Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 263Citation - Scopus: 384Smart Grid Communication and Information Technologies in the Perspective of Industry 4.0: Opportunities and Challenges(Elsevier, 2018-11) Faheem, M.; Shah, S. B. H.; Butt, R. A.; Raza, B.; Anwar, M.; Ashraf, M. W.; Gungor, V. C.The fourth industrial revolution known as Industry 4.0 has paved the way for a systematical deployment of the modernized power grid (PG) to manage continuously growing energy demand by integrating renewable energy resources. In the context of Industry 4.0, a smart grid (SG) by employing advanced Information and Communication Technologies (ICTs), intelligent information processing (IIP) and future-oriented techniques (FoT) allows energy utilities to monitor and control power generation, transmission and distribution processes in more efficient, flexible, reliable, sustainable, decentralized, secure and economic manners. Despite providing immense opportunities, SG has many challenges in the context of Industry 4.0 (I 4.0). To this end, this paper presents a comprehensive presentation on critical smart grid components with international standards and information technologies in the context of Industry 4.0. In addition, this study gives an overview of different smart grid applications, their benefits, characteristics, and requirements. Also, this research investigates and explores different wired and wireless communication technologies used in smart grid with their benefits and characteristics. Finally, this article discusses a number of critical challenges and open issues and future research directions. (C) 2018 Elsevier Inc. All rights reserved.Article Citation - WoS: 52Citation - Scopus: 63MGRP: Mobile Sinks-Based QoS-Aware Data Gathering Protocol for Wireless Sensor Networks-Based Smart Grid Applications in the Context of Industry 4.0-Based on Internet of Things(Elsevier Science Bv, 2018-05) Faheem, Muhammad; Gungor, V. C.The recent advances in internet of things (IoT) and industrial wireless sensor networks (IWSNs) paradigm provide a promising opportunity for upgrading todays elderly electricity industrial systems and even allow the fourth stage of the industrial revolution, referred to as smart grid industry (SGI) 4.0. In SGI 4.0 paradigm, the WSNs are considered as promising solutions due to their advantages, such as cable replacement, ease of deployment, flexibility, and cost reduction. However, harsh and complex smart grid (SG) environments pose great challenges to guarantee reliable communication for WSNs-based SG applications due to equipment noise, electromagnetic interference, multipath effects and fading in SG environments. This results in deteriorating the quality-of-service (QoS) requirements as well as the network lifetime of multi-hop communication-based WSNs for SG applications. Thus, for SGI 4.0 paradigm to come true, a WSN-based highly reliable communication infrastructure is crucial that will wirelessly connect and integrate power system components for more efficient, reliable, and intelligent operations of the next-generation electricity power grids. To address these challenges, in this paper a novel multi-mobile sinks-based QoS-aware data gathering protocol (called MQRP) for WSNs-based SG applications has been proposed to empower SGI 4.0. The extensive simulations study is carried through a network simulation tool called EstiNet9.0. The obtained experimental facts show that the proposed scheme has not only improved the QoS performance metrics, such as packet delivery ratio, memory utilization, control message overhead, residual energy, network lifetime, and throughput, but also reduced packet error rate and end-to-end delay compared to existing data collection schemes. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 2Citation - Scopus: 2Lifetime 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 - Scopus: 56Industrial Wireless Sensor and Actuator Networks in Industry 4.0: Exploring Requirements, Protocols, and Challenges—A MAC Survey(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019-08-13) Raza, Saleem; Faheem, Muhammed Yasir; Güneş, Mesut; Guenes, MesutThe vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. In this article, we explore IWSAN, its applications, requirements, challenges, and solutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards and technologies. A number of application-specific MAC protocols developed to support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, we emphasize on existing challenges and present important future directions. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Citation - Scopus: 6High Spatial Resolution IoT Based Air PM Measurement System(Springer, 2021-04-12) Icoz, Ebru; Malik, Fasih M.; Icoz, KutayAir pollution is one of the global problems of the current era. According to World Health Organization more than 80% of the people living in metropolitan areas breathe air which exceeds the guideline limits. Particulate matter, the mixture of liquid and solid particles having diameters less than 10 mu m, is one of the important pollutants in the air. The main source of the Particulate matter is mostly burning reactions associated with industry, vehicles and homes. Several studies have shown the lethal impact of particulate matter to public health and environment. The rise of particulate matter amount in air has been linked to several health problems such as not only respiratory diseases but also mortality in infants and heart attacks. Currently, bulky stations which are high-cost and have limited spatial resolution are used to monitor the air quality. In this study we developed an alternative particulate matter measurement system which is portable and low-cost (less than 200 USD) and also integrated with cloud computing. The system allows real time distant monitoring of PM particles with high spatial resolution (meter range). The developed sensor system is able to provide air quality data in correlation with the existing stations (R-2 = 0.87). The statistical comparison between the developed system and the reference methods revealed that two systems produced statistically equal results in detecting the variations of the particulate matter.Article Citation - Scopus: 63Energy Efficient and Reliable Data Gathering Using Internet of Software-Defined Mobile Sinks for WSNS-Based Smart Grid Applications(Elsevier B.V., 2019-10) Faheem, Muhammed Yasir; Butt, Rizwan Aslam; Raza, Basit; Ashraf, Muhammad Waqar; Ngadi, M. A.; Güngör, Vehbi ÇağrıThe smart grid is an emerging concept that introduces innovative ways to handle the power quality and reliability issues for both service provider and consumers. The key aims of the smart grid (SG) in smart cities (SCs) is to preserve a certain level of residents’ life quality and support the entire spectrum of their economic activities. In this paper, we present a novel Energy Efficient and Reliable Data Gathering Routing Protocol (ODGRP) for wireless sensor networks (WSNs)-based smart grid applications. The developed scheme employs a software-defined centralized controller and multiple mobile sinks for energy efficient and reliable data gathering from WSNs in the SG. The extensive simulation results conducted through the EstiNet 9.0 show that the designed scheme outperforms existing approaches and achieves its defined goals for event-driven applications in the SG. