Future Internet

22 pages, 2456 KiB

Open AccessArticle

FedRecI2C: A Novel Federated Recommendation Framework Integrating Communication and Computation to Accelerate Convergence Under Communication Constraints

by Qizhong Zheng and Xiujie Huang

Future Internet 2025, 17(3), 132; https://doi.org/10.3390/fi17030132 - 20 Mar 2025

Viewed by 234

Abstract

The federated recommender system (FRS) employs federated learning methodologies to create a recommendation model in a distributed environment, where clients share locally updated data with the server without exposing raw data and achieving privacy preservation. However, varying communication capabilities among devices restrict the [...] Read more.

The federated recommender system (FRS) employs federated learning methodologies to create a recommendation model in a distributed environment, where clients share locally updated data with the server without exposing raw data and achieving privacy preservation. However, varying communication capabilities among devices restrict the participation of only a subset of clients in each round of federated training, resulting in slower convergence and requiring additional training rounds. In this work, we propose a novel federated recommendation framework, called FedRecI2C, which integrates communication and computation resources in the system. This framework accelerates convergence by utilizing not only communication-capable clients for federated training but also communication-constrained clients to leverage their computation and limited communication resources for further local training. This framework offers simplicity and flexibility, providing a plug-and-play architecture that effectively enhances the convergence speed in FRSs. It has demonstrated remarkable effectiveness in a wide range of FRSs when operating under diverse communication conditions. Extensive experiments are conducted to validate the effectiveness of FedRecI2C. Moreover, we provide in-depth analyses of the FedRecI2C framework, offering novel insights into the training patterns of FRSs. Full article

(This article belongs to the Topic Internet of Things Architectures, Applications, and Strategies: Emerging Paradigms, Technologies, and Advancing AI Integration)

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21 pages, 3321 KiB

Open AccessArticle

A Distributed Machine Learning-Based Scheme for Real-Time Highway Traffic Flow Prediction in Internet of Vehicles

by Hani Alnami, Imad Mahgoub, Hamzah Al-Najada and Easa Alalwany

Future Internet 2025, 17(3), 131; https://doi.org/10.3390/fi17030131 - 19 Mar 2025

Viewed by 315

Abstract

Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the [...] Read more.

Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the sheer volume of traffic data that needs to be processed in real-time. Thus, it is not scalable and lacks fault tolerance and data privacy. This study designs and evaluates a scalable distributed machine learning-based scheme to predict highway traffic flows in real-time. The proposed system is segment-based where the vehicles in each segment form a cluster. We train and validate a local Random Forest Regression (RFR) model for each vehicle’s cluster (highway-segment) using six different hyper parameters. Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. Kappa Architecture is utilized to enable real-time prediction. The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Adjusted R-Squared (AR²). The proposed scheme demonstrates high accuracy in predicting abnormal traffic flows while maintaining scalability and data privacy. Full article

(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)

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15 pages, 2967 KiB

Open AccessArticle

Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning

by Mohammad Munzurul Islam and Mohammed Alawad

Future Internet 2025, 17(3), 130; https://doi.org/10.3390/fi17030130 - 19 Mar 2025

Viewed by 329

Abstract

In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity presents significant challenges in developing a highly accurate deep learning (DL) global [...] Read more.

In real-world scenarios, ECG data are collected from a diverse range of heterogeneous devices, including high-end medical equipment and consumer-grade wearable devices, each with varying computational capabilities and constraints. This heterogeneity presents significant challenges in developing a highly accurate deep learning (DL) global model for ECG classification, as traditional centralized approaches struggle to address privacy concerns, scalability issues, and model inconsistencies arising from diverse device characteristics. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without sharing raw data, thus preserving privacy and security. However, standard FL assumes uniform device capabilities and model architectures, which is impractical given the varied nature of ECG data collection devices. Although heterogeneity has been explored in other domains, its impact on ECG classification and the classification of similar time series physiological signals remains underexplored. In this study, we adopted HeteroFL, a technique that enables model heterogeneity to reflect real-world resource constraints. By allowing local models to vary in complexity while aggregating their updates, HeteroFL accommodates the computational diversity of different devices. This study evaluated the applicability of HeteroFL for ECG classification using the MIT-BIH Arrhythmia dataset, identifying both its strengths and limitations. Our findings establish a foundation for future research on improving FL strategies for heterogeneous medical data, highlighting areas for further optimization and adaptation in real-world deployments. Full article

(This article belongs to the Special Issue Distributed Machine Learning and Federated Edge Computing for IoT)

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50 pages, 566 KiB

Open AccessReview

Health Misinformation in Social Networks: A Survey of Information Technology Approaches

by Vasiliki Papanikou, Panagiotis Papadakos, Theodora Karamanidou, Thanos G. Stavropoulos, Evaggelia Pitoura and Panayiotis Tsaparas

Future Internet 2025, 17(3), 129; https://doi.org/10.3390/fi17030129 - 15 Mar 2025

Viewed by 536

Abstract

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this [...] Read more.

