Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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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.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: 64Citation - Scopus: 77EDHRP: Energy Efficient Event Driven Hybrid Routing Protocol for Densely Deployed Wireless Sensor Networks(Academic Press Ltd- Elsevier Science Ltd, 2015-12) Faheem, Muhammad; Abbas, Muhammad Zahid; Tuna, Gurkan; Gungor, Vehbi CagriEfficient management of energy resources is a challenging research area in Wireless Sensor Networks (WSNs). Recent studies have revealed that clustering is an efficient topology control approach for organizing a network into a connected hierarchy which balances the traffic load of the sensor nodes and improves the overall scalability and the lifetime of WSNs. Inspired by the advantages of clustering techniques, we have three main contributions in this paper. First, we propose an energy efficient cluster formation algorithm called Active Node Cluster Formation (ANCF). The core aim to propose ANCF algorithm is to distribute heavy data traffic and high energy consumption load evenly in the network by offering unequal size of clusters in the network. The developed scheme appoints each cluster head (CH) near to the sink and sensing event while the remaining set of the cluster heads (CHs) are appointed in the middle of each cluster to achieve the highest level of energy efficiency in dense deployment. Second, we propose a lightweight sensing mechanism called Active Node Sensing Algorithm (ANSA). The key aim to propose the ANSA algorithm is to avoid high sensing overlapping data redundancy by appointing a set of active nodes in each cluster with satisfy coverage near to the event. Third, we propose an Active Node Routing Algorithm (ANRA) to address complex inter and intra cluster routing issues in highly dense deployment based on the node dominating values. Extensive experimental studies conducted through network simulator NCTUNs 6.0 reveal that our proposed scheme outperforms existing routing techniques in terms of energy efficiency, end-to-end delay and data redundancy, congestion management and setup robustness. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 48Citation - Scopus: 56Analyzing the Relationship Between Energy Efficiency and Environmental and Financial Variables: A Way Towards Sustainable Development(Pergamon-Elsevier Science Ltd, 2022-08) Taskin, Dilvin; Dogan, Eyup; Madaleno, MaraThe literature has mainly relied on an annual and short span of data to analyze the relationship between energy, environmental and financial indicators. This study analyzes the relationship between energy efficiency, energy research, pollution mitigation, and FinTech by applying two novel methods-the causality test in the frequency domain [11] and the causality test in the time domain (Shi et al., 2018; 2020) on the daily data from June 17, 2016 to November 16, 2021. Empirical results from the frequency domain test report that pollution mitigation temporarily causes energy efficiency only in the short run while energy efficiency Granger causes it in the short, medium, and long run. Furthermore, energy efficiency can predict FinTech in the short, medium, and long-run; on the other way, FinTech Granger causes energy efficiency in the long and medium run, suggesting a permanent causality relationship. Empirical results from the time-varying test show a bidirectional relationship between energy efficiency, and environmental and financial variables, especially with very high significant episodes around the recent pandemic collapse. Policymakers should promote the launch of financial technologies that will provide finance through green bonds for energy efficiency improvements as well as energy efficiency improvements for pollution mitigation. Further policy implications are discussed in the study.(c) 2022 Elsevier Ltd. All rights reserved.Article Citation - WoS: 12Citation - Scopus: 13All-Surface Induction Heating With High Efficiency and Space Invariance Enabled by Arraying Squircle Coils in Square Lattice(IEEE-Inst Electrical Electronics Engineers Inc, 2018-08) Kilic, Veli Tayfun; Unal, Emre; Yilmaz, Namik; Demir, Hilmi VolkanThis paper reports an all-surface induction heating system that enables efficient heating at a constant speed all over the surface independent of the specific location on the surface. In the proposed induction system, squircle coils are placed tangentially in a two-dimensional square lattice as opposed to commonly used hexagonal packing. As a proof-of-concept demonstration, a simple model setup was constructed using a 3 x 3 coil array along with a steel plate to be inductively heated. To model surface heating, a set of six locations for the plate was designated considering symmetry points. For all of these cases, power dissipated by the system and the plate's transient heating were recorded. Independent from the specific plate position, almost equal heating speeds were measured for the similar levels of dissipated energies in the system. Using full three-dimensional electromagnetic solutions, the experimental results were also verified. The findings indicate that the proposed system is proved to enable energy efficient space-invariant heating in all-surface induction hobs.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.