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Application of Artificial Intelligence in the Internet of Things

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1017

Special Issue Editors


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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

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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 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

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

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Published Papers (1 paper)

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Research

34 pages, 4668 KiB  
Article
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
Viewed by 364
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 [...] Read more.
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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