In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Research on misinformation spans multiple disciplines, but technical surveys rarely focus on the medical domain. Existing medical misinformation surveys provide broad insights for various stakeholders but lack a deep dive into computational methods. This survey fills that gap by examining how fact-checking and fake news detection techniques are adapted to the medical field from a computer engineering perspective. Specifically, we first present manual and automatic approaches for fact-checking, along with publicly available fact-checking tools. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation. While this survey primarily serves researchers and technology experts, it can also provide valuable insights for policymakers working to combat health misinformation. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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16 pages, 1856 KiB

Open AccessArticle

GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning

by Yulin Kang, Wuzheng Tan, Linlin Fan, Yinuo Chen, Xinbin Lai and Jian Weng

Future Internet 2025, 17(3), 128; https://doi.org/10.3390/fi17030128 - 15 Mar 2025

Viewed by 382

Abstract

Federated learning is a powerful tool for securing participants’ private data due to its ability to make data “available but not visible”. In recent years, federated learning has been enhanced by the emergence of multi-weight aggregation protocols, which minimize the impact of erroneous [...] Read more.

Federated learning is a powerful tool for securing participants’ private data due to its ability to make data “available but not visible”. In recent years, federated learning has been enhanced by the emergence of multi-weight aggregation protocols, which minimize the impact of erroneous parameters, and verifiable protocols, which prevent server misbehavior. However, it still faces significant security and performance challenges. Malicious participants may infer the private data of others or carry out poisoning attacks to compromise the model’s correctness. Similarly, malicious servers may return incorrect aggregation results, undermining the model’s convergence. Furthermore, substantial communication overhead caused by interactions between participants or between participants and servers hinders the development of federated learning. In response to this, this paper proposes GHEFL, a group-based, verifiable, federated learning method based on homomorphic encryption that aims to prevent servers from maliciously stealing participant privacy data or performing malicious aggregation. While ensuring the usability of the aggregated model, it strives to minimize the workload on the server as much as possible. Finally, we experimentally evaluate the performance of GHEFL. Full article

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15 pages, 4129 KiB

Open AccessArticle

Deep Neural Network-Based Modeling of Multimodal Human–Computer Interaction in Aircraft Cockpits

by Li Wang, Heming Zhang and Changyuan Wang

Future Internet 2025, 17(3), 127; https://doi.org/10.3390/fi17030127 - 13 Mar 2025

Viewed by 557

Abstract

Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. To address the limitations of single-modal interaction methods, a multimodal interaction model based on gaze and EEG target selection is proposed using deep learning technology. This model consists of [...] Read more.

Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. To address the limitations of single-modal interaction methods, a multimodal interaction model based on gaze and EEG target selection is proposed using deep learning technology. This model consists of two parts: target classification and intention recognition. The target classification model based on long short-term memory networks is established and trained by combining the eye movement information of the operator. The intention recognition model based on transformers is constructed and trained by combining the operator’s EEG information. In the application scenario of the aircraft radar page system, the highest accuracy of the target classification model is 98%. The intention recognition rate obtained by training the 32-channel EEG information in the intention recognition model is 98.5%, which is higher than other compared models. In addition, we validated the model on a simulated flight platform, and the experimental results show that the proposed multimodal interaction framework outperforms the single gaze interaction in terms of performance. Full article

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22 pages, 601 KiB

Open AccessArticle

University Students’ Subjective Well-Being in Japan Between 2021 and 2023: Its Relationship with Social Media Use

by Shaoyu Ye and Kevin K. W. Ho

Future Internet 2025, 17(3), 126; https://doi.org/10.3390/fi17030126 - 12 Mar 2025

Viewed by 1072

Abstract

This study investigated whether young adults’ social media use and subjective well-being (SWB) changed during the COVID-19 pandemic. It examined the possible relationships between social media use, SWB, and personality traits. It included generalized trust, self-consciousness, friendship, and desire for self-presentation and admiration, [...] Read more.

