Submit to Special Issue Submit Abstract to Special Issue Review for Technologies Propose a Special Issue

Journal Menu

Journal Browser

Emerging Technologies and Intelligent Systems for Sustainable Development

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 15 May 2026 | Viewed by 14327

Share This Special Issue

Special Issue Editors

Dr. Yousef Farhaoui

E-Mail Website
Guest Editor

Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, Meknes 50050, Morocco
Interests: AI; software engineering; computer communications (networks); big data analysis; data mining
Special Issues, Collections and Topics in MDPI journals

Dr. Hamed Taherdoost

E-Mail Website
Guest Editor

School of Arts, Science, and Technology, University Canada West, Vancouver, BC V6B 1V9, Canada
Interests: blockchain; AI-driven tech; cybersecurity and innovation management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of digital technologies is transforming the way we approach sustainability across various sectors. This Special Issue, “Emerging Technologies and Intelligent Systems for Sustainable Development”, aims to explore the integration of cutting-edge innovations—such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, robotics, and edge computing—in designing intelligent, adaptive, and sustainable systems.

The objective is to gather high-quality research and practical insights on how these technologies can be harnessed to address critical global challenges, including climate change, resource management, smart cities, green energy, precision agriculture, healthcare, and more. Emphasis will be placed on intelligent systems that improve efficiency, reduce environmental impact, and support data-driven decision-making for long-term sustainability.

We invite original research articles, case studies, and reviews that contribute to the development, deployment, and evaluation of intelligent and sustainable solutions. Interdisciplinary contributions that connect engineering, computer science, and sustainability science are especially welcome.

This Special Issue provides a platform for academics, industry professionals, and policymakers to present novel approaches and discuss future directions at the intersection of emerging technologies and sustainable development.

Dr. Yousef Farhaoui
Dr. Hamed Taherdoost
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. Technologies is an international peer-reviewed open access monthly 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 1800 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

artificial intelligence (AI)
Internet of Things (IoT)
intelligent systems
smart cities
sustainable development
green computing
edge and fog computing
machine learning
blockchain for sustainability
cyber–physical systems
renewable energy systems
digital twins
smart agriculture
environmental monitoring
data-driven decision-making
energy efficiency
predictive analytics
sustainable infrastructure
autonomous systems
human-centered AI

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.

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review, Other

28 pages, 1543 KB

Open AccessArticle

Green Computing for Critical Infrastructure: A Sustainability-First AI Framework for Energy-Efficient Anomaly Detection in Industrial Control Systems

by Muhammad Muzamil Aslam, Ali Tufail, Yepeng Ding, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong and Megat F. Zuhairi

Technologies 2026, 14(5), 267; https://doi.org/10.3390/technologies14050267 - 29 Apr 2026

Viewed by 316

Abstract

Industrial Control Systems (ICSs) face dual imperatives: protecting critical infrastructure from escalating cybersecurity threats while reducing the environmental impact of AI-powered defense mechanisms. Current deep learning anomaly detection approaches achieve security performance but consumes substantial computational resources, creating an environmental paradox in which AI solutions designed to protect infrastructure contribute to carbon emissions at scale. This competition between cybersecurity effectiveness and sustainability objectives intensifies as regulatory frameworks increasingly mandate both security resilience and environmental accountability. This research presents Green-USAD, a sustainability-first AI framework that inverts traditional design paradigms by integrating energy efficiency as a primary architectural constraint from inception rather than applying compression retrospectively. The proposed approach advances green computing for critical infrastructure through four key contributions: (1) a compressed architecture with validation-guided convergence protocols achieving competitive detection performance with minimal computational overhead; (2) a multi-objective optimization framework using the Analytic Hierarchy Process to systematically balance security and sustainability requirements; (3) a hardware-validated energy measurement methodology addressing reproducibility challenges in green AI literature; and (4) a comprehensive evaluation demonstrating cross-datasets and edge-deployment viability. Validation on ICS benchmarks demonstrates that sustainability-first design achieves substantial energy reduction while maintaining operational detection accuracy, with measured training consumption below 1% of conventional approaches and proportional carbon emission reductions. Comparative analysis against post hoc compression baselines establishes fundamental advantages of design-from-inception over train-then-compress paradigms. Edge device deployment on resource-constrained hardware confirms real-world applicability for distributed industrial environments. Results establish that robust cybersecurity and environmental sustainability represent unified rather than competing objectives when intelligent systems are designed with sustainability as a foundational principle. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

30 pages, 1924 KB

Open AccessArticle

TinyML for Sustainable Edge Intelligence: Practical Optimization Under Extreme Resource Constraints

by Mohamed Echchidmi and Anas Bouayad

Technologies 2026, 14(4), 215; https://doi.org/10.3390/technologies14040215 - 7 Apr 2026

