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AI-Enabled Sustainable Computing for Digital Infrastructures: Challenges and Innovations
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Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
Department of Computer Information Systems, Faculty of Information & Communication Technology, University of Malta, MSD 2080 Msida, Malta
Faculty of Informatics and Computing, Singidunum University, 11010 Belgrade, Serbia
Institute of Mechanical Engineering, University of Zielona Góra, Zielona Góra, Poland
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AI-Enabled Sustainable Computing for Digital Infrastructures: Challenges and Innovations

Abstract submission deadline
15 October 2023
Manuscript submission deadline
15 December 2023
Viewed by
430

Topic Information

Dear Colleagues,

The Internet of Things (IoT) has revolutionized various aspects of our lives, with the integration of semiconductor technology and artificial intelligence (AI) playing a crucial role. However, the intensive computational requirements of AI and blockchain technologies have created significant challenges for energy-constrained IoT devices. The rapid advancement of AI technologies, such as deep learning, offers exciting opportunities for extracting reliable information from large amounts of raw sensor data in IoT applications. Blockchain, on the other hand, is gaining traction in IoT development to address security and privacy concerns due to its immutable and decentralized nature.

This Topic focuses on the latest advances and research findings in sustainable computing for IoT applications driven by AI and blockchain. It aims to offer a platform for academics and practitioners worldwide to develop innovative solutions to current challenges. The topics of interest include but are not limited to lightweight deep learning models with blockchain-based architectures, a fusion of AI and blockchain for sustainable IoT, new computing architectures for sustainable IoT systems, cyber physical systems, energy-efficient communication protocols, and security and privacy issues in sustainable computing for IoT applications.

In summary, this Topic aims to explore the interplay between AI, blockchain, and sustainable computing in the context of IoT applications and to provide insights and practical solutions for addressing the challenges of sustainable computing in digital infrastructures.

Prof. Dr. Robertas Damaševičius
Dr. Lalit Garg
Dr. Nebojsa Bacanin
Prof. Dr. Justyna Patalas-Maliszewska
Topic Editors

Keywords

  • Internet of Things (IoT)
  • artificial intelligence (AI)
  • deep learning
  • blockchain
  • sustainable computing
  • edge computing
  • cyber physical systems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.838 3.7 2011 14.9 Days 2300 CHF Submit
Digital
digital 		- - 2021 29.8 Days 1000 CHF Submit
Electronics
electronics 		2.690 3.7 2012 14.4 Days 2000 CHF Submit
Infrastructures
infrastructures 		- 3.4 2016 15.7 Days 1600 CHF Submit
Machines
machines 		2.899 3.1 2013 16.2 Days 2000 CHF Submit
Sensors
sensors 		3.847 6.4 2001 15 Days 2400 CHF Submit
Systems
systems 		2.895 4.3 2013 18.1 Days 1600 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (1 paper)

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Article
Tendon Stress Estimation from Strain Data of a Bridge Girder Using Machine Learning-Based Surrogate Model
by
Sadia Umer Khayam
,
Ammar Ajmal
,
Junyoung Park
,
In-Ho Kim
and
Jong-Woong Park
Sensors 2023, 23(11), 5040; https://doi.org/10.3390/s23115040 - 24 May 2023
Viewed by 230
Abstract
Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring the tendon force to prevent excessive creep. [...] Read more.
Prestressed girders reduce cracking and allow for long spans, but their construction requires complex equipment and strict quality control. Their accurate design depends on a precise knowledge of tensioning force and stresses, as well as monitoring the tendon force to prevent excessive creep. Estimating tendon stress is challenging due to limited access to prestressing tendons. This study utilizes a strain-based machine learning method to estimate real-time applied tendon stress. A dataset was generated using finite element method (FEM) analysis, varying the tendon stress in a 45 m girder. Network models were trained and tested on various tendon force scenarios, with prediction errors of less than 10%. The model with the lowest RMSE was chosen for stress prediction, accurately estimating the tendon stress, and providing real-time tensioning force adjustment. The research offers insights into optimizing girder locations and strain numbers. The results demonstrate the feasibility of using machine learning with strain data for instant tendon force estimation. Full article
(This article belongs to the Topic AI-Enabled Sustainable Computing for Digital Infrastructures: Challenges and Innovations)
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