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Application of Artificial Intelligence in the Internet of Things

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 January 2027 | Viewed by 8496

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Editors

Prof. Dr. César A. Collazos

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Guest Editor

Facultad de Ingeniería Electrónica y Telecomunicaciones, Universidad del Cauca, Cl 5 #4, Popayán 190003, Cauca, Colombia
Interests: HCI; CSCW; CSCL; technology on education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Juan Calleros

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Guest Editor

Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla, Puebla 72410, Mexico
Interests: human-computer interaction; serious games; extended reality; cognitive tutoring systems

Prof. Dr. Huizilopoztli Luna García

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Guest Editor

Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico
Interests: human technology-interaction; infotainmnet ystems; data processing.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming different companies, cities, and daily life around the world, creating a new paradigm often referred to as the Artificial Intelligence of Things (AIoT). This Special Issue aims to explore the powerful synergies between AI and IoT, highlighting how AI enhances IoT systems’ intelligence, autonomy, and efficiency in a variety of applications, including smart cities, healthcare, manufacturing, agriculture, and more.

AI enables IoT systems to perform advanced data analysis, automate processes, and make decisions in real time. From deep learning for predictive maintenance and machine learning for optimizing energy consumption, to computer vision in autonomous vehicles, the possibilities are vast. However, the integration of AI into IoT comes with technical, ethical, and security challenges that must be addressed to unlock its full potential.

This Special Issue seeks original research articles, comprehensive reviews, and case studies on topics including, but not limited to, the following:

AI-driven data analytics and decision-making in IoT systems;
Machine learning algorithms for IoT sensor data processing;
Real-time AI applications in IoT environments;
AI and IoT in edge computing and fog computing architectures;
AI-based predictive maintenance and fault detection in IoT systems;
Security and privacy challenges of AIoT systems;
Ethical and social implications of AIoT;
AI-enhanced automation in smart homes, smart cities, healthcare, and industrial IoT;
Energy efficiency and sustainability in AI-powered IoT applications;
Human–AI–IoT interaction: interface design, validation and user experience.

This Special Issue will serve as a platform for researchers, practitioners, and industry experts to showcase the latest advancements, discuss the challenges, and share future directions for the integration of AI and IoT technologies. We welcome high-quality submissions that contribute to the development, implementation, and evaluation of AIoT solutions.

Prof. Dr. César A. Collazos
Prof. Dr. Juan M. González Calleros
Prof. Dr. Huizilopoztli Luna García
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-anonymized 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

Artificial Intelligence of Things
edge computing
machine learning in IoT
predictive analytics
IoT data security

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Published Papers (4 papers)

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Research

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19 pages, 1546 KB

Open AccessArticle

Deep Learning-Enhanced Proactive Strategy: LSTM and VRP/ACO for Autonomous Replenishment and Demand Forecasting in Shared Logistics

by Martin Straka and Kristína Kleinová

Appl. Sci. 2026, 16(6), 2838; https://doi.org/10.3390/app16062838 - 16 Mar 2026

Cited by 1 | Viewed by 634

Abstract

At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO₂ emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Internet of Things)

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16 pages, 10932 KB

Open AccessArticle

Spatial Modeling of PM_2.5 Concentrations Using Random Forest and Geostatistical Interpolation in Kraków, Poland

by Elżbieta Węglińska, Mateusz Zaręba and Tomasz Danek

Appl. Sci. 2026, 16(5), 2470; https://doi.org/10.3390/app16052470 - 4 Mar 2026

Viewed by 454

Abstract

Spatial mapping of PM_2.5 in complex urban and suburban terrains remains challenging for classical geostatistical interpolation. This study evaluates a Random Forest (RF) framework for high-resolution air pollution mapping and compares its performance with ordinary kriging in the Kraków region. The analysis integrates measurements from 51 low-cost air quality sensors with topographic and meteorological predictors, including elevation, temperature, relative humidity, and wind speed. Five representative hours during a relatively windless, inversion dominated day were selected to examine hourly variability in pollution patterns. Model robustness was assessed using leave-one-out (LOO) cross-validation, while interpretability was addressed through permutation-based predictor importance analysis. The RF model achieved high predictive accuracy (R² = 0.85 to 0.95) and good spatial stability with an LOO standard error below 5%. Elevation consistently emerged as the dominant predictor, confirming the key role of terrain-controlled accumulation, while temperature and humidity gained importance during evening and nighttime hours. The RF approach captured fine-scale transport features along river valleys that were not resolved by ordinary kriging, which produced smoother but less interpretable surfaces. The results demonstrate that RF mapping provides an accurate and explainable support to traditional geostatistical methods for analyzing urban air pollution dynamics in complex terrain. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Internet of Things)

