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Sustainability
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Sustainable Engineering Trends and Challenges Toward Industry 4.0
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Sustainable Engineering Trends and Challenges Toward Industry 4.0

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 2846

Special Issue Editors

Prof. Dr. Andreea Cristina Ionica

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Guest Editor
Department of Management and Industrial Engineering, University of Petrosani, 332006 Petrosani, Romania
Interests: sustainable systems development; quality of life; innovative software project management; Society 5.0
Prof. Dr. Jose Machado

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Guest Editor
Mechanical Engineering Department, MEtRICs Research Center, University of Minho, 4800-058 Guimarães, Portugal
Interests: cyber-physical systems; dependable controllers for dependable mechatronic systems; mechatronic systems design for medical/biomedical applications, wellbeing and/or rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable engineering is essential in meeting the needs of Industry 4.0, as the digital revolution transforms manufacturing, supply chains, and service industries. The integration of cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing offers vast opportunities for innovation, but also raises concerns about their environmental impact. Sustainable engineering addresses these challenges by developing processes and frameworks that ensure technological advancements align with environmental sustainability. This approach seeks to optimize energy efficiency, minimize waste, and promote a broader vision of sustainability in industrial systems. As industries continue to evolve, sustainable engineering becomes vital in balancing innovation with ecological responsibility, ensuring that the advancements driving Industry 4.0 contribute to a more sustainable future.

The aim of this Special Issue is to explore the latest advancements in sustainable engineering within the context of Industry 4.0. Aligned with the journal’s mission to promote sustainable development, this Special Issue will focus on how the increasing digitalization of industries impacts environmental sustainability. Authors are invited to submit research that addresses key topics such as energy-efficient engineering practices, sustainable design principles, green technologies, and lifecycle management in the era of digital transformation. This collection seeks to highlight innovative approaches that ensure industrial growth and technological progress do not compromise environmental goals, contributing to a more sustainable future.

Topics of interest include, but are not limited to, the following:

  • Sustainable design, and innovation for Industry 4.0;
  • Lifecycle assessment and management of systems for sustainability;
  • Green cloud computing, big data, and resource optimization;
  • Integrating sustainability into software development methodologies and project management practices;
  • Balancing sustainability with quality assurance and performance;
  • Innovations in sustainable engineering tools, frameworks, and best practices;
  • The role of sustainable engineering in driving innovation and fostering resilient systems.

This Special Issue invites interdisciplinary research that contributes to the development of environmentally responsible solutions for the evolving digital industry. 

We look forward to receiving your contributions. 

Prof. Dr. Andreea Cristina Ionica
Dr. Jose Machado
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 250 words) can be sent to the Editorial Office for assessment.

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. Sustainability 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

  • sustainable engineering
  • computer simulation
  • product lifecycle management
  • eco-friendly software design
  • digital transformation
  • sustainable project management
  • industrial robots
  • innovation in software development
  • software quality and sustainability
  • big data and data analysis

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

Published Papers (2 papers)

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24 pages, 2155 KB  
Article
Distributed IoT-Based Predictive Maintenance Framework for Solar Panels Using Cloud Machine Learning in Industry 4.0
by Alin Diniță, Cosmina-Mihaela Rosca, Adrian Stancu and Catalin Popescu
Sustainability 2025, 17(21), 9412; https://doi.org/10.3390/su17219412 - 23 Oct 2025
Viewed by 1114
Abstract
Renewable energy systems in the Industry 4.0 era have maintenance and production maximization as their central element, depending on the type of source. For solar panels, achieving these goals requires periodic cleaning of dust deposits. This research integrates the detection of dust particles [...] Read more.
Renewable energy systems in the Industry 4.0 era have maintenance and production maximization as their central element, depending on the type of source. For solar panels, achieving these goals requires periodic cleaning of dust deposits. This research integrates the detection of dust particles on solar panels using classification models based on machine learning models integrated into the Azure platform. However, the main contribution of the work does not lie in the development or improvement of a classification model, but in the design and implementation of an Internet of Things (IoT) hardware–software infrastructure that integrates these models into a complete predictive maintenance workflow for photovoltaic parks. The second objective focuses on how the identification of dust particles further generates alerts through a centralized platform that meets the needs of Industry 4.0. The methodology involves analyzing how the Azure Custom Vision tool is suitable for solving such a problem, while also focusing on how the resulting system allows for integration into an industrial workflow, providing real-time alerts when excessive dust is generated on the panels. The paper fits within the theme of the Special Issue by combining digital technologies from Industry 4.0 with sustainability goals. The novelty of this work lies in the proposed architecture, which, unlike traditional IoT approaches where the decision is centralized at the level of a single application, the authors propose a distributed logic where the local processing unit (Raspberry Pi) makes the decision to trigger cleaning based on the response received from the cloud infrastructure. This decentralization is directly reflected in the reduction in operational costs, given that the process is not a rapid one that requires a high speed of reaction from the system. Full article
(This article belongs to the Special Issue Sustainable Engineering Trends and Challenges Toward Industry 4.0)
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18 pages, 3325 KB  
Article
AI-Driven Arm Movement Estimation for Sustainable Wearable Systems in Industry 4.0
by Emanuel Muntean, Monica Leba and Andreea Cristina Ionica
Sustainability 2025, 17(14), 6372; https://doi.org/10.3390/su17146372 - 11 Jul 2025
Cited by 1 | Viewed by 675
Abstract
In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and [...] Read more.
In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and smart manufacturing, demands the evolution of operational methodologies to ensure processes’ sustainability. One area of focus is the development of wearable systems that utilize artificial intelligence for the estimation of arm movements, which can enhance the ergonomics and efficiency of labor-intensive tasks. This study proposes a Random Forest-based regression model to estimate upper arm kinematics using only shoulder orientation data, reducing the need for multiple sensors and thereby lowering hardware complexity and energy demands. The model was trained on biomechanical data collected via a minimal three-IMU wearable configuration and demonstrated high predictive performance across all motion axes, achieving R2 > 0.99 and low RMSE scores on training (1.14, 0.71, and 0.73), test (3.37, 1.97, and 2.04), and unseen datasets (2.77, 0.78, and 0.63). Statistical analysis confirmed strong biomechanical coupling between shoulder and upper arm motion, justifying the feasibility of a simplified sensor approach. The findings highlight the relevance of our method for sustainable wearable technology design and its potential applications in rehabilitation robotics, industrial exoskeletons, and human–robot collaboration systems. Full article
(This article belongs to the Special Issue Sustainable Engineering Trends and Challenges Toward Industry 4.0)
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