Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and...
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 5298

Special Issue Editors

Dr. Rui Pinto

E-Mail Website1 Website2
Guest Editor
SYSTEC-ARISE, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal
Interests: Industry 4.0; cyber–physical systems; artificial immune systems; autonomic computing; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro M. B. Torres

E-Mail Website1 Website2
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. SYSTEC—Research Center for Systems and Technologies, ARISE—Advanced Production and Intelligent Systems Associated Laboratory, 4200-465 Porto, Portugal
Interests: electronics; instrumentation; automation; control; robotics; cyber-physical systems; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Volker Lohweg

E-Mail Website1 Website2
Guest Editor
Institute Industrial IT (inIT), Technische Hochschule Ostwestfalen-Lippe (TH OWL), Campusallee 6, D-32657 Lemgo, Germany
Interests: intelligent automation; digitalization; information fusion; industrial image processing; pattern recognition; cyber–physical (production) systems; machine learning; resource-limited electronics; mobile devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue "Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges".

Autonomous systems are emerging as game-changers in the realm of Cyber–Physical Systems (CPSs) and Smart Industry, revolutionizing how industries operate and interact with the physical world. This Special Issue is dedicated to exploring the integration and impact of autonomous systems within the CPS framework. We invite contributions that delve into the design, development, and deployment of Self-* capabilities in CPSs and industrial applications. Topics of interest include autonomous manufacturing, logistics, predictive maintenance, AI (artificial intelligence), machine learning in industrial processes, and autonomous decision-making processes. We also welcome research on the challenges and opportunities presented by autonomous systems, such as safety, reliability, security, privacy, and ethical considerations. Join us in uncovering the transformative potential of autonomous systems in shaping the future of Smart Industry.

Dr. Rui Pinto
Dr. Pedro M. B. Torres
Prof. Dr. Volker Lohweg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber–physical systems
  • smart industry
  • autonomous systems
  • self-*
  • artificial intelligence (AI)
  • machine learning
  • real-time monitoring
  • predictive maintenance
  • security and privacy in industry
  • ethical considerations in autonomous systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

22 pages, 5184 KiB  
Article
Evaluating the Vulnerability of Hiding Techniques in Cyber-Physical Systems Against Deep Learning-Based Side-Channel Attacks
by Seungun Park, Aria Seo, Muyoung Cheong, Hyunsu Kim, JaeCheol Kim and Yunsik Son
Appl. Sci. 2025, 15(13), 6981; https://doi.org/10.3390/app15136981 - 20 Jun 2025
Viewed by 379
Abstract
(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; [...] Read more.
(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; however, their effectiveness has been increasingly challenged. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs (DL-SCAs). (2) Methods: A power trace dataset was generated using a simulation environment based on Quick Emulator (QEMU) and GNU Debugger (GDB), integrating dummy traces to obfuscate execution signatures. DL models, including a Recurrent Neural Network (RNN), a Bidirectional RNN (Bi-RNN), and a Multi-Layer Perceptron (MLP), were used to evaluate classification performance. (3) Results: The models trained with dummy traces achieved high classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, DL models effectively distinguished real and dummy traces, highlighting limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against DL-SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. This study underscores the urgency of evolving security paradigms to defend against artificial intelligence-powered attacks. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

