WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Data Paper Citation - WoS: 34Citation - Scopus: 41Big Data Acquired by Internet of Things-Enabled Industrial Multichannel Wireless Sensors Networks for Active Monitoring and Control in the Smart Grid Industry 4.0(Elsevier, 2021-04) Faheem, Muhammad; Fizza, Ghulam; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Ngadi, Md. Asri; Gungor, Vehbi CagriSmart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyberphysical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) re-quirements in the smart grid. In this context, this paper de-scribes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assign-ment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid. (C) 2021 The Authors. Published by Elsevier Inc.Data Paper Citation - WoS: 26Citation - Scopus: 33Big Datasets of Optical-Wireless Cyber-Physical Systems for Optimizing Manufacturing Services in the Internet of Things-Enabled Industry 4.0(Elsevier, 2022-06) Faheem, Muhammad; Butt, Rizwan AslamThe Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Article Citation - WoS: 2Evolution of Production Spaces: A Historical Review for Projecting Smart Factories(Konya Technical Univ, Fac Architecture & design, 2023) Basegmez, Merve Pekdemir; Asiliskender, BurakFactories are transforming not only mechanically and technologically but also architecturally due to emerging developments in the industry and fabrication: This new process, called the Second Machine Age or Industry 4.0, a new model is designed in production by providing the human-machine partnership over a virtual network. It is aimed that the machines used in production and the people participating in different stages of production can work in different spaces. In time, jobs that require human power will be replaced by robots, and a new order is being considered where there will be no people in production spaces, and they can work in the virtual environment. Production for human beings is mostly from material production to digital production; labour will turn into digital labour. For this reason, it is thought that production spaces will turn into smart factories with only machines and production robots and no workers. And now the question is: what is a smart factory?The revolutions in the industry history started with the invention of the steam engine; then, new technological revolutions were experienced with the use of electricity in production, the development of automation systems and internetbased systems. While technology and production tools are constantly changing, these developments also affect production spaces. Factories are also transforming to keep up with these rapid and continuous physical and fictional innovations. This study focuses on the architectural evolution of factories by following the technological revolutions of the industry. It examines the main criteria in the process of change and transformation of factories and spatial reflections of the revolutions. It establishes a relationship between production technology and the needs of the production spaces and seeks references from past samples. The study aims to review the historical background for generating a projection to new production spaces and to be a new discussion for future factories.