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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: closed (20 December 2025) | Viewed by 5484

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Special Issue Editors

Prof. Dr. Bijan Raahemi

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Guest Editor

Knowledge Discovery and Data Mining Lab, Telfer School of Management, and School of Electrical Engineering and Computer Science (cross-appointed), University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: artificial intelligence; machine learning; data mining; big data analytics; applications in business; healthcare; and engineering; information systems and technologies
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ahmad Akbari Azirani

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Guest Editor

Knowledge Discovery and Data Mining Lab, Telfer School of Management, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: machine learning; artificial intelligence communication and information technologies management of networks; IoT; network security

Special Issue Information

Dear Colleagues,

This Special Issue aims to publish papers on the latest advancements and the prevailing challenges within the realm of AI applications in IoT systems. Integration of AI with IoT systems enables us to collect, analyze, and act on data in real-time. Deploying advanced machine learning (ML) algorithms with the large amount of data collected by IoT devices enables advanced decision-making and automation. An AI application may analyze data from IoT devices, learn from it, and continuously refine its models. This continuous learning cycle makes the entire system smarter over time. However, AI/ML deployment in IoT systems is an open research problem due to several implications, including the resource limitations of IoT devices and security considerations.

Leveraging AI for data analysis, monitoring, and modelling in IoT systems is challenging. This is due not only to the limitation of IoT storage, processing power, and energy but also the real-time requirements of most intelligent IoT applications. Thus, novel solutions are studied to efficiently manage data to support the intelligent tasks of IoT systems. Moreover, there are new security challenges to protect data, considering machine learning complexities and limited resources for supporting data encryption.

Hence, the Special Issue invites submissions of recent research in the areas of artificial intelligence applications in the IoT, including, but not limited to, the followings:

AI/ML applications in IoT data analysis;
Application of deep learning in IOT;
Machine learning for industrial IoT;
Edge learning in IoT;
Deploying generative AI in IoT applications;
Security challenges of AI-empowered IoT applications;
Trustworthy AI for IoT applications;
Prototype design for AI applications in IoT;
AI applications in IoT for automotive and transportation applications;
AI applications in IoT for advanced manufacturing applications;
AI applications in IoT for smart cities;
AI applications in IoT for health care applications;
Climate change and AI applications in IoT;
Digital transformation and AI applications in IoT.

Prof. Dr. Bijan Raahemi
Prof. Dr. Ahmad Akbari Azirani
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. 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
Internet of Things
machine learning
automation
intelligent IoT
generative AI
edge learning

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Published Papers (5 papers)

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Research

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21 pages, 1543 KB

Open AccessArticle

Understanding Patient Adherence Through Sensor Data: An Integrated Approach to Chronic Disease Management

by David Díaz-Jiménez, José L. López Ruiz, Juan F. Gaitán-Guerrero and Macarena Espinilla Estévez

Appl. Sci. 2025, 15(24), 13226; https://doi.org/10.3390/app152413226 - 17 Dec 2025

Viewed by 111

Abstract

Treatment adherence in chronic diseases is addressed here as a measurable construct that can be formally defined and computed from heterogeneous IoT data streams. The central contribution of this work lies in establishing a mathematical formulation of adherence that integrates both explicit treatment-related activities and behavioural indicators derived from sensor observations. The methodology specifies how raw data from wearables, BLE beacons, and ambient devices can be transformed into clinically meaningful activities through fuzzy logic, enabling the representation of uncertainty, temporal variability, and partial evidence. This framework also accommodates activity labels generated by machine learning models, providing a mechanism to adapt their outputs—originally expressed as probabilistic or categorical predictions—into fuzzy memberships suitable for adherence computation. By unifying sensor-driven activity extraction and model-based activity recognition under a common fuzzy representation, the proposed formulation delivers a coherent pathway for calculating adherence across multiple dimensions and contexts, thereby supporting robust and interpretable evaluation of patient behaviour. By integrating these elements, the methodology provides a comprehensive and interpretable profile of adherence, moving from isolated measures to a unified characterisation of patient behaviour. The framework enables healthcare professionals and patients to better monitor progress, anticipate risks, and support long-term disease management. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the IoT)

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25 pages, 2096 KB

Open AccessArticle

A Fuzzy Multi-Objective Sustainable and Agile Supply Chain Model Based on Digital Twin and Internet of Things with Adaptive Learning Under Environmental Uncertainty

by Hamed Nozari, Agnieszka Szmelter-Jarosz and Dariusz Weiland

Appl. Sci. 2025, 15(19), 10399; https://doi.org/10.3390/app151910399 - 25 Sep 2025

