Digital Twin Applications for Smart and Connected Cities

Utku, Durdu Hakan; Çatak, Ferhat Özgür; Kuzlu, Murat; Sarp, Salih; Jovanovic, Vukica; Cali, Ümit; Zohrabi, Nasibeh

doi:10.1007/978-981-99-0252-1_6

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…According to UTKU, Durdu Hakan et al (2023), Digital Twin (DT) technologies generate a virtual system representation, allowing for monitoring of its operations, linkages, and interactions. Thus, fresh technical approaches and concepts are being researched to optimize building processes by maximizing restricted resources [122].…”

Section: 2mentioning

confidence: 99%

Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

Almusaed,

Yitmen,

Almssad

2023

Preprint

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This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. The preceding methodology, AEC Industry 6.0, has substantially improved the efficacy and sustainability of construction by using new technology, digitalization, and environmentally conscious methods. Extensive discussions have occurred on the various benefits of AEC Industry 5.0. These merits include heightened stakeholder involvement, increased efficiency via automation and robots, decision-making processes centered around data, and the optimization of resources. Nevertheless, this study also examines the obstacles linked to the complete adoption of AEC Industry 6.0. These challenges require proficient professionals who can effectively utilize contemporary technologies, manage diverse stakeholder technological competencies, establish interoperable systems and standards, and reinforce cybersecurity measures. This study examines the viability of implementing concepts from Industry 6.0 to create intelligent, sustainable, and environmentally friendly structures. The objective is to showcase the potentially revolutionary impact that these principles may have on the construction industry. This study explores the possible applications of AEC Industry 6.0 within the Architecture, Engineering, and Construction (AEC) sector, explicitly highlighting the anticipated industry-wide consequences of the forthcoming paradigm shift. This paper comprehensively examines big data analytics, the Internet of Things (IoT), and collaborative robots as they pertain to the AEC Industry 6.0. This research investigates the interplay between organizational dynamics, human actors, and robotic systems to provide light on the opportunities and obstacles associated with integrating AEC Industry 6.0 concepts into the construction of buildings.

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Section: 2mentioning

confidence: 99%

Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

Almusaed,

Yitmen,

Almssad

2023

Preprint

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DT-AXYOLOV5: An Efficient Digital Twin–Assisted Deep-Learning-Based Blockchain Framework for Patient Discomfort Detection in Smart Healthcare System

Antony Vijay,

Premkumar,

Revathi

2024

Transforming Industry Using Digital Twin Technology

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Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Abbas,

Ahmad,

Syed

et al. 2024

IEEE Access

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With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.

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Digital Twin Applications for Smart and Connected Cities

Cited by 4 publications

References 39 publications

Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

DT-AXYOLOV5: An Efficient Digital Twin–Assisted Deep-Learning-Based Blockchain Framework for Patient Discomfort Detection in Smart Healthcare System

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

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