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Electronics
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IoT-Enabled Smart Devices and Systems in Smart Environments
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IoT-Enabled Smart Devices and Systems in Smart Environments

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 6624

Special Issue Editors

Dr. Anatolijs Zabasta

E-Mail Website
Guest Editor
Institute of Industrial Electronics and Electrical Engineering, Riga Technical University, LV-1048 Riga, Latvia
Interests: IoT; cyber-physical systems; critical infrastructure systems; intelligent manufacturing systems; application of wireless sensor networks; innovation management
Prof. Dr. Darius Plonis

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), Plytinės g. 25, LT-10105 Vilnius, Lithuania
Interests: neural networks; electronics engineering; machine learning; signal processing; tracking; embedded systems; beekeeping
Special Issues, Collections and Topics in MDPI journals
Dr. Andrius Katkevičius

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
Interests: microwave devices; artificial intelligence; embedded systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a computing paradigm in which everyday life objects are augmented with computational and wireless communication capabilities, typically by incorporating resource-constrained devices including sensors and actuators, which enable their connection to the internet. The IoT is considered the key ingredient for developing smart environments. IoT-enabled smart devices and systems constitute the key elements of divergent smart environments, e.g., smart homes, health, cities, and farming, and intelligent manufacturing systems.

In smart environments, sensors with intelligent computational capacities are placed in a location containing an internet connection. These sensors can communicate with anything, at any time, and from any place within the network. Data collection systems locate and transfer data through large numbers of such communication devices within the IoT infrastructure, facilitating the process of data collection.

The collection and exchange of data might be enabled through web-service-enabled devices deployed in the different systems. Therefore, such devices will be connected to physical and cyber resources, such as sensors, actuators, and data exchangers. The next generation of automation and digitalisation technology has to meet a large set of requirements and involve a wider scope of actors and stakeholders. This is the big challenge for technology suppliers of the future in this field.

The key technology for creating integration within and between different levels of the ISA-95 architecture (today’s standard architecture for automation systems) is Service-Oriented Architecture (SOA). The SOA-based enterprise architecture allows devices and systems, from the shop floor to the business level, to function in a cross-layer interaction mode.

This cloud-based approach allows a multi-level composition of systems-of-systems and services-of-services. The use of a cloud-based architecture enables tight integration and interaction between high-end business systems, e.g., ERP/MES, and factory floor systems, e.g., SCADA/DCS, thereby allowing management, control, monitoring, and other supervisory control functionalities to be performed as a result of the composition and service orchestration of a framework of intelligent manufacturing systems. Intelligent manufacturing systems are integrated and built on many existing technologies and components, such as industrial production systems, including industrial devices equipped with sensors and actuators; industrial IoT (IIoT) components; and backend systems, such as cloud platforms.

We invite you to submit unpublished original research related to the theme of this Special Issue: “IoT-Enabled Smart Devices and Systems in Smart Environments”.

Dr. Anatolijs Zabasta
Prof. Dr. Darius Plonis
Dr. Andrius Katkevičius
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. Electronics 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

  • IoT
  • IIoT
  • smart devices
  • smart home
  • smart health
  • smart cities
  • smart environments
  • systems of systems
  • services of services
  • SOA
  • intelligent manufacturing systems

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Published Papers (5 papers)