Viewed by 499

Abstract

Deep learning has emerged as an effective tool for automatic waste classification, supporting cleaner cities and more sustainable recycling systems. Because environmental protection is central to the United Nations Sustainable Development Goals (SDGs), improving the sorting and processing of everyday waste is a practical step toward this broader objective. In many real-world settings, however, waste is still sorted manually, which is slow, labor-intensive, and prone to human error. Although convolutional neural networks (CNNs) can automate this task with high accuracy, many state-of-the-art models remain too large and computationally demanding for low-cost edge devices intended for deployment in homes, schools, and small recycling facilities. In this work, we investigate lightweight waste-classification models suitable for TinyML deployment while preserving competitive accuracy. We first benchmark multiple CNN architectures to establish a strong baseline, then apply complementary compression strategies including quantization, pruning, singular value decomposition (SVD) low-rank approximation, and knowledge distillation. In addition, we evaluate an RL-guided multi-teacher selection benchmark that adaptively chooses one teacher per minibatch during distillation to improve student training stability, achieving up to 85% accuracy with only 0.496 M parameters (FP32 ≈ 1.89 MB; INT8 ≈ 0.47 MB). Across all experiments, the best accuracy–size trade-off is obtained by combining knowledge distillation with post-training quantization, reducing the model footprint from approximately 16 MB to 281 KB while maintaining 82% accuracy. The resulting model is feasible for deployment on mobile applications and resource-constrained embedded devices based on model size and TensorFlow Lite Micro compatibility. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

27 pages, 12369 KB

Open AccessArticle

Design and Validation of a Solar-Powered LoRa Weather Station for Environmental Monitoring and Agricultural Decision Support

by Uriel E. Alcalá-Rodríguez, Héctor A. Guerrero-Osuna, Fabián García-Vázquez, Jesús A. Nava-Pintor, Luis F. Luque-Vega, Emmanuel Lopez-Neri, Salvador Castro-Tapia, Luis O. Solís-Sánchez and Ma. del Rosario Martínez-Blanco

Technologies 2026, 14(1), 32; https://doi.org/10.3390/technologies14010032 - 5 Jan 2026

Viewed by 1415

Abstract

Due to changing weather conditions, productivity needs to be enhanced and resources must be used more efficiently in agriculture. Precision agriculture relies on systems that can gather real-time environmental data to address these issues. However, the high cost of commercial weather stations often limits their adoption in rural areas. This study introduces a low-cost weather station designed for precision agriculture applications. The system consists of three main modules. The first module is the weather station, which gathers data on temperature, relative humidity, barometric pressure, solar radiation, wind speed and direction, and precipitation. It then transmits this data via LoRa communication to the local console module. This console receives the data, displays it on a screen, and sends it through Wi-Fi to the cloud server module. The cloud server presents the information via an interactive interface and is responsible for storing, processing, and analyzing the data records collected. The system was installed in the municipality of Ojocaliente, Zacatecas, Mexico, where performance and validation tests were conducted over a one-month period using sensors and reference measurements to evaluate the accuracy and stability of the data. The results showed high operational reliability and a strong correlation between the recorded values and the reference data. This confirms that the proposed solution provides a scalable, low-cost, and reliable alternative for environmental monitoring in precision agriculture. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

26 pages, 8192 KB

Open AccessArticle

Enhancing Deep Learning Models with Attention Mechanisms for Interpretable Detection of Date Palm Diseases and Pests

by Amine El Hanafy, Abdelaaziz Hessane and Yousef Farhaoui

Technologies 2025, 13(12), 596; https://doi.org/10.3390/technologies13120596 - 18 Dec 2025

Cited by 1 | Viewed by 945

Abstract

Deep learning has become a powerful tool for diagnosing pests and plant diseases, although conventional convolutional neural networks (CNNs) generally suffer from limited interpretability and suboptimal focus on important image features. This study examines the integration of attention mechanisms into two prevalent CNN architectures—ResNet50 and MobileNetV2—to improve the interpretability and classification of diseases impacting date palm trees. Four attention modules—Squeeze-and-Excitation (SE), Efficient Channel Attention (ECA), Soft Attention, and the Convolutional Block Attention Module (CBAM)—were systematically integrated into ResNet50 and MobileNetV2 and assessed on the Palm Leaves dataset. Using transfer learning, the models were trained and evaluated through accuracy, F1-score, Grad-CAM visualizations, and quantitative metrics such as entropy and Attention Focus Scores. Analysis was also performed on the model’s complexity, including parameters and FLOPs. To confirm generalization, we tested the improved models on field data that was not part of the dataset used for learning. The experimental results demonstrated that the integration of attention mechanisms substantially improved both predictive accuracy and interpretability across all evaluated architectures. For MobileNetV2, the best performance and the most compact attention maps were obtained with SE and ECA (reaching 91%), while Soft Attention improved accuracy but produced broader, less concentrated activation patterns. For ResNet50, SE achieved the most focused and symptom-specific heatmaps, whereas CBAM reached the highest classification accuracy (up to 90.4%) but generated more spatially diffuse Grad-CAM activations. Overall, these findings demonstrate that attention-enhanced CNNs can provide accurate, interpretable, and robust detection of palm tree diseases and pests under real-world agricultural conditions. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