32 pages, 10560 KiB  
Article
BIM-GIS-Based Approach for Quality Management Aligned with ISO 9001
by Pablo Araya-Santelices, Pedro Moraga, Edison Atencio, Fidel Lozano-Galant and José Antonio Lozano-Galant
Appl. Sci. 2025, 15(11), 6107; https://doi.org/10.3390/app15116107 - 29 May 2025
Viewed by 675
Abstract
Quality management during construction is critical to ensuring compliance with technical specifications and quality standards. Traditional practices often rely on manual, paper-based documentation, leading to inefficiencies, data fragmentation, and poor traceability. This study presents QualiSite, a novel digital workflow that integrates Building Information [...] Read more.
Quality management during construction is critical to ensuring compliance with technical specifications and quality standards. Traditional practices often rely on manual, paper-based documentation, leading to inefficiencies, data fragmentation, and poor traceability. This study presents QualiSite, a novel digital workflow that integrates Building Information Modeling (BIM) and Geographic Information Systems (GIS), aligned with ISO 9001:2015 requirements, to enhance quality management in building projects. The research is framed under the Design Science Research Method (DSRM), guiding the iterative development and validation of the tool. QualiSite was tested in a real-world case study involving the construction of reinforced concrete walls. The results demonstrated functional improvements in inspection traceability, consistency of quality records, and coordination between field data and BIM elements. Using structured digital forms contributed to more consistent data capture and greater efficiency in recording, organizing, and visualizing quality control statuses within the 3D environment. These outcomes enabled transparent inspection processes and clear visualization of quality status across construction elements. The digital workflow also facilitated the identification of nonconformities and streamlined communication between field inspectors and model managers. This approach advances traditional quality management by embedding inspection records into a Cyber-Physical Systems (CPS) framework, contributing to the digital transformation of the Architecture, Engineering, and Construction (AEC) industry and supporting the vision of Smart Industry. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

32 pages, 1107 KiB  
Review
Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector
by Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli and Virginia Fani
Appl. Sci. 2025, 15(13), 7589; https://doi.org/10.3390/app15137589 - 7 Jul 2025
Viewed by 365
Abstract
In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling [...] Read more.
In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

34 pages, 977 KiB  
Review
Autonomous Cyber-Physical Systems Enabling Smart Positive Energy Districts
by Dimitrios Siakas, Georgios Lampropoulos and Kerstin Siakas
Appl. Sci. 2025, 15(13), 7502; https://doi.org/10.3390/app15137502 - 3 Jul 2025
Viewed by 447
Abstract
The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to [...] Read more.
The European Union (EU) is striving to achieve its goal of being climate-neutral by 2050. Aligned with the European Green Deal and in search of means to decarbonize its urban environments, the EU advocates for smart positive energy districts (PEDs). PEDs contribute to the United Nations’ (UN) sustainable development goals (SDGs) of “Sustainable Cities and Communities”, “Affordable and Clean Energy”, and “Climate Action”. PEDs are urban neighborhoods that generate renewable energy to a higher extent than they consume, mainly through the utilization of innovative technologies and renewable energy sources. In accordance with the EU 2050 aim, the PED concept is attracting growing research interest. PEDs can transform existing energy systems and aid in achieving carbon neutrality and sustainable urban development. PED is a novel concept and its implementation is challenging. This study aims to present the emerging technologies enabling the proliferation of PEDs by identifying the main challenges and potential solutions to effective adoption and implementation of PEDs. This paper examines the importance and utilization of cyber-physical systems (CPSs), digital twins (DTs), artificial intelligence (AI), the Internet of Things (IoT), edge computing, and blockchain technologies, which are all fundamental to the creation of PEDs for enhancing energy efficiency, sustainable energy, and user engagement. These systems combine physical infrastructure with digital technologies to create intelligent and autonomous systems to optimize energy production, distribution, and consumption, thus positively contributing to achieving smart and sustainable development. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

28 pages, 953 KiB  
Review
Integrating Digital Twin Software Solutions with Collaborative Industrial Systems: A Comprehensive Review for Operational Efficiency
by David A. Guerra-Zubiaga, Murat Aksu, Gershom Richards and Vladimir Kuts
Appl. Sci. 2025, 15(13), 7049; https://doi.org/10.3390/app15137049 - 23 Jun 2025
Viewed by 534
Abstract
The integration of digital twin software solutions with industrial collaborative robotics applications has gained significant attention due to its potential to enhance operational efficiency in various industries. The authors of this paper provide a comprehensive review of the literature, analyzing the benefits, challenges, [...] Read more.
The integration of digital twin software solutions with industrial collaborative robotics applications has gained significant attention due to its potential to enhance operational efficiency in various industries. The authors of this paper provide a comprehensive review of the literature, analyzing the benefits, challenges, and opportunities associated with this unification. The research methodology incorporates both quantitative and qualitative analyses of relevant scholarly articles, case studies, and industry reports. The study identifies research gaps and challenges, including data management, security, scalability, interoperability, and transitioning simulations to digital twins. To address these gaps, the authors explore published frameworks for effectively integrating digital twin software solutions with industrial collaborative robotics applications. An important challenge is to define some tools to develop a digital twin. This paper explores the tools implemented by other researchers to develop a digital twin. The findings of this research contribute to a deeper understanding of the combination of digital twins and collaborative robots, paving the way for improved operational efficiency and informed decision-making. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