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Research

20 pages, 2128 KiB  
Article
Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults
by Meiling Chan, Ying Yu, Pohan Chang, Tsung-Yi Chen, Hok-Long Wong, Jian-Hua Huang, Wiping Zhang and Shih-Lun Chen
Electronics 2025, 14(1), 121; https://doi.org/10.3390/electronics14010121 - 30 Dec 2024
Viewed by 905
Abstract
With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are [...] Read more.
With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are often limited to basic parameters such as heart rate, pulse pressure (PP), and systolic blood pressure (SBP), which may not provide a comprehensive assessment of cardiac health. This study introduces an intelligent heart health monitoring system that leverages the Internet of Things (IoT) and advanced sensor technologies. By incorporating IoT-based sensors, this system aims to improve the early detection and continuous monitoring of cardiac function in young obese women. The research employed a TERUMO ES-P2000 to measure blood pressure and a PhysioFlow device to assess noninvasive cardiac hemodynamic parameters. Through precise sensor data collection, the study identified key indicators for monitoring cardiovascular health. Machine learning models and big data analysis were utilized to predict cardiac index (CI) values based on the sensor-derived inputs. The findings indicated that young obese women showed significant deviations in blood pressure (SBP and PP) and cardiac hemodynamic metrics (SVI, EDV, and ESV) at an early stage. The implementation of signal processing techniques and IoT sensors enhanced the CI prediction accuracy from 33% (using basic parameters like heart rate, PP, and SBP) to 66%. Moreover, the integration of extra sensor-based parameters, such as Stroke Volume Index (SVI) and Cardiac Output (CO), along with the use of color space transformations, successfully improved the prediction accuracy of the original data by 36.68%, increasing from 53.33% to 90.01%. This represents a significant improvement of 30.01% compared to the existing technology’s accuracy of 60%. These results underscore the importance of utilizing sensor-derived parameters as critical early indicators of cardiac function in young obese women. This research advances smart healthcare through early cardiovascular risk assessment using AI and noninvasive sensors. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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19 pages, 1074 KiB  
Article
A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors
by Dalius Matuzevičius
Electronics 2024, 13(23), 4763; https://doi.org/10.3390/electronics13234763 - 2 Dec 2024
Viewed by 1069
Abstract
This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. [...] Read more.
This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. There is a notable gap in systematic analyses of foundation models for specialized detection tasks, particularly within the domain of facial accessories. Six state-of-the-art models—Grounding DINO, Detic, OWLViT, OWLv2, YOLO World, and Florence-2—were evaluated across three datasets (FFHQ with custom annotations, CelebAMask-HQ, and Face Synthetics) to assess their effectiveness in zero-shot detection and labeling. Performance metrics, including Average Precision (AP), Average Recall (AR), and Intersection over Union (IoU), were used to benchmark foundation models. The results show that Detic achieved the highest performance scores (AP of 0.97 and AR of 0.98 on FFHQ, with IoU values reaching 0.97), making it highly suitable for automated annotation workflows. Grounding DINO and OWLv2 also showed potential, especially in high-recall scenarios. The results emphasize the importance of prompt engineering. Practical recommendations for using foundation models in specialized dataset annotation are provided. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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19 pages, 3074 KiB  
Article
Inner External DQN LoRa SF Allocation Scheme for Complex Environments
by Shengli Pang, Delin Kong, Xute Wang, Ruoyu Pan, Honggang Wang, Zhifan Ye and Di Liu
Electronics 2024, 13(14), 2761; https://doi.org/10.3390/electronics13142761 - 14 Jul 2024
Viewed by 918
Abstract
In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often [...] Read more.
In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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25 pages, 11635 KiB  
Article
IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks
by Mikhail Gorobetz, Jurijs Timofejevs, Andrejs Potapovs and Artjoms Obusevs
Electronics 2024, 13(12), 2357; https://doi.org/10.3390/electronics13122357 - 16 Jun 2024
Viewed by 1510
Abstract
This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the [...] Read more.
This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device’s ability to function under various adverse conditions. The results demonstrate the system’s capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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19 pages, 2793 KiB  
Article
Key-Point-Descriptor-Based Image Quality Evaluation in Photogrammetry Workflows
by Dalius Matuzevičius, Vytautas Urbanavičius, Darius Miniotas, Šarūnas Mikučionis, Raimond Laptik and Andrius Ušinskas
Electronics 2024, 13(11), 2112; https://doi.org/10.3390/electronics13112112 - 29 May 2024
Cited by 1 | Viewed by 1691
Abstract
Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process [...] Read more.
Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process by reducing data redundancy and computational demands. This study presents a novel approach to image quality evaluation tailored for photogrammetric applications that uses the key point descriptors typically encountered in image matching. Using a LightGBM ranker model, this research evaluates the effectiveness of key point descriptors such as SIFT, SURF, BRISK, ORB, KAZE, FREAK, and SuperPoint in predicting image quality. These descriptors are evaluated for their ability to indicate image quality based on the image patterns they capture. Experiments conducted on various publicly available image datasets show that descriptor-based methods outperform traditional no-reference image quality metrics such as BRISQUE, NIQE, PIQE, and BIQAA and a simple sharpness-based image quality evaluation method. The experimental results highlight the potential of using key-point-descriptor-based image quality evaluation methods to improve the photogrammetric workflow by selecting high-quality images for 3D modeling. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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