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 109Citation - Scopus: 129Energy Efficient and QoS-Aware Routing Protocol for Wireless Sensor Network-Based Smart Grid Applications in the Context of Industry 4.0(Elsevier Science Bv, 2018-07) Faheem, M.; Gungor, V. C.Recently, there have been great advances in internet of things (IoT) and wireless sensor networks (WSNs) leading to the fourth industrial revolution in power grid, namely, Smart Grid Industry 4.0 (SGI 4.0). In the Smart Grid Industry 4.0 framework, the WSNs have the potential to improve power grid efficiency by cable replacement, deployment flexibility, and cost reduction. However, the smart grid (SG) environment that the WSNs operate in is very challenging because of equipment noise, dust, heat, electromagnetic interference, multipath effects and fading, which make it difficult for current WSNs to provide reliable communication. For SGI 4.0 to come true, a WSN-based highly reliable communication infrastructure is essential for successful operation of the next-generation electricity power grids. To address this need, in this paper a novel dynamic clustering based energy efficient and quality-of-service (QoS)-aware routing protocol (called EQRP), which is inspired by the real behavior of the bird mating optimization (BMO), has been proposed. The proposed distributed scheme improves network reliability significantly and reduces excessive packets retransmissions for WSN-based SG applications. Performance results show that the proposed protocol has successfully reduced the end-to-end delay and has improved packet delivery ratio, memory utilization, residual energy, and throughput. (C) 2017 Elsevier B.V. All rights reserved.Article Citation - WoS: 8Citation - Scopus: 9Edgebus: Co-Simulation Based Resource Management for Heterogeneous Mobile Edge Computing Environments(Elsevier, 2024-12) Ali, Babar; Golec, Muhammed; Gill, Sukhpal Singh; Wu, Huaming; Cuadrado, Felix; Uhlig, SteveKubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container orchestration. Intelligent and efficient container placement in Mobile Edge Computing (MEC) is challenging subjected to user mobility, and surplus but heterogeneous computing resources. One solution to constantly altering user location is to relocate containers closer to the user; however, this leads to additional underutilized active nodes and increases migration's computational overhead. On the contrary, few to no migrations are attributed to higher latency, thus degrading the Quality of Service (QoS). To tackle these challenges, we created a framework named EdgeBus(1), which enables the co-simulation of container resource management in heterogeneous MEC environments based on Kubernetes. It enables the assessment of the impact of container migrations on resource management, energy, and latency. Further, we propose a mobility and migration cost-aware (MANGO) lightweight scheduler for efficient container management by incorporating migration cost, CPU cores, and memory usage for container scheduling. For user mobility, the Cabspotting dataset is employed, which contains real-world traces of taxi mobility in San Francisco. In the EdgeBus framework, we have created a simulated environment aided with a real-world testbed using Google Kubernetes Engine (GKE) to measure the performance of the MANGO scheduler in comparison to baseline schedulers such as IMPALA-based MobileKube, Latency Greedy, and Binpacking. Finally, extensive experiments have been conducted, which demonstrate the effectiveness of the MANGO in terms of latency and number of migrations.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: 20Citation - Scopus: 30BlockFaas: Blockchain-Enabled Serverless Computing Framework for AI-Driven IoT Healthcare Applications(Springer, 2023-11-03) Golec, Muhammed; Gill, Sukhpal Singh; Golec, Mustafa; Xu, Minxian; Ghosh, Soumya K.; Kanhere, Salil S.; Uhlig, SteveWith the development of new sensor technologies, Internet of Things (IoT)-based healthcare applications have gained momentum in recent years. However, IoT devices have limited resources, making them incapable of executing large computational operations. To solve this problem, the serverless paradigm, with its advantages such as dynamic scalability and infrastructure management, can be used to support the requirements of IoT-based applications. However, due to the heterogeneous structure of IoT, user trust must also be taken into account when providing this integration. This problem can be overcome by using a Blockchain that guarantees data immutability and ensures that any data generated by the IoT device is not modified. This paper proposes a BlockFaaS framework that supports dynamic scalability and guarantees security and privacy by integrating a serverless platform and Blockchain architecture into latency-sensitive Artificial Intelligence (AI)-based healthcare applications. To do this, we deployed the AIBLOCK framework, which guarantees data immutability in smart healthcare applications, into HealthFaaS, a serverless-based framework for heart disease risk detection. To expand this framework, we used high-performance AI models and a more efficient Blockchain module. We use the Transport Layer Security (TLS) protocol in all communication channels to ensure privacy within the framework. To validate the proposed framework, we compare its performance with the HealthFaaS and AIBLOCK frameworks. The results show that BlockFaaS outperforms HealthFaaS with an AUC of 4.79% and consumes 162.82 millijoules less energy on the Blockchain module than AIBLOCK. Additionally, the cold start latency value occurring in Google Cloud Platform, the serverless platform into which BlockFaaS is integrated, and the factors affecting this value are examined.