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: 24Citation - Scopus: 32Routing Protocol Design Guidelines for Smart Grid Environments(Elsevier, 2014-02) Temel, Samil; Gungor, Vehbi Cagri; Kocak, TaskinThe evaluation of the current electric power grid with novel communication facilities is one of the most challenging and exciting issues of the 21st century. The modern grid technology is called the smart grid in the sense that it utilizes digital communication technologies to monitor and control the grid environments, which ultimately require novel communication techniques to be adapted to the system. Wireless sensor networks (WSN) have. recently been considered as a cost-effective technology for the realization of reliable remote monitoring systems for smart grid. However, problems such as noise, interference and fading in smart grid environments, make reliable and energy-efficient multi-hop routing a difficult task for WSNs in smart grid. Our main goal is to describe advantages and applications of WSNs for smart grid and motivate the research community to further investigate this promising research area. In this study we have investigated and experimented some of the well-known on-demand, table-driven and QoS-aware routing protocols, in terms of packet delivery ratio, end-to-end delay, and energy consumption to show the advantages and disadvantages of each routing protocol type in different smart grid spectrum environments. The environmental characteristics which are based on real-world field tests are injected into ns-2 Network Simulator and the performance of four different multi-hop routing protocols is investigated. Also, we have shown that traditional multi-hop routing protocols cannot deliver adequate performance on smart grid environments. Hence, based on our simulation results, we present some guidelines on how to design routing protocols specifically for smart grid environments. (C) 2013 Elsevier B.V. All rights reserved.Article Citation - WoS: 63Citation - Scopus: 107Research Article Energy Consumption of On-Device Machine Learning Models for IoT Intrusion Detection(Elsevier, 2023-04) Tekin, Nazli; Acar, Abbas; Aris, Ahmet; Uluagac, A. Selcuk; Gungor, Vehbi CagriRecently, Smart Home Systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection Systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption.Article Citation - WoS: 39Citation - Scopus: 49Quality-of Differentiation in Single-Path and Multi-Path Routing for Wireless Sensor Network-Based Smart Grid Applications(Elsevier, 2014-11) Sahin, Dilan; Gungor, Vehbi Cagri; Kocak, Taskin; Tuna, GurkanElectrical grid is one of the most important infrastructure of the modern nation. However, power grid has been aged over 100 years and prone to major failures. The imbalance between power demand and supply, the equipment failures and the lack of comprehensive monitoring and control capabilities are other important signs to take incremental steps for switching to a smarter power grid with effective communication, automation and monitoring skills. This new concept is named as smart grid, which is a modern power grid system with advanced communication, monitoring, sensing and control capabilities. Wireless sensor network (WSN) concept places an important role in this modernization process of the power grid with its efficient and low-cost deployment characteristics. However, harsh and complex smart grid environmental conditions, dynamic topology changes, connectivity problems, interference and fading may pose some challenges for the communication performance of WSN technology. For this objective, in this paper, the use of multi-path and single-path QoS-aware routing algorithms under harsh SG environmental conditions is investigated in order to evaluate their service differentiation capabilities in reliability and timeliness domains. In this regard, this study is an important step towards developing novel routing protocols specifically designed for smart grid environments. (C) 2014 Elsevier B.V. All rights reserved.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: 25Citation - Scopus: 29On the Interdependency Between Multi-Channel Scheduling and Tree-Based Routing for WSNs in Smart Grid Environments(Elsevier, 2014-06) Yigit, Melike; Incel, Ozlem Durmaz; Gungor, Vehbi CagriField tests show that the link-quality of wireless links in different smart grid environments, such as outdoor substation, varies greatly both in space and time because of various factors, including multi-path, fading, node contentions, radio frequency (RF) interference, and noise. This leads to both time and location dependent capacity limitations of wireless links in smart grid environments. To improve network capacity in such environments, multichannel communication and the use of proper routing topologies emerge as efficient solutions to achieve simultaneous, interference-free transmissions over multiple channels. In this paper, we explore the impact of multi-channel communication and the selection of efficient routing topologies on the performance of wireless sensors networks in different smart grid spectrum environments. Particularly, we evaluate the network performance using a receiver-based channel selection method and using different routing trees, including routing trees constructed considering the link qualities, Capacitated Minimum Spanning Trees (CMSTs), capacitated minimum spanning tree considering link qualities and Minimum Hop Spanning Trees (MHSTs). We focus on performance measures such as delay and throughput that can benefit from the simultaneous parallel transmissions and show that the use multiple channels together with routing trees that consider network capacity and link quality, i.e., capacitated minimum spanning tree considering link qualities, substantially improve the network performance in harsh smart-grid environments compared to single-channel communication and minimum-hop routing trees. (C) 2014 Elsevier B.V. All rights reserved.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 Lifetime Maximization of IoT-Enabled Smart Grid Applications Using Error Control Strategies(Elsevier, 2024-12) Tekin, Nazli; Dedeturk, Bilge Kagan; Gungor, Vehbi CagriRecently, with the advancement of Internet of Things (IoT) technology, IoT-enabled Smart Grid (SG) applications have gained tremendous popularity. Ensuring reliable communication in IoT-based SG applications is challenging due to the harsh channel environment often encountered in the power grid. Error Control (EC) techniques have emerged as a promising solution to enhance reliability. Nevertheless, ensuring network reliability requires a substantial amount of energy consumption. In this paper, we formulate a Mixed Integer Programming (MIP) model which considers the energy dissipation of EC techniques to maximize IoT network lifetime while ensuring the desired level of IoT network reliability. We develop meta-heuristic approaches such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) to address the high computation complexity of large-scale IoT networks. Performance evaluations indicate that the EC-Node strategy, where each IoT node employs the most energy-efficient EC technique, yields a minimum of 8.9% extended lifetimes compared to the EC-Net strategies, where all IoT nodes employ the same EC method for a communication. Moreover, the PSO algorithm reduces the computational time by 77% while exhibiting a 2.69% network lifetime decrease compared to the optimal solution.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: 45Citation - Scopus: 57Energy Efficient Multi-Objective Evolutionary Routing Scheme for Reliable Data Gathering in Internet of Underwater Acoustic Sensor Networks(Elsevier, 2019-10) Faheem, Muhammad; Ngadi, Asri; Gungor, Vehbi Cagri; Ngadi, Md AsriEarth's surface is covered with two-thirds of water. The marine world covers the lakes, rivers and sea and is rich in natural resources largely unexplored by human beings. Recently, underwater wireless sensor network (UWSN) with the advancement in the Internet of underwater smart things has emerged as promising networking techniques to explore the mysteries of vastly unexplored ocean environments for several underwater applications. These applications include offshore exploration, pollution monitoring, disaster prevention, oceanographic data collection, offshore oil fields monitoring, tactical surveillance applications and several others. However, the underwater channel impairments caused by multipath effects, fading, bit errors, variable and high latency and low bandwidth severely limits the data transmission reliability for UWSNs-based applications. This results in poor quality-aware data gathering in UWSNs. Therefore, designing a quality of service (QoS)-aware data gathering protocol to monitor and explore oceans is challenging in the underwater environments. In this paper, we propose a bio-inspired multi-objective evolutionary routing protocol (called MERP) for UWSNs-based applications. The designed routing protocol exploits the features of the natural evolution of the multi-objective genetic algorithm in order to provide reliable and energy-aware information gathering in UWSNs. The extensive simulation results show that the developed protocol attains its defined goals compared to existing UWSNs-based routing protocols during monitoring and exploring underwater environments. (C) 2019 Elsevier B.V. All rights reserved.