Cited by 1 | Viewed by 831

Abstract

This paper presents an advanced, adaptive model for designing and optimizing agile and sustainable supply chains by integrating fuzzy multi-objective programming, Internet of Things (IoT), digital twin (DT) technologies, and reinforcement learning. Unlike conventional static models, the proposed framework utilizes real-time data and dynamically updates fuzzy parameters through a deep deterministic policy gradient (DDPG) algorithm. The model simultaneously addresses three conflicting objectives: minimizing cost, delivery time, and carbon emissions, while maximizing agility. To validate the model’s effectiveness, various optimization strategies including NSGA-II, MOPSO, and the Whale Optimization Algorithm are applied across small- to large-scale scenarios. Results demonstrate that the integration of IoT and DT, alongside adaptive learning, significantly improves decision accuracy, responsiveness, and sustainability. The model is particularly suited for high-volatility environments, offering decision-makers an intelligent, real-time support tool. Case study simulations further illustrate the model’s value in sectors such as urban logistics and humanitarian aid supply chains. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the IoT)

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36 pages, 4468 KB

Open AccessArticle

Apis mellifera Bee Verification with IoT and Graph Neural Network

by Apolinar Velarde Martínez, Gilberto González Rodríguez and Juan Carlos Estrada Cabral

Appl. Sci. 2025, 15(14), 7969; https://doi.org/10.3390/app15147969 - 17 Jul 2025

Cited by 2 | Viewed by 836

Abstract

Automatic recognition systems (ARS) have been proposed in scientific and technological research for the care and preservation of endangered species; these systems, consisting of Internet of Things (IoT) devices and object-recognition techniques with artificial intelligence (AI), have emerged as proposed solutions to detect and prevent parasite attacks on Apis mellifera bees. This article presents a pilot ARS for the recognition and analysis of honeybees at the hive entrance using IoT devices and automatic object-recognition techniques, for the early detection of the Varroa mite in test apiaries. Two object-recognition techniques, namely the k-Nearest Neighbor Algorithm (kNN) and Graph Neural Network (GNN), were evaluated with an image dataset of 600 images from a single beehive. The results of the experiments show the viability of using GNN in real environments. GNN has greater accuracy in bee recognition, but with greater processing time, while the kNN classifier requires fewer processing resources but has lower recognition accuracy. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the IoT)

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23 pages, 6493 KB

Open AccessArticle

Frailty Insights Detection System (FIDS)—A Comprehensive and Intuitive Dashboard Using Artificial Intelligence and Web Technologies

by Bogdan-Iulian Ciubotaru, Gabriel-Vasilică Sasu, Nicolae Goga, Andrei Vasilățeanu, Iuliana Marin, Ionel-Bujorel Păvăloiu and Claudiu Teodor Ion Gligore

Appl. Sci. 2024, 14(16), 7180; https://doi.org/10.3390/app14167180 - 15 Aug 2024

Cited by 5 | Viewed by 2014

Abstract

Frailty, known as a syndrome affecting the elderly, have a direct impact on both social well-being and body’s ability to function properly. Specific to geriatric healthcare, the early detection of frailty helps the specialists to mitigate risks of severe health outcomes. This article presents the development process of a system used to determine frailty-specific parameters, focusing on easy-to-use, non-intrusive nature and reliance on objectively measured parameters. The multitude of methodologies and metrics involved in frailty assessment emphasize the multidimensional aspects of this process and the lack of a common and widely accepted methodology as being the gold standard. After the research phase, the frailty-specific parameters considered are physical activity, energy expenditure, unintentional weight loss, and exhaustion, along with additional parameters like daily sedentary time, steps history, heart rate, and body mass index. The system architecture, artificial intelligence models, feature selection, and final prototype results are presented. The last section addresses the challenges, limitations, and future work related to the Frailty Insights Detection System (FIDS). Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the IoT)

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Review

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35 pages, 3811 KB

Open AccessReview

The Impact of Data Analytics Based on Internet of Things, Edge Computing, and Artificial Intelligence on Energy Efficiency in Smart Environment

by Izabela Rojek, Piotr Prokopowicz, Maciej Piechowiak, Piotr Kotlarz, Nataša Náprstková and Dariusz Mikołajewski

Appl. Sci. 2026, 16(1), 225; https://doi.org/10.3390/app16010225 (registering DOI) - 25 Dec 2025

Abstract

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the IoT)

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