27 pages, 2171 KB

Open AccessArticle

Digital Maturity of SMEs in the EU: Leaders and Laggards of Luxembourg’s Manufacturing Ecosystem

by Marko Orošnjak, Slawomir Kedziora and Mickael Desloges

Technologies 2025, 13(12), 541; https://doi.org/10.3390/technologies13120541 - 21 Nov 2025

Viewed by 1692

Abstract

Digital maturity is increasingly recognised as a determinant of competitiveness for small and medium-sized enterprises (SMEs), yet empirical evidence from advanced economies remains limited. Here, we evaluate a sample of Luxembourgish manufacturing SMEs across six dimensions of the Digital Maturity Assessment Tool (DMAT)—Digital Business Strategy (DBS), Digital Readiness (DR), Human-Centric Digitalisation (HCD), Data Governance/Connectedness (DG), Automation and AI (AAI), and Green Digitalisation (GD)—to quantify their overall maturity. To avoid compositional artefacts, given that we rely on the EU’s DMAT, we introduce leave-one-out correlation (LOOC) to assess the association between DMA score and each focal dimension; within-firm disparities were tested via repeated-measures ANOVA; sample profiles were examined using Principal Component Analysis (PCA) followed by hierarchical clustering (HCPC). Respectively, the results converged across methods: HCD (r = 0.717) and DBS (r = 0.652) exhibited the strongest links to maturity, DG/AAI/GD were moderate contributors (r ≈ 0.50–0.58), and DR was weak (r = 0.298). The ANOVA analysis indicated substantial between-dimension differences (partial η² ≈ 0.41), with DG and DBS leading and AAI and GD lagging. PCA–HCPC revealed two coherent cluster profiles—Leaders and Laggards—arrayed along a general maturity axis, with the most significant gaps in DBS and HCD. Practically, firms that prioritise DBS and HCD exhibit a higher DMA score, which creates a foundation for industrialising and automatising manufacturing processes. Given the small, single-country, cross-sectional design, longitudinal and adequately powered studies with objective performance outcomes are warranted to validate and generalise these findings. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Graphical abstract

25 pages, 3263 KB

Open AccessArticle

Combining MTCNN and Enhanced FaceNet with Adaptive Feature Fusion for Robust Face Recognition

by Sasan Karamizadeh, Saman Shojae Chaeikar and Hamidreza Salarian

Technologies 2025, 13(10), 450; https://doi.org/10.3390/technologies13100450 - 3 Oct 2025

Cited by 2 | Viewed by 2798

Abstract

Face recognition systems typically face actual challenges like facial pose, illumination, occlusion, and ageing that significantly impact the recognition accuracy. In this paper, a robust face recognition system that uses Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and face alignment with an enhanced FaceNet for facial embedding extraction is presented. The enhanced FaceNet uses attention mechanisms to achieve more discriminative facial embeddings, especially in challenging scenarios. In addition, an Adaptive Feature Fusion module synthetically combines identity-specific embeddings with context information such as pose, lighting, and presence of masks, hence enhancing robustness and accuracy. Training takes place using the CelebA dataset, and the test is conducted independently on LFW and IJB-C to enable subject-disjoint evaluation. CelebA has over 200,000 faces of 10,177 individuals, LFW consists of 13,000+ faces of 5749 individuals in unconstrained conditions, and IJB-C has 31,000 faces and 117,000 video frames with extreme pose and occlusion changes. The system introduced here achieves 99.6% on CelebA, 94.2% on LFW, and 91.5% on IJB-C and outperforms baselines such as simple MTCNN-FaceNet, AFF-Net, and state-of-the-art models such as ArcFace, CosFace, and AdaCos. These findings demonstrate that the proposed framework generalizes effectively between datasets and is resilient in real-world scenarios. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Graphical abstract

Review

Jump to: Research, Other

32 pages, 1860 KB

Open AccessReview

Integrating Artificial Intelligence into Smart Infrastructure Management for Sustainable Urban Planning

by Abdulaziz I. Almulhim

Technologies 2025, 13(11), 481; https://doi.org/10.3390/technologies13110481 - 23 Oct 2025

