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28 pages, 1328 KiB  
Review
Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
by Dung T. Nguyen, Mien L. Trinh, Minh T. Nguyen, Thang C. Vu, Tao V. Nguyen, Long Q. Dinh and Mui D. Nguyen
Future Internet 2025, 17(8), 350; https://doi.org/10.3390/fi17080350 (registering DOI) - 1 Aug 2025
Abstract
In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to [...] Read more.
In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article
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40 pages, 1548 KiB  
Article
Real-Time Service Migration in Edge Networks: A Survey
by Yutong Zhang, Ke Zhao, Yihong Yang and Zhangbing Zhou
J. Sens. Actuator Netw. 2025, 14(4), 79; https://doi.org/10.3390/jsan14040079 (registering DOI) - 1 Aug 2025
Abstract
With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, [...] Read more.
With the rapid proliferation of Internet of Things (IoT) devices and mobile applications and the growing demand for low-latency services, edge computing has emerged as a transformative paradigm that brings computation and storage closer to end users. However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks. Full article
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37 pages, 6916 KiB  
Review
The Role of IoT in Enhancing Sports Analytics: A Bibliometric Perspective
by Yuvanshankar Azhagumurugan, Jawahar Sundaram, Zenith Dewamuni, Pritika, Yakub Sebastian and Bharanidharan Shanmugam
IoT 2025, 6(3), 43; https://doi.org/10.3390/iot6030043 (registering DOI) - 31 Jul 2025
Abstract
The use of Internet of Things (IoT) for sports innovation has transformed the way athletes train, compete, and recover in any sports activity. This study performs a bibliometric analysis to examine research trends, collaborations, and publications in the realm of IoT and Sports. [...] Read more.
The use of Internet of Things (IoT) for sports innovation has transformed the way athletes train, compete, and recover in any sports activity. This study performs a bibliometric analysis to examine research trends, collaborations, and publications in the realm of IoT and Sports. Our analysis included 780 Scopus articles and 150 WoS articles published during 2012–2025, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 3.6.1, VOSviewer version 1.6.20, and the bibliometrix library. The study provides insights from a bibliometric analysis, showcasing the allocation of topics, scientific contributions, patterns of co-authorship, prominent authors and their productivity over time, notable terms, key sources, publications with citations, analysis of citations, source-specific citation analysis, yearly publication patterns, and the distribution of research papers. The results indicate that China and India have the leading scientific production in the development of IoT and Sports research, with prominent authors like Anton Umek, Anton Kos, and Emiliano Schena making significant contributions. Wearable technology and wearable sensors are the most trending topics in IoT and Sports, followed by medical sciences and artificial intelligence paradigms. The analysis also emphasizes the importance of open-access journals like ‘Journal of Physics: Conference Series’ and ‘IEEE Access’ for their contributions to IoT and Sports research. Future research directions focus on enhancing effective, lightweight, and efficient wearable devices while implementing technologies like edge computing and lightweight AI in wearable technologies. Full article
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24 pages, 4753 KiB  
Article
A Secure Satellite Transmission Technique via Directional Variable Polarization Modulation with MP-WFRFT
by Zhiyu Hao, Zukun Lu, Xiangjun Li, Xiaoyu Zhao, Zongnan Li and Xiaohui Liu
Aerospace 2025, 12(8), 690; https://doi.org/10.3390/aerospace12080690 (registering DOI) - 31 Jul 2025
Abstract
Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both [...] Read more.
Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems. Full article
(This article belongs to the Section Astronautics & Space Science)
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40 pages, 18911 KiB  
Article
Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration
by Shohreh Kia, Johannes B. Mayer, Erik Westphal and Benjamin Leiding
Sensors 2025, 25(15), 4731; https://doi.org/10.3390/s25154731 (registering DOI) - 31 Jul 2025
Abstract
The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly [...] Read more.
The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
23 pages, 1830 KiB  
Article
Fuzzy Multi-Objective Optimization Model for Resilient Supply Chain Financing Based on Blockchain and IoT
by Hamed Nozari, Shereen Nassar and Agnieszka Szmelter-Jarosz
Digital 2025, 5(3), 32; https://doi.org/10.3390/digital5030032 (registering DOI) - 31 Jul 2025
Abstract
Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just [...] Read more.
Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article
(This article belongs to the Topic Sustainable Supply Chain Practices in A Digital Age)
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21 pages, 2909 KiB  
Article
Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks
by Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat and Bernardi Pranggono
Mathematics 2025, 13(15), 2471; https://doi.org/10.3390/math13152471 (registering DOI) - 31 Jul 2025
Abstract
Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT environments. In this paper, we propose FedGCL, a secure and efficient [...] Read more.
Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT environments. In this paper, we propose FedGCL, a secure and efficient FL framework integrating contrastive graph representation learning for enhanced feature discrimination, a Jain-index-based fairness-aware aggregation mechanism, an adaptive synchronization scheduler to optimize communication rounds, and secure aggregation via homomorphic encryption within a Trusted Execution Environment. We evaluate FedGCL on four benchmark malware datasets (Drebin, Malgenome, Kronodroid, and TUANDROMD) using 5 to 15 graph neural network clients over 20 communication rounds. Our experiments demonstrate that FedGCL achieves 96.3% global accuracy within three rounds and converges to 98.9% by round twenty—reducing required training rounds by 45% compared to FedAvg—while incurring only approximately 10% additional computational overhead. By preserving patient data privacy at the edge, FedGCL enhances system resilience without sacrificing model performance. These results indicate FedGCL’s promise as a secure, efficient, and fair federated malware detection solution for IoMT ecosystems. Full article
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26 pages, 5549 KiB  
Article
Intrusion Detection and Real-Time Adaptive Security in Medical IoT Using a Cyber-Physical System Design
by Faeiz Alserhani
Sensors 2025, 25(15), 4720; https://doi.org/10.3390/s25154720 (registering DOI) - 31 Jul 2025
Abstract
The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical [...] Read more.
The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article
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16 pages, 2174 KiB  
Article
TwinFedPot: Honeypot Intelligence Distillation into Digital Twin for Persistent Smart Traffic Security
by Yesin Sahraoui, Abdessalam Mohammed Hadjkouider, Chaker Abdelaziz Kerrache and Carlos T. Calafate
Sensors 2025, 25(15), 4725; https://doi.org/10.3390/s25154725 (registering DOI) - 31 Jul 2025
Abstract
The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we [...] Read more.
The integration of digital twins (DTs) with intelligent traffic systems (ITSs) holds strong potential for improving real-time management in smart cities. However, securing digital twins remains a significant challenge due to the dynamic and adversarial nature of cyber–physical environments. In this work, we propose TwinFedPot, an innovative digital twin-based security architecture that combines honeypot-driven data collection with Zero-Shot Learning (ZSL) for robust and adaptive cyber threat detection without requiring prior sampling. The framework leverages Inverse Federated Distillation (IFD) to train the DT server, where edge-deployed honeypots generate semantic predictions of anomalous behavior and upload soft logits instead of raw data. Unlike conventional federated approaches, TwinFedPot reverses the typical knowledge flow by distilling collective intelligence from the honeypots into a central teacher model hosted on the DT. This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. Experimental results demonstrate significant improvements in accuracy and F1-score, establishing TwinFedPot as a scalable and effective defense solution for smart traffic infrastructures. Full article
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26 pages, 3844 KiB  
Article
A No-Code Educational Platform for Introducing Internet of Things and Its Application to Agricultural Education
by George Lagogiannis and Avraam Chatzopoulos
IoT 2025, 6(3), 42; https://doi.org/10.3390/iot6030042 (registering DOI) - 31 Jul 2025
Abstract
This study introduces a no-code educational platform created to introduce Internet of Things (IoT) to university students who lack programming experience. The platform allows users to set IoT sensor nodes, and create a wireless sensor network through a simple graphical interface. Sensors’ data [...] Read more.
This study introduces a no-code educational platform created to introduce Internet of Things (IoT) to university students who lack programming experience. The platform allows users to set IoT sensor nodes, and create a wireless sensor network through a simple graphical interface. Sensors’ data can be sent to cloud services but they can also be stored locally, which makes our platform particularly realistic in fieldwork settings where internet access may be limited. The platform was tested in a pilot activity within a university course that previously covered IoT only in theory and was evaluated using the Technology Acceptance Model (TAM). Results showed strong student engagement and high ratings for ease of use, usefulness, and future use intent. These findings suggest that a no-code approach can effectively bridge the gap between IoT technologies and learners in non-engineering fields. Full article
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18 pages, 651 KiB  
Article
Enhancing IoT Connectivity in Suburban and Rural Terrains Through Optimized Propagation Models Using Convolutional Neural Networks
by George Papastergiou, Apostolos Xenakis, Costas Chaikalis, Dimitrios Kosmanos and Menelaos Panagiotis Papastergiou
IoT 2025, 6(3), 41; https://doi.org/10.3390/iot6030041 (registering DOI) - 31 Jul 2025
Abstract
The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment [...] Read more.
The widespread adoption of the Internet of Things (IoT) has driven major advancements in wireless communication, especially in rural and suburban areas where low population density and limited infrastructure pose significant challenges. Accurate Path Loss (PL) prediction is critical for the effective deployment and operation of Wireless Sensor Networks (WSNs) in such environments. This study explores the use of Convolutional Neural Networks (CNNs) for PL modeling, utilizing a comprehensive dataset collected in a smart campus setting that captures the influence of terrain and environmental variations. Several CNN architectures were evaluated based on different combinations of input features—such as distance, elevation, clutter height, and altitude—to assess their predictive accuracy. The findings reveal that CNN-based models outperform traditional propagation models (Free Space Path Loss (FSPL), Okumura–Hata, COST 231, Log-Distance), achieving lower error rates and more precise PL estimations. The best performing CNN configuration, using only distance and elevation, highlights the value of terrain-aware modeling. These results underscore the potential of deep learning techniques to enhance IoT connectivity in sparsely connected regions and support the development of more resilient communication infrastructures. Full article
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30 pages, 3898 KiB  
Article
Application of Information and Communication Technologies for Public Services Management in Smart Villages
by Ingrida Kazlauskienė and Vilma Atkočiūnienė
Businesses 2025, 5(3), 31; https://doi.org/10.3390/businesses5030031 (registering DOI) - 31 Jul 2025
Abstract
Information and communication technologies (ICTs) are becoming increasingly important for sustainable rural development through the smart village concept. This study aims to model ICT’s potential for public services management in European rural areas. It identifies ICT applications across rural service domains, analyzes how [...] Read more.
