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Sustainability
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AI- and IoT-Driven Solutions for Industrial Sustainability and Smart Manufacturing
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AI- and IoT-Driven Solutions for Industrial Sustainability and Smart Manufacturing

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 806

Special Issue Editors

Dr. Luisiana Sabbatini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, AN, Italy
Interests: Industry 4.0; machine learning; artificial intelligence; smart manufacturing; extended reality (XR)
Prof. Dr. Paola Pierleoni

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, AN, Italy
Interests: network protocols; wireless sensor network; Internet of Things; signal processing; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Raggiunto

E-Mail Website
Guest Editor
Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: internet of things; wireless sensor networks; wireless body sensor networks; bluetooth mesh network; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Esposito

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, AN, Italy
Interests: artificial intelligence; internet of things; edge computing; signal processing; wireless sensor networks

Special Issue Information

Dear Colleagues,

The accelerating pace of digital transformation in the industrial domain presents both unprecedented opportunities and critical challenges for achieving sustainability goals. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) within industrial systems and manufacturing systems has enabled novel approaches to energy optimization, predictive maintenance, resource efficiency, and smart decision-making. These technologies have the potential to significantly reduce environmental impact while improving the operational performance of the so-called Industry 4.0.

This Special Issue aims to gather high-quality contributions that explore the intersection between advanced digital technologies and industrial sustainability. We welcome original research articles, case studies, and comprehensive reviews that address the design, development, and deployment of AI- and IoT-based solutions for sustainable manufacturing, logistics, and industrial operations.

Key topics include, but are not limited to, the following:

  • AI and IoT synergies (Artificial Intelligence of Things, Edge-AI, Tiny-ML);
  • AI and machine learning for sustainable process optimization;
  • IoT architectures and edge computing for energy-aware industrial monitoring;
  • Predictive analytics and digital twins for circular economy strategies;
  • Sustainable supply chain management enabled by smart technologies;
  • Industrial data analytics for emission reduction and resource conservation;
  • Predictive maintenance and fault detection using machine learning;
  • Industrial AI, machine learning, and deep learning in manufacturing for industrial sustainability and smart manufacturing;
  • Ethical and policy considerations in AI/IoT-driven sustainability frameworks.

This Special Issue will serve as a platform for interdisciplinary dialogue between engineers, data scientists, sustainability experts, and industry practitioners, with the shared goal of driving innovation toward a greener, smarter, and more resilient industrial future.

Dr. Luisiana Sabbatini
Prof. Dr. Paola Pierleoni
Dr. Sara Raggiunto
Dr. Marco Esposito
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

  • industrial sustainability
  • sustainable manufacturing
  • sustainable supply chain management
  • artificial Intelligence
  • internet of things
  • energy optimization
  • predictive maintenance
  • resource efficiency
  • smart decision-making

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Published Papers (2 papers)

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Research

24 pages, 537 KB  
Article
Behavior-Dependent Pricing: An IoT-Enabled Pricing Model Under Servicizing
by Tina Arabian, Mojtaba Araghi and Hamid Noori
Sustainability 2025, 17(24), 10986; https://doi.org/10.3390/su172410986 - 8 Dec 2025
Viewed by 139
Abstract
The benefits of the servicizing business model, in which a firm sells the use or functionality of a product rather than the product itself, extend beyond attracting new customers and driving economic growth. Aligned with circular economy principles, servicizing promotes sustainability by encouraging [...] Read more.
The benefits of the servicizing business model, in which a firm sells the use or functionality of a product rather than the product itself, extend beyond attracting new customers and driving economic growth. Aligned with circular economy principles, servicizing promotes sustainability by encouraging firms to enhance product durability and customers to be more mindful of their amount of usage. However, the lack of product ownership may lead to product misuse, negatively affecting both economic and environmental outcomes. This study addresses product misuse as a major risk to servicizing firms’ performance and investigates whether, and under what conditions, adopting Behavior-Dependent Pricing (BDP) can mitigate this risk. Leveraging digital technologies such as the Internet of Things (IoT), we develop a BDP model in which a firm monitors customers’ usage behavior and provides monetary incentives for more sustainable use. We identify conditions under which BDP leads to a win–win–win outcome by increasing firm profits, enhancing customer utility, and reducing environmental impacts. This study provides firms with insights on how and when servicizing can be less vulnerable to product misuse risk that could undermine profitability, thereby encouraging adoption of the servicizing business model and generating economic and environmental benefits. Full article
(This article belongs to the Special Issue AI- and IoT-Driven Solutions for Industrial Sustainability and Smart Manufacturing)
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23 pages, 3845 KB  
Article
A Spatiotemporal Forecasting Method for Cooling Load of Chillers Based on Patch-Specific Dynamic Filtering
by Jie Li, Zhengri Jin and Tao Wu
Sustainability 2025, 17(21), 9883; https://doi.org/10.3390/su17219883 - 5 Nov 2025
Viewed by 393
Abstract
Accurate cooling load forecasting in chiller units is critical for building energy optimization, yet remains challenging due to non-stationary nonlinear dynamics driven by coupled external weather variability (solar radiation, ambient temperature) and internal thermal loads. Conventional models fail to capture the spatiotemporal coupling [...] Read more.
Accurate cooling load forecasting in chiller units is critical for building energy optimization, yet remains challenging due to non-stationary nonlinear dynamics driven by coupled external weather variability (solar radiation, ambient temperature) and internal thermal loads. Conventional models fail to capture the spatiotemporal coupling inherent in load time series, violating their stationarity assumptions. To address this, this research proposes OptiNet, a spatiotemporal forecasting framework integrating patch-specific dynamic filtering with graph neural networks. OptiNet partitions multi-sensor data into non-overlapping time patches to develop a dynamic spatiotemporal graph. A learnable routing mechanism then performs adaptive dependency filtering to capture time-varying temporal–spatial correlations, followed by graph convolution for load prediction. Validated on long-term industrial logs (52,075 multi-sensor samples at 20 min; district cooling plant in Zhangjiang, Shanghai, with multiple chillers, towers, pumps, building meters, and a weather station), OptiNet achieves consistently lower MAE and MSE than Graph WaveNet across 6–144-step horizons and sampling frequencies of 20–60 min; among 30 set-tings it leads in 26, with MSE reductions up to 27.8% (60 min, 72-step) and typical long-horizon (72–144 steps) gains of ≈2–18% MSE and ≈1–15% MAE. Crucially, the model provides interpretable spatial-temporal dependencies (e.g., “Zone B solar radiation influences Unit 2 load with 4-h lag”), enabling data-driven chiller sequencing strategies that reduce electricity consumption by 12.7% in real-world deployments—directly advancing energy-efficient building operations. Full article
(This article belongs to the Special Issue AI- and IoT-Driven Solutions for Industrial Sustainability and Smart Manufacturing)
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