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26 pages, 874 KiB

Open AccessArticle

Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care

by Francesco Nucci, Gabriele Papadia and Emiliano Fedeli

Appl. Sci. 2025, 15(8), 4456; https://doi.org/10.3390/app15084456 - 17 Apr 2025

Viewed by 174

Abstract

This paper addresses the critical challenge of optimal allocation and scheduling of Internet of Things (IoT) tracking devices for patient monitoring in healthcare facilities, where limited resources must be efficiently distributed to minimize cost and maximize care quality. We formulate this healthcare management [...] Read more.

This paper addresses the critical challenge of optimal allocation and scheduling of Internet of Things (IoT) tracking devices for patient monitoring in healthcare facilities, where limited resources must be efficiently distributed to minimize cost and maximize care quality. We formulate this healthcare management problem as a specialized variant of the Resource-Constrained Scheduling Problem that incorporates patient-specific factors such as duration of stay and priority. After establishing the computational complexity of the problem, we propose a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address the complex problem of balancing multiple objectives: cost minimization and quality of care maximization. Our approach offers a set of optimal trade-offs, enabling informed decision-making to select the best final solution. Computational experiments conducted on both simulated scenarios and real-world healthcare facility datasets demonstrate that our approach outperforms existing methods, achieving between 1.2 and 3.0 times more solutions than the state of the art. Moreover, in comparison to manual scheduling by medical center managers, our method achieves cost savings of up to 12% (with an average of 6.3%) and quality improvements of up to 20% (with an average of 10%) across the tested experiments. The proposed method scales effectively to realistic healthcare settings with varying numbers of patients and tracking devices, maintaining solution quality while keeping computational time within practical limits for daily operational use. Our findings contribute to both healthcare operations research and clinical practice by providing an efficient methodology for optimizing the use of limited monitoring resources while prioritizing patient safety. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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33 pages, 6514 KiB

Open AccessArticle

IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System

by Ali Aghazadeh Ardebili, Cristian Martella, Antonella Longo, Chiara Rucco, Federico Izzi and Antonio Ficarella

Appl. Sci. 2025, 15(4), 2092; https://doi.org/10.3390/app15042092 - 17 Feb 2025

Viewed by 695

Abstract

This study focuses on Digital Twin-integrated smart energy systems, which serve as an example of Next-Generation Critical Infrastructures (CI). The resilience of these systems is influenced by a variety of internal features and external interactions, all of which are subject to change following [...] Read more.

This study focuses on Digital Twin-integrated smart energy systems, which serve as an example of Next-Generation Critical Infrastructures (CI). The resilience of these systems is influenced by a variety of internal features and external interactions, all of which are subject to change following cyber-physical disturbances. This necessitates real-time resilience monitoring for CI during crises; however, a significant gap remains in resilience monitoring. To address this gap, this study leverages the role of Internet of Things (IoT) in monitoring complex systems to enhance resilience through critical indicators relevant to cyber-physical safety and security. The study empirically implements Resilience-Key Performance Indicators (R-KPIs) from the domain, including Functionality Loss, Minimum Performance, and Recovery Time Duration. The main goal is to examine real-time IoT-based resilience monitoring in a real-life context. A cyber-physical system equipped with IoT-driven Digital Twins, data-driven microservices, and a False Data Injection Attack (FDIA) scenario is simulated to assess the real-time resilience of this smart system. The results demonstrate that real-time resilience monitoring provides actionable insights into resilience performance based on the selected R-KPIs. These findings contribute to a systematic and reusable model for enhancing the resilience of IoT-enabled CI, advancing efforts to ensure service continuity and secure essential services for society. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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35 pages, 6005 KiB

Open AccessArticle

An Artificial Intelligence-Based Digital Twin Approach for Rejection Rate and Mechanical Property Improvement in an Investment Casting Plant

by Javier Nieves, David Garcia, Jorge Angulo-Pines, Fernando Santos and Pedro Pablo Rodriguez

Appl. Sci. 2025, 15(4), 2013; https://doi.org/10.3390/app15042013 - 14 Feb 2025

Viewed by 559

Abstract

The manufacturing process carried out in the investment casting industry suffers from problems similar to other production processes. In addition, the high requirements of the customers and the industries that require these parts mean that high quality standards must be met. If those [...] Read more.

