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 - 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.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 - WoS: 26Citation - Scopus: 27ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments(IEEE-Inst Electrical Electronics Engineers Inc, 2024-11) Golec, Muhammed; Gill, Sukhpal Singh; Cuadrado, Felix; Parlikad, Ajith Kumar; Xu, Minxian; Wu, Huaming; Uhlig, SteveServerless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and CO2 emission amount of these models are evaluated and compared for the training and prediction phases.Article Citation - WoS: 137Citation - Scopus: 237AI-Based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions(Elsevier, 2023-04) Iftikhar, Sundas; Gill, Sukhpal Singh; Song, Chenghao; Xu, Minxian; Aslanpour, Mohammad Sadegh; Toosi, Adel N.; Uhlig, SteveResource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.Article Citation - WoS: 24Citation - Scopus: 39A Review of On-Device Machine Learning for IoT: An Energy Perspective(Elsevier, 2024-02) Tekin, Nazli; Aris, Ahmet; Acar, Abbas; Uluagac, Selcuk; Gungor, Vehbi CagriRecently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.Article Citation - WoS: 43Citation - Scopus: 55CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0(Elsevier, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi CagriIndustry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.