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34 pages, 4668 KB

Open AccessArticle

A User-Centric Smart Library System: IoT-Driven Environmental Monitoring and ML-Based Optimization with Future Fog–Cloud Architecture

by Sarkan Mammadov and Enver Kucukkulahli

Appl. Sci. 2025, 15(7), 3792; https://doi.org/10.3390/app15073792 - 30 Mar 2025

Cited by 10 | Viewed by 4413

Abstract

University libraries are essential academic spaces, yet existing smart systems often overlook user perception in environmental optimization. A key challenge is the lack of adaptive frameworks balancing objective sensor data with subjective user experience. This study introduces an Internet of Things (IoT)-powered framework integrating real-time sensor data, image-based occupancy tracking, and user feedback to enhance study conditions via machine learning (ML). Unlike prior works, our system fuses objective measurements and subjective input for personalized assessment. Environmental factors—including air quality, sound, temperature, humidity, and lighting—were monitored using microcontrollers and image processing. User feedback was collected via surveys and incorporated into models trained using Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Extreme Gradient Boosting (XGBoost), and Naive Bayes. KNNs achieved the highest F1 score (99.04%), validating the hybrid approach. A user interface analyzes environmental factors, identifying primary contributors to suboptimal conditions. A scalable fog–cloud architecture distributes computation between edge devices (fog) and cloud servers, optimizing resource management. Beyond libraries, the framework extends to other smart workspaces. By integrating the IoT, ML, and user-driven optimization, this study presents an adaptive decision support system, transforming libraries into intelligent, user-responsive environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Internet of Things)

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Review

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33 pages, 2674 KB

Open AccessReview

Application of Artificial Intelligence in Environmental Analysis for Decision Making in Energy Efficiency in University Classrooms Monitored with IoT

by Ana Bustamante-Mora, Francisco Escobar-Jara, Jaime Díaz-Arancibia, Gabriel Mauricio Ramírez and Javier Medina-Gómez

Appl. Sci. 2026, 16(5), 2322; https://doi.org/10.3390/app16052322 - 27 Feb 2026

Viewed by 1528

Abstract

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in educational buildings represents an emerging opportunity to enhance intelligent environmental monitoring, data analysis, and energy optimization. This article presents a systematic literature review focused on AI-based applications in IoT-enabled learning environments, with special attention to indoor air quality (IAQ) management. A total of 585 documents were initially retrieved from Web of Science, Scopus, and IEEE Xplore using two targeted search strings. After removing duplicates and applying successive relevance filters based on title, abstract, and pertinence, 128 final documents were selected for full-text analysis. This study addresses four research questions: (RQ1) Which AI techniques are applied to environmental data analysis in educational contexts? (RQ2) What methods are used to detect sensor anomalies in IoT-based monitoring systems? (RQ3) How is AI applied in real-time decision making based on air quality indicators? (RQ4) What AI-driven strategies support energy efficiency in classrooms? The results reveal a growing use of machine learning and deep learning models, such as convolutional neural networks, decision trees, and LSTM architectures, particularly in applications focused on air quality classification, fault detection, and predictive control. Supervised learning methods were the most frequently applied, with CNN-based models leading in air quality prediction tasks and decision trees being preferred for anomaly detection. Deep learning approaches showed higher accuracy but required greater computational resources, limiting their use in low-cost educational environments. However, the literature also shows a lack of contextualized implementations, especially in low-resource or Latin American environments, and a limited focus on user-centered and educationally integrable systems. In addition, the review identifies a research gap regarding the integration of environmental and educational data, suggesting the potential for future empirical studies that evaluate real classroom conditions using IoT devices to inform AI-driven energy optimization strategies in academic settings. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the Internet of Things)

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