34 pages, 963 KiB  
Review
Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems
by Hongming Yang, Hao Liu, Xin Yuan, Kai Wu, Wei Ni, J. Andrew Zhang and Ren Ping Liu
Appl. Sci. 2025, 15(12), 6587; https://doi.org/10.3390/app15126587 - 11 Jun 2025
Viewed by 733
Abstract
Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical [...] Read more.
Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical methods enabling this fusion, such as efficient low-rank adaptation (LoRA) for fine-tuning large models and memory-efficient Split Federated Learning (SFL) for collaborative edge training. However, this integration faces significant hurdles: the resource limitations of IoT devices, unreliable network communication, data heterogeneity, diverse security threats, fairness considerations, and regulatory demands. While other surveys cover pairwise combinations, this review distinctively analyzes the three-way synergy, highlighting how IoT, LLMs, and FL working in concert unlock capabilities unattainable otherwise. Our analysis compares various strategies proposed to tackle these issues (e.g., federated vs. centralized, SFL vs. standard FL, DP vs. cryptographic privacy), outlining their practical trade-offs. We showcase real-world progress and potential applications in domains like Industrial IoT and smart cities, considering both opportunities and limitations. Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. Addressing these areas is essential for responsibly harnessing this powerful technological blend. Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

25 pages, 1190 KiB  
Systematic Review
A Systematic Review of Reimagining Fashion and Textiles Sustainability with AI: A Circular Economy Approach
by Hiqmat Nisa, Rebecca Van Amber, Julia English, Saniyat Islam, Georgia McCorkill and Azadeh Alavi
Appl. Sci. 2025, 15(10), 5691; https://doi.org/10.3390/app15105691 - 20 May 2025
Cited by 1 | Viewed by 1219
Abstract
Artificial intelligence (AI) is revolutionizing the fashion, textile, and clothing industries by enabling automated assessment of garment quality, condition, and recyclability, addressing key challenges in sustainability. This systematic review explores the applications of AI in evaluating clothing quality and condition within the framework [...] Read more.
Artificial intelligence (AI) is revolutionizing the fashion, textile, and clothing industries by enabling automated assessment of garment quality, condition, and recyclability, addressing key challenges in sustainability. This systematic review explores the applications of AI in evaluating clothing quality and condition within the framework of a circular economy, with a focus on supporting second-hand clothing resale, charitable donations by NGOs, and sustainable recycling practices. A total of 135 research resources were identified through searching academic databases including Google Scholar, Springer, ScienceDirect, IEEE, Taylor and Francis, and Sage journals. These publications were subsequently refined down to 49 based on selected inclusion criteria. The selection of these sources from diverse databases was undertaken to mitigate any potential bias in the selection process. By analyzing the effectiveness and challenges of related peer-reviewed articles, conference papers, and technical reports, this study highlights state-of-the-art methodologies such as convolutional neural networks (CNNs), hybrid models, and other machine vision systems. A critical aspect of this review is the examination and analysis of datasets used for model development, categorized and detailed in a comprehensive table to guide future research. Whilst the findings emphasize the potential of AI to enhance quality assurance in second-hand clothing markets, streamline textile sorting for donations and recycling, and reduce waste in the fashion industry, they also highlight gaps in the available datasets, often due to limited size and scope. The types of textiles captured were most commonly swatches of fabric, with 20 studies examining these, whereas whole garments were less frequently studied, with only 7 instances. This review concludes with insights into future research directions and the promising use of AI within fashion and textiles to facilitate a transition to a circular economy. This project was supported through RMIT University’s School of Fashion and Textiles internal seed funding (2024). Full article
(This article belongs to the Special Issue Autonomous Systems in Cyber-Physical Systems and Smart Industry: Innovations and Challenges, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop