applsci-logo

Journal Browser

Journal Browser

Advanced IoT/ICT Technologies in Smart Systems

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 August 2025) | Viewed by 17151

Special Issue Editor


E-Mail Website
Guest Editor
Biological and Chemical Engineering Department, Hongik University, Sejong 2639, Republic of Korea
Interests: remote sensing; imaging; image analysis; high-throughput analysis; toxicity assay; smart system; deep learning; machine learning; IoT; ICT; Arduino
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the Internet of Things (IoT) has established its position as one of the most important technologies. As inexpensive microcontrollers, costing only a few dollars, are being manufactured and sold, smart systems utilizing these low-power microcontrollers are increasingly employed in research and industry. Examples include agricultural climate environment monitoring, bio-healthcare devices, object recognition, and face recognition using built-in cameras and deep learning, wireless data transmission and reception devices, motors, lighting, water pumps, and robot control.

Traditionally, the development of smart systems required high expertise in hardware and software, mainly by professionals in electrical, electronics, and computer majors. However, with the advent of the Arduino platform in 2005, significant changes came to smart system development. Arduino microcontroller boards and numerous compatible components were developed and made available, along with user-friendly software development tools and environments, facilitating the creation of embedded programs needed for microcontrollers. Most importantly, various contributors to the Arduino ecosystem share circuit diagrams and source code online, making it easy to acquire the information necessary for smart system development.

Smart systems will be further developed and utilized in research and industry since they can not only automatically perform various tasks such as data collection, transmission, and control but can also communicate with center servers and other devices. For this Special Issue, we invite submissions that explore advanced research and recent progress in the development and application of Arduino-based smart systems across various research and industrial fields. In addition, comprehensive reviews and survey papers as well as experimental studies are also welcome. Topics of interest for this Special Issue include, but are not limited to, the following:

  • 3D printing;
  • Animal/human diagnostics;
  • Arduino development tools/software, Arduino programming, coding, and Arduino-based software;
  • Artificial intelligence (AI) and deep learning (DNN, YOLO, Transformer, CNN, R-CNN);
  • Assistive devices, healthcare devices, wearable devices, and alarm systems;
  • Automatic control (drone/robot/motor/valve/pump/light control) and automation (home, factory, laboratory);
  • Cloud, parallel computing, and edge computing;
  • Data acquisition, transmission, and encryption;
  • Drone/robot/motor/valve/pump/light control;
  • Global Positioning System (GPS);
  • Graphical user interface (GUI);
  • Imaging, imaging devices (ESP32-CAM, thermal/IR/multispectral camera), and image processing/analysis;
  • Microcontrollers (ATmega, ATtiny, UNO, Nano, ESP32, STM32, ESP8266, Raspberry PI);
  • Object detection and face recognition;
  • Recording and voice recognition/analysis;
  • Remote sensing, biosensors, and plant/crop health monitoring;
  • Smart cities, smart factories, smart homes, smart farms, and smart manufacturing;
  • Transportation and autonomous driving;
  • Wireless communication (WiFi, Bluetooth, LoRa, ZigBee, RFID).

Dr. Sang-Kyu Jung
Guest Editor

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

  • smart system
  • Arduino
  • microcontroller
  • IoT
  • ICT

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 (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

28 pages, 5269 KB  
Article
IoT-Based Off-Grid Solar Power Supply: Design, Implementation, and Case Study of Energy Consumption Control Using Forecasted Solar Irradiation
by Marijan Španer, Mitja Truntič and Darko Hercog
Appl. Sci. 2025, 15(22), 12018; https://doi.org/10.3390/app152212018 - 12 Nov 2025
Viewed by 801
Abstract
This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 [...] Read more.
This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

19 pages, 759 KB  
Article
EV Charging Load Prediction and Electricity–Carbon Joint Trading Model
by Chunjie Li, Yimin Zhou and Jun Li
Appl. Sci. 2025, 15(21), 11662; https://doi.org/10.3390/app152111662 - 31 Oct 2025
Viewed by 305
Abstract
With the large-scale integration of electric vehicles (EVs) into the power grids, the disorderly charging of EVs may cause local overload and exacerbate peak-valley load difference. The current pricing strategies primarily focus on the supply side while neglecting user urgent charging demands and [...] Read more.
With the large-scale integration of electric vehicles (EVs) into the power grids, the disorderly charging of EVs may cause local overload and exacerbate peak-valley load difference. The current pricing strategies primarily focus on the supply side while neglecting user urgent charging demands and the impact of carbon trades; hence, an electricity–carbon joint pricing strategy is proposed in this paper. The strategy includes the selection of optimal charging modes based on the charging demand emergency, charging service satisfaction indicators, as well as the establishment of an electricity–carbon joint trading framework. A Stackelberg game model is further developed between the charging stations (CS) and EV users, which is solved under Karush–Kuhn–Tucker (KKT) conditions and duality theory. Simulation experiments have been performed, and the results demonstrate that this strategy can smooth the grid supply and reduce the CS operational costs via the increased carbon revenue while simultaneously satisfying EV users’ emergency power demands. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

17 pages, 2412 KB  
Article
A Gamified AI-Driven System for Depression Monitoring and Management
by Sanaz Zamani, Adnan Rostami, Minh Nguyen, Roopak Sinha and Samaneh Madanian
Appl. Sci. 2025, 15(13), 7088; https://doi.org/10.3390/app15137088 - 24 Jun 2025
Viewed by 1942
Abstract
Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This [...] Read more.
Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

Review

Jump to: Research, Other

21 pages, 1241 KB  
Review
Fuzzy Neural Networks—A Review with Case Study
by Lukasz Apiecionek
Appl. Sci. 2025, 15(13), 6980; https://doi.org/10.3390/app15136980 - 20 Jun 2025
Cited by 4 | Viewed by 4213
Abstract
This publication focuses on the use of fuzzy neural networks for data prediction. The author reviews papers in which fuzzy neural networks were used. The papers were selected mainly from 2020 to 2025 and were selected if fuzzy neural network were used for [...] Read more.
This publication focuses on the use of fuzzy neural networks for data prediction. The author reviews papers in which fuzzy neural networks were used. The papers were selected mainly from 2020 to 2025 and were selected if fuzzy neural network were used for practical applications. Also, some chosen networks are described: FALCON, ANFIS, and a fuzzy network with Ordered Fuzzy Numbers. The networks with the implementation code presented in other publications were tested and compared to K Neighbors Classifier, Decision Tree Classifier, and Random Forest Classifier. The methodology and configuration of the networks are provided. Finally, the conclusions discuss limitations, future research prospects, and guidelines for future work. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

39 pages, 4490 KB  
Review
The Internet of Things Empowering the Internet of Pets—An Outlook from the Academic and Scientific Experience
by Pablo Pico-Valencia and Juan A. Holgado-Terriza
Appl. Sci. 2025, 15(4), 1722; https://doi.org/10.3390/app15041722 - 8 Feb 2025
Cited by 1 | Viewed by 7785
Abstract
This paper presents a systematic review to explore how the Internet of Things (IoT) is empowering the Internet of Pets (IoP) to enhance the quality of life for companion animals. Thirty-six relevant papers published between 2010 and 2024 were retrieved and analyzed following [...] Read more.
This paper presents a systematic review to explore how the Internet of Things (IoT) is empowering the Internet of Pets (IoP) to enhance the quality of life for companion animals. Thirty-six relevant papers published between 2010 and 2024 were retrieved and analyzed following both the PRISMA and the Kitchenham and Charters guidelines for conducting literature reviews. The findings demonstrate that the IoP is transforming pet care by offering innovative solutions for monitoring, feeding, and animal welfare. Asian countries are leading the development of these technologies, with a surge in research activity in recent years (2020–2024). While remote feeding prototypes currently dominate the field (79%), the IoP is anticipated to expand into other areas. Monitoring health (25%), surveillance and monitoring activities (49%), and providing comfort (17%) for pets are the primary research interests. The IoT holds immense potential to improve pet care. Research in this area is expected to continue growing, driving innovation and the creation of new IoP solutions utilizing artificial intelligence to achieve smart and predictive devices. In the future, the development of multifunctional devices that combine various capabilities in a single unit will become commonplace in a society where it is trending for young people to adopt pets instead of having children. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

Other

Jump to: Research, Review

19 pages, 646 KB  
Systematic Review
A Structured Review of IoT-Based Embedded Systems and Machine Learning for Water Quality Monitoring
by Eduardo C. Vicente, Luis Augusto Silva, Anita M. da Rocha Fernandes and Wemerson D. Parreira
Appl. Sci. 2025, 15(21), 11719; https://doi.org/10.3390/app152111719 - 3 Nov 2025
Viewed by 931
Abstract
This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these [...] Read more.
This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management. Full article
(This article belongs to the Special Issue Advanced IoT/ICT Technologies in Smart Systems)
Show Figures

Figure 1

Back to TopTop