The manufacturing process carried out in the investment casting industry suffers from problems similar to other production processes. In addition, the high requirements of the customers and the industries that require these parts mean that high quality standards must be met. If those requirements are not achieved, this leads to the rejection of the manufactured parts. Therefore, given the current technology revolution (i.e., Industry 4.0) and the possibilities offered by tools such as digital twins and artificial intelligence, it is possible to work on a generation of intelligent systems that can reduce and even avoid these problems. Therefore, this study proposes the creation of a digital twin based on artificial intelligence to work on proactively identifying problems before they happen and, if they are detected, launch an optimization process that offers corrective actions to solve them. More specifically, this work focuses on the analysis of the manufacturing process (definition, KPI extraction, capture, distribution, and visualization), the creation of a base system for the integral management of process optimization, and experiments developed for determining the best method for making predictions. Additionally, we propose a recommender system to (i) avoid the appearance of porosities and (ii) keep the elongation of the parts in the ranges desired by the customer. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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19 pages, 6598 KiB

Open AccessArticle

Cybersecurity Requirements for Industrial Machine Control Systems

by Leszek Kasprzyczak, Anna Manowska and Marek Dźwiarek

Appl. Sci. 2025, 15(3), 1267; https://doi.org/10.3390/app15031267 - 26 Jan 2025

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Abstract

The first part of this paper discusses the research context, taking a closer look at the development of Industry 4.0 and the growing importance of the IIoT, which entails new cybersecurity challenges. The issue of cyber threats and the need to increase the [...] Read more.

The first part of this paper discusses the research context, taking a closer look at the development of Industry 4.0 and the growing importance of the IIoT, which entails new cybersecurity challenges. The issue of cyber threats and the need to increase the level of protection in machine control systems, which are particularly vulnerable to attacks due to their connection to the network, is also presented. The Introduction concludes with a presentation of the article’s objective, which is to analyze the requirements of security levels (SLs) and the implementation of relevant international standards. The next section reviews the current research on cybersecurity in machine control systems. This section also points out the research gaps that the article aims to fill. The next section presents the risk assessment used to ensure safety during machine operations based on ISO 12100. The article describes safety functions implemented in machine control systems, including the SIL (safety integrity level) and PL (performance level) specifications. An important part of the article is the creation of a relationship between PL and SL, showing how the safety functions of systems are related to protection against cyber threats. The last part of the article gives a case study in the form of examples of machines and their control systems performing safety functions, which require various SLs depending on the PLs. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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22 pages, 3342 KiB

Open AccessArticle

Detection of Pipe Ruptures in Shipboard Firefighting Systems Using Machine Learning and Deep Learning Techniques

by Sara Ferreno-Gonzalez, Vicente Diaz-Casas, Marcos Miguez-Gonzalez and Carlos G. San-Gabino

Appl. Sci. 2025, 15(3), 1181; https://doi.org/10.3390/app15031181 - 24 Jan 2025

Viewed by 573

Abstract

In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Neural network [...] Read more.

In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Neural network models were developed to distinguish between normal operational states and anomalies, as well as to accurately locate pipe faults within the ship. Data were collected using real-world tests with FiFi system sensors, capturing both normal operations and simulated pipe ruptures, and were meticulously labeled to facilitate neural network training. Two neural network models were introduced: one for classifying system states (normal or anomalous) based on time-series pressure data, and another for identifying the location of anomalies related to pipe ruptures. Experimental results demonstrated the efficacy of these models in detecting and localizing pipe faults, with performance evaluated using mean squared error (MSE) across different network configurations. The successful implementation of these machine learning and deep learning algorithms highlights their potential for enhancing maritime safety and operational efficiency. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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20 pages, 742 KiB

Open AccessArticle

FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

by Jae Won Jang and Bong Jun Choi

Appl. Sci. 2024, 14(24), 12024; https://doi.org/10.3390/app142412024 - 23 Dec 2024

Viewed by 1294

Abstract

Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, especially in IoT scenarios where devices have varying capabilities and data heterogeneity exists among [...] Read more.

Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, especially in IoT scenarios where devices have varying capabilities and data heterogeneity exists among IoT clients, appropriate personalization methods are necessary. In this paper, this work aims to address this heterogeneity using a form of parameter decoupling known as representation learning. Representation learning divides deep learning models into ‘base’ and ‘head’ components. The base component, capturing common features across all clients, is shared with the server, while the head component, capturing unique features specific to individual clients, remains local. This work proposes a new representation learning-based approach, named FedSeq, that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods, which can benefit not only data heterogeneity but also class heterogeneity. FedSeq has two different layer scheduling approaches, namely forward (Vanilla) and backward (Anti), in the context of data and class heterogeneity among clients. Our experimental results show that FedSeq, when compared to existing personalized federated learning algorithms, achieves increased accuracy, especially under challenging conditions, while reducing computation costs. The study introduces a novel personalized federated learning approach that integrates sequential layer expansion and dynamic scheduling methods, demonstrating a 7.31% improvement in classification accuracy on the CIFAR-100 dataset and a 4.1% improvement on the Tiny-ImageNet dataset compared to existing methods, while also reducing computation costs by up to 15%. Furthermore, Anti Scheduling achieves a computational efficiency improvement of 3.91% compared to FedAvg and 3.06% compared to FedBABU, while Vanilla Scheduling achieves a significant efficiency improvement of 63.93% compared to FedAvg and 63.61% compared to FedBABU. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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28 pages, 15228 KiB

Open AccessArticle

A Scalable and User-Friendly Framework Integrating IoT and Digital Twins for Home Energy Management Systems

by Myrto Stogia, Vasilis Naserentin, Asimina Dimara, Orfeas Eleftheriou, Ioannis Tzitzios, Christoforos Papaioannou, Mariya Pantusheva, Alexios Papaioannou, George Spaias, Christos-Nikolaos Anagnostopoulos, Anders Logg and Stelios Krinidis

Appl. Sci. 2024, 14(24), 11834; https://doi.org/10.3390/app142411834 - 18 Dec 2024

Cited by 1 | Viewed by 1750

Abstract

The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin [...] Read more.

The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin technologies. These measures not only mitigate rising bills but also ensure optimized thermal comfort and sustainability in typical residential settings. This paper proposes an innovative framework to facilitate the adoption of energy-efficient practices in households by leveraging the integration of Internet of Things technologies with Digital Twins. It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems. This design significantly reduces deployment complexity, enhances scalability, and empowers users with real-time insights into energy consumption, indoor conditions, and actionable strategies for sustainable energy management. The results showcase that the proposed method significantly outperforms traditional approaches, achieving a 94% reduction in deployment time and a 98% decrease in memory usage through the use of standardized parametric models and plug-and-play IoT integration. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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Review

Jump to: Research

27 pages, 7189 KiB

Open AccessReview

Review of Monitoring and Control Systems Based on Internet of Things

by Dawid Witczak and Sabina Szymoniak

Appl. Sci. 2024, 14(19), 8943; https://doi.org/10.3390/app14198943 - 4 Oct 2024

Cited by 2 | Viewed by 7397

Abstract

The Internet of Things is currently one of the fastest-growing branches of computer science. The development of 5G wireless networks and modern data transmission protocols offers excellent opportunities for rapid development in this field. The article presents an overview of monitoring and control [...] Read more.

The Internet of Things is currently one of the fastest-growing branches of computer science. The development of 5G wireless networks and modern data transmission protocols offers excellent opportunities for rapid development in this field. The article presents an overview of monitoring and control systems based on the Internet of Things. The authors discuss various aspects of these systems, including their architecture, applications, and challenges. We focus on analyzing the latest achievements in this field, considering technological innovations and practical applications in various sectors. Also, we emphasize the importance of integrating data from multiple sources and developing data analysis algorithms to ensure the effectiveness and precision of IoT-based monitoring and control systems. The article provides a valuable overview of the current state of knowledge in this dynamic area, inspiring further research and technological development. It also includes case studies showing various IoT device applications and energy consumption management. Full article

(This article belongs to the Special Issue The Internet of Things (IoT) and Its Application in Monitoring)

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