Cited by 8 | Viewed by 3911

Abstract

This paper systematically reviewed studies on the integration of Artificial Intelligence (AI) into infrastructure management to support sustainable urban planning across three primary domains: predictive maintenance and energy optimization, traffic and mobility systems, and public participation with ethical considerations. Findings from thirty peer-reviewed studies underscore how AI-driven models enhance operational efficiency, sustainability, and governance in smart cities. Effective management of AI-driven smart infrastructure can transform urban planning by optimizing resources efficiency and predictive maintenance, including 15% energy savings, 25–30% cost reductions, 25% congestion reduction, and 18% decrease in travel times. Similarly, participatory digital twins and citizen-centric approaches are found to enhance public participation and help address ethical issues. The findings further reveal that AI-based predictive maintenance frameworks improve system reliability, while deep learning and hybrid models achieve up to 92% accuracy in traffic forecasting. Nonetheless, obstacles to equitable implementation, including the digital divide, privacy infringements, and algorithmic bias, persist. Establishing ethical and participatory frameworks, anchored in responsible AI governance, is therefore vital to promote transparency, accountability, and inclusivity. This study demonstrates that AI-enabled smart infrastructure management strengthens urban planning by enhancing efficiency, sustainability, and social responsiveness. It concludes that achieving sustainable and socially accepted smart cities depends on striking a balance between technological innovation, ethical responsibility, and inclusive governance. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

Other

Jump to: Research, Review

22 pages, 8021 KB

Open AccessSystematic Review

AI-Driven Digital Twins in Mining Operations: A Comprehensive Review

by Shouki A. Ebad, Aws I. Abueid, Marwa Amara and Rabie Ahmed

Technologies 2026, 14(5), 269; https://doi.org/10.3390/technologies14050269 - 29 Apr 2026

Viewed by 359

Abstract

The mining industry is going through a big digital change because of the use of new technologies that are meant to make work safer, more productive, and more environmentally friendly. AI-driven digital twins (AI-DTs) are one of these new ideas. They combine real-time data collection with smart analytics to make it possible for decisions to be made in a predictive, adaptive, and autonomous way. This paper provides a thorough systematic literature review (SLR) of AI-DT applications in mining operations, encompassing studies published from 2015 to 2025. According to the PRISMA method, 68 primary studies were chosen and looked at from many angles, such as publication trends, demographic analysis, research methods, data sources, mining domains, and the AI techniques that were used. The findings reveal an increasing scholarly interest in AI-DTs, characterized by a significant prevalence of machine learning and deep learning methodologies, alongside a preference for real-world sensory data to augment model accuracy. Most applications deal with physical assets, processing plants, and operational systems. Subsurface environments, on the other hand, are still not well understood. The review also points out some major problems with data integration, scalability, interoperability, and the fact that there has not been much large-scale industrial validation. Based on these findings, the paper points out important areas of research that need more work and suggests ways to move forward with the development and use of AI-DTs in mining. In conclusion, this study gives researchers and practitioners a clear plan for how to use AI-DTs to make mining operations more efficient, resilient, and long-lasting. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Figure 1

20 pages, 2763 KB

Open AccessSystematic Review

Sustainability Reporting: A Machine Learning Meta-Regression Analysis

by Hanvedes Daovisan

Technologies 2026, 14(1), 21; https://doi.org/10.3390/technologies14010021 - 29 Dec 2025

Cited by 1 | Viewed by 815

Abstract

The quality of sustainability reporting (SR) has come to be widely regarded as a factor of considerable importance in influencing organisational performance. However, empirical evidence in relation to SR has been characterised by fragmentation across industrial sectors. The purpose of this study was to synthesise the relationship between SR and organisational performance across the manufacturing, finance, energy and utilities, services, and ICT sectors. Our systematic review, performed using the PRISMA 2020 framework and machine learning meta-regression, was conducted on 372 studies retrieved from the Scopus database between 1 January 2020 and 1 November 2025. Our pooled correlation showed that the SR effect was positively associated with outcome performance (r = 0.231, 95% CI [0.184, 0.279]) and yielded a standardised mean difference (g = 0.426, 95% CI [0.341, 0.512]). The meta-regression showed that assurance quality (β = 0.156, p < 0.001), the regulatory regime (β = 0.142, p < 0.001), and reporting standard alignment (β = 0.118, p = 0.003) are significant moderating factors. The predictive robustness was confirmed through cross-validation (R² = 0.55; RMSE = 0.056), while feature stability was substantiated by a mean SHAP variance of less than 0.012. Transparency, comparability, and decision usefulness in SR were found to be enhanced by institutional mechanisms—particularly those providing credible assurance within mandatory regulatory frameworks. Full article

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

► Show Figures

Journal Menu

Journal Browser

Emerging Technologies and Intelligent Systems for Sustainable Development

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI