erel-Özçevik M.Edge computing plays a significant role in shaping the future of Internet of Things (IoT) networks by processing high volumes of data at the network edge and meeting the desired latency demand. Although edge computing is regarded as a promising technology for these features as well as its decentralization and supportive functionalities, there is a need to develop systems to protect data security and privacy since users can face the risk of data leakage when they offload data to the edge/cloud server. In order to solve this problem, in this paper, we propose a three-layer digital twin edge network architecture for task scheduling based on edge-cloud computing. The digital twin has been used to map the IoT network to the virtual space, monitor physical assets in real-time, and perform comprehensive network analysis. On this basis, a task scheduling model that prioritizes data privacy has been introduced to prevent users’ privacy-sensitive information from being collected by attackers. The proposed model can both take full advantage of edge and cloud computing and protect data privacy. We classify computation tasks based on data privacy sensitivity and model them using the M/M/1 Markov model in local, edge server, and cloud computations. Then, we propose a data privacy prioritizing task scheduling algorithm combining digital twin and edge-cloud collaboration. This genetic optimization based algorithm significantly increases data privacy by 40% compared to the greedy approach, while keeping the time under an acceptable level. In this way, each task is distributed between the local, edge server, and cloud server. Thanks to the intelligence behavior and repetitive growth of the proposed algorithm, the optimal task scheduling is performed in a digital twin system. Our analysis reveals that data leakage attacks can be significantly decreased with the proposed task scheduling algorithm by minimizing task processing time. © 2024 IEEE.Item Average Localization Error Prediction for 5G Networks: An Investigation of Different Machine Learning Algorithms(Springer, 2024) Altay O.; Erel-Özçevik M.; Varol Altay E.; Özçevik Y.In the realm of today’s networking technologies, user localization has been a formidable challenge for recent applications. There are different approaches in pursuit of heightened position detection of an end-user with the help of GPS, Wi-Fi fingerprint and 5G equipment. However, these approaches require both deployment and maintenance costs because of equipment establishment for position tracking. Moreover, they are not capable of minimizing the localization error, especially for indoor scenarios to track the indoor position of an end-user. Hence, there is an urgent need to delve deeper into innovative approaches to drive further advancements in user localization. In response, Machine Learning (ML) approaches have recently been widely adapted to predict the localization of end-users with minimum error. More specifically, average localization error (ALE) of an end-user can be predicted in a cost-effective way by using proper data and ML methods. For this purpose, we have investigated different ML approaches to get an accurate ALE prediction scheme for 5G networks with mobile end-users. Accordingly, an existing dataset is utilized to generate localization data of end-users in which the ALE is directly calculated by Received Signal Strength Indicator. Moreover, three different normalization approaches are applied for the overarching goal of increased data quality. Consequently, six different ML algorithms, including Linear regression, support vector machine with three different kernels, Gaussian process, and ensemble least-squares boosting (LSBoost) are evaluated with respect to a set of evaluation criteria including R, R2, RMSE, and MAE. The evaluation outcomes emphasize that ensemble LSBoost method, in the context of localization prediction, outperforms the other approaches and is sufficient to yield a viable learning strategy for ALE prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item PoS-based blockchain-assisted UAV networks(Institution of Engineering and Technology, 2024) Erel-Özçevik M.[No abstract available]Item Token as a Service for Software-Defined Zero Trust Networking(Springer, 2025) Erel-Özçevik M.Zero Trust Networking (ZTN) is more challenging in a multi-tenant environment. To meet different service requirements of multi-tenants and minimize the risk of physical deployment with low operational and capital expenditures, investments in Software-Defined Networks (SDN) based ZTN have been increased. The research question is whether is there any SDN-based architecture to maintain a trusted zone in a complex multi-tenant environment, where each network equipment can be dynamically configurable by many SDN controllers in a distributed way without security breach. Therefore, this paper proposes a novel Software-Defined Zero Trust Networking (SDZTN) decoupling Cyber and Physical layers. To maintain a trusted zone, it proposes a novel Token as a Service (TaaS) that executes genetic algorithm-based service optimization and generates unique tokens by its solution and using a simply implemented JSON Web Token (JWT). It reduces authentication/authorization load in cloud servers by simplifying and distributing databases in each OpenFlow switch. According to the proposed Zero Trust Evaluation (ZTE) metric considering the token similarity and infection probability, SDZTN results in 25% higher trust than the conventional one. It also overcomes several infection attacks which have the potential to revolutionize token management systems by providing decentralized, easily implementable, and trusted solutions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.