Information and communication technologies (ICTs) are becoming increasingly important for sustainable rural development through the smart village concept. This study aims to model ICT’s potential for public services management in European rural areas. It identifies ICT applications across rural service domains, analyzes how these technologies address specific rural challenges, and evaluates their benefits, implementation barriers, and future prospects for sustainable rural development. A qualitative content analysis method was applied using purposive sampling to analyze 79 peer-reviewed articles from EBSCO and Elsevier databases (2000–2024). A deductive approach employed predefined categories to systematically classify ICT applications across rural public service domains, with data coded according to technology scope, problems addressed, and implementation challenges. The analysis identified 15 ICT application domains (agriculture, healthcare, education, governance, energy, transport, etc.) and 42 key technology categories (Internet of Things, artificial intelligence, blockchain, cloud computing, digital platforms, mobile applications, etc.). These technologies address four fundamental rural challenges: limited service accessibility, inefficient resource management, demographic pressures, and social exclusion. This study provides the first comprehensive systematic categorization of ICT applications in smart villages, establishing a theoretical framework connecting technology deployment with sustainable development dimensions. Findings demonstrate that successful ICT implementation requires integrated urban–rural cooperation, community-centered approaches, and balanced attention to economic, social, and environmental sustainability. The research identifies persistent challenges, including inadequate infrastructure, limited digital competencies, and high implementation costs, providing actionable insights for policymakers and practitioners developing ICT-enabled rural development strategies. Full article
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18 pages, 8520 KiB  
Article
Cross-Layer Controller Tasking Scheme Using Deep Graph Learning for Edge-Controlled Industrial Internet of Things (IIoT)
by Abdullah Mohammed Alharthi, Fahad S. Altuwaijri, Mohammed Alsaadi, Mourad Elloumi and Ali A. M. Al-Kubati
Future Internet 2025, 17(8), 344; https://doi.org/10.3390/fi17080344 - 30 Jul 2025
Abstract
Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking [...] Read more.
Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment. Full article
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40 pages, 3463 KiB  
Review
Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications
by Sita Rani, Raman Kumar, B. S. Panda, Rajender Kumar, Nafaa Farhan Muften, Mayada Ahmed Abass and Jasmina Lozanović
Diagnostics 2025, 15(15), 1914; https://doi.org/10.3390/diagnostics15151914 - 30 Jul 2025
Abstract
Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, [...] Read more.
Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article
(This article belongs to the Special Issue Machine-Learning-Based Disease Diagnosis and Prediction)
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13 pages, 248 KiB  
Article
Fake News: Offensive or Defensive Weapon in Information Warfare
by Iuliu Moldovan, Norbert Dezso, Daniela Edith Ceană and Toader Septimiu Voidăzan
Soc. Sci. 2025, 14(8), 476; https://doi.org/10.3390/socsci14080476 - 30 Jul 2025
Abstract
Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred [...] Read more.
Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinformation, becomes increasingly blurred and difficult to identify. The purpose of this study is to describe this concept, to draw attention to one of the “pandemics” of the 21st-century world, and to find methods by which we can defend ourselves against them. Materials and methods. A cross-sectional study was conducted based on a sample of 442 respondents. Results. For 77.8% of the people surveyed, the concept of “fake news” is important in Romania. Regarding trust in the mass media, a clear dominance (72.4%) was observed among participants who have little trust in the mass media. Although 98.2% of participants detect false information found on the internet, 78.5% are occasionally deceived by the information provided. Of the participants, 47.3% acknowledged their vulnerability to disinformation. The main source of disinformation is the internet, as 59% of the interviewed subjects believed. As the best measure against disinformation, the study group was divided almost equally according to the three possible answers, all of which were considered to be equally important: imposing legal restrictions and blocking the posting of certain news (35.4%), imposing stricter measures for authors (33.9%), and increasing vigilance among people (30.5%). Conclusions. According to the statistics based on the participants’ responses, the main purposes of disinformation are propaganda, manipulation, distracting attention from the truth, making money, and misleading the population. It can be observed that the main intention of disinformation, in the perception of the study participants, is manipulation. Full article
(This article belongs to the Special Issue Disinformation and Misinformation in the New Media Landscape)
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