This study investigated whether young adults’ social media use and subjective well-being (SWB) changed during the COVID-19 pandemic. It examined the possible relationships between social media use, SWB, and personality traits. It included generalized trust, self-consciousness, friendship, and desire for self-presentation and admiration, in relation to different patterns of social media use and genders. Data were collected from university students in Japan from 2021 to 2023 and were analyzed based on different social media use patterns. The conceptual model was based on the cognitive bias and social network mediation models. Data were analyzed using ANOVA and regression analyses. The findings revealed that, over time, young adults’ anxiety toward COVID-19 decreased, while their SWB improved and their social support increased. Depression tendencies showed a negative association, whereas social support was positively related to improvement of SWB for all three patterns of social media use. Furthermore, online communication skills had a positive relationship with improvements in students’ SWB in Patterns 1 (LINE + Twitter + Instagram) and 2 (LINE + Twitter + Instagram + TikTok). The self-indeterminate factor had a positive relationship with students’ SWB for all patterns in 2022 and 2023, and the praise acquisition factor had a positive relationship with improvements in students’ SWB in Patterns 1 and 2. These results suggest that young adults maintained their mental health through different social media usage patterns, considering their personality traits and social situations associated with COVID-19. Particularly, receiving social support, decreasing people’s depression tendencies, and displaying different aspects of the “self” online can improve SWB. This study elucidates the mental health situations of university students in Japan and will help public health authorities develop new support programs that help digital natives improve their mental health in the context of social environmental changes. Full article

(This article belongs to the Special Issue Information Communication Technologies and Social Media)

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30 pages, 24605 KiB

Open AccessArticle

Advanced Trajectory Analysis of NASA’s Juno Mission Using Unsupervised Machine Learning: Insights into Jupiter’s Orbital Dynamics

by Ashraf ALDabbas, Zaid Mustafa and Zoltan Gal

Future Internet 2025, 17(3), 125; https://doi.org/10.3390/fi17030125 - 11 Mar 2025

Viewed by 646

Abstract

NASA’s Juno mission, involving a pioneering spacecraft the size of a basketball court, has been instrumental in observing Jupiter’s atmosphere and surface from orbit since it reached the intended orbit. Over its first decade of operation, Juno has provided unprecedented insights into the [...] Read more.

NASA’s Juno mission, involving a pioneering spacecraft the size of a basketball court, has been instrumental in observing Jupiter’s atmosphere and surface from orbit since it reached the intended orbit. Over its first decade of operation, Juno has provided unprecedented insights into the solar system’s origins through advanced remote sensing and technological innovations. This study focuses on change detection in terms of Juno’s trajectory, leveraging cutting-edge data computing techniques to analyze its orbital dynamics. Utilizing 3D position and velocity time series data from NASA, spanning 11 years and 5 months (August 2011 to January 2023), with 5.5 million samples at 1 min accuracy, we examine the spacecraft’s trajectory modifications. The instantaneous average acceleration, jerk, and snap are computed as approximations of the first, second, and third derivatives of velocity, respectively. The Hilbert transform is employed to visualize the spectral properties of Juno’s non-stationary 3D movement, enabling the detection of extreme events caused by varying forces. Two unsupervised machine learning algorithms, DBSCAN and OPTICS, are applied to cluster the sampling events in two 3D state spaces: (velocity, acceleration, jerk) and (acceleration, jerk, snap). Our results demonstrate that the OPTICS algorithm outperformed DBSCAN in terms of the outlier detection accuracy across all three operational phases (OP1, OP2, and OP3), achieving accuracies of 99.3%, 99.1%, and 98.9%, respectively. In contrast, DBSCAN yielded accuracies of 98.8%, 98.2%, and 97.4%. These findings highlight OPTICS as a more effective method for identifying outliers in elliptical orbit data, albeit with higher computational resource requirements and longer processing times. This study underscores the significance of advanced machine learning techniques in enhancing our understanding of complex orbital dynamics and their implications for planetary exploration. Full article

(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)

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27 pages, 4252 KiB

Open AccessArticle

Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms

by Andri Santoso, Samsul Huda, Yuta Kodera and Yasuyuki Nogami

Future Internet 2025, 17(3), 124; https://doi.org/10.3390/fi17030124 - 11 Mar 2025

Viewed by 599

Abstract

In the digital age, sharing moments through photos has become a daily habit. However, every face captured in these photos is vulnerable to unauthorized identification and potential misuse through AI-powered synthetic content generation. Previously, we introduced SnapSafe, a secure system for enabling selective [...] Read more.

In the digital age, sharing moments through photos has become a daily habit. However, every face captured in these photos is vulnerable to unauthorized identification and potential misuse through AI-powered synthetic content generation. Previously, we introduced SnapSafe, a secure system for enabling selective image privacy focusing on facial regions for single-party scenarios. Recognizing that group photos with multiple subjects are a more common scenario, we extend SnapSafe to support multi-user facial privacy protection with dynamic access control designed for online photo platforms. Our approach introduces key splitting for access control, an owner-centric permission system for granting and revoking access to facial regions, and a request-based mechanism allowing subjects to initiate access permissions. These features ensure that facial regions remain protected while maintaining the visibility of non-facial content for general viewing. To ensure reproducibility and isolation, we implemented our solution using Docker containers. Our experimental assessment covered diverse scenarios, categorized as “Single”, “Small”, “Medium”, and “Large”, based on the number of faces in the photos. The results demonstrate the system’s effectiveness across all test scenarios, consistently performing face encryption operations in under 350 ms and achieving average face decryption times below 286 ms across various group sizes. The key-splitting operations maintained a

100 %

success rate across all group configurations, while revocation operations were executed efficiently with server processing times remaining under 16 ms. These results validate the system’s capability in managing facial privacy while maintaining practical usability in online photo sharing contexts. Full article

(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—3rd Edition)

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20 pages, 1690 KiB

Open AccessArticle

How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry

by Yanxuan Li and Vatcharapol Sukhotu

Future Internet 2025, 17(3), 123; https://doi.org/10.3390/fi17030123 - 11 Mar 2025

Viewed by 687

Abstract

In recent years, the rapid advancement of digital technologies and the growing demand for sustainability have driven unprecedented transformations in the automotive industry, particularly toward electric vehicles (EVs) and renewable energy. The EV supply chain, a complex global network, has become increasingly vulnerable [...] Read more.

In recent years, the rapid advancement of digital technologies and the growing demand for sustainability have driven unprecedented transformations in the automotive industry, particularly toward electric vehicles (EVs) and renewable energy. The EV supply chain, a complex global network, has become increasingly vulnerable to globalization and frequent “black swan” events. The purpose of this study, grounded in organizational information processing theory, aims to systematically examine the role of digital capability in strengthening supply chain resilience (SCR) through improved risk management effectiveness. Specifically, it explores the multidimensional nature of digital capability, clarifies its distinct impact on SCR, and addresses existing research gaps in this domain. To achieve this, this study develops a theoretical framework and validates it using survey data collected from 249 EV supply chain enterprises in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) is employed to empirically test the proposed relationships. The findings provide valuable theoretical insights and actionable guidance for EV manufacturers seeking to leverage digital transformation to mitigate risks effectively and enhance supply chain resilience. However, as the study focuses on Chinese EV supply chain enterprises, caution is needed when generalizing the findings to other regions. Future research could extend this investigation to different markets, such as to Europe and the United States, to explore potential variations. Full article

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20 pages, 1087 KiB

Open AccessReview

Enabling Tactile Internet via 6G: Application Characteristics, Requirements, and Design Considerations

by Bharat S. Chaudhari

Future Internet 2025, 17(3), 122; https://doi.org/10.3390/fi17030122 - 11 Mar 2025

Viewed by 870

Abstract

With the emergence of artificial intelligence and advancements in network technologies, the imminent arrival of 6G is not very far away. The 6G technology will introduce unique and innovative applications of the Tactile Internet in the near future. This paper highlights the evolution [...] Read more.

With the emergence of artificial intelligence and advancements in network technologies, the imminent arrival of 6G is not very far away. The 6G technology will introduce unique and innovative applications of the Tactile Internet in the near future. This paper highlights the evolution towards the Tactile Internet enabled by 6G technology, along with the details of 6G capabilities. It emphasizes the stringent requirements for emerging Tactile Internet applications and the critical role of parameters, such as latency, reliability, data rate, and others. The study identifies the important characteristics of future Tactile Internet applications, interprets them into explicit requirements, and then discusses the associated design considerations. The study focuses on the role of application characteristics of various applications, like virtual reality/augmented reality, remote surgery, gaming, smart cities, autonomous vehicles, industrial automation, brain–machine interface, telepresence/holography, and requirements in the design of 6G and the Tactile Internet. Furthermore, we discuss the exclusive parameters and other requirements of Tactile Internet to realize real-time haptic interactions with the help of 6G and artificial intelligence. The study deliberates and examines the important performance parameters for the given applications. It also discusses various types of sensors that are required for Tactile Internet applications. Full article

(This article belongs to the Special Issue Advanced 5G and Beyond Networks)

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20 pages, 320 KiB

Open AccessArticle

CommC: A Multi-Purpose COMModity Hardware Cluster

by Agorakis Bompotas, Nikitas-Rigas Kalogeropoulos and Christos Makris

Future Internet 2025, 17(3), 121; https://doi.org/10.3390/fi17030121 - 11 Mar 2025

Viewed by 570

Abstract

The high costs of acquiring and maintaining high-performance computing (HPC) resources pose significant barriers for medium-sized enterprises and educational institutions, often forcing them to rely on expensive cloud-based solutions with recurring costs. This paper introduces CommC, a multi-purpose commodity hardware cluster designed to [...] Read more.

The high costs of acquiring and maintaining high-performance computing (HPC) resources pose significant barriers for medium-sized enterprises and educational institutions, often forcing them to rely on expensive cloud-based solutions with recurring costs. This paper introduces CommC, a multi-purpose commodity hardware cluster designed to reduce operational expenses and extend hardware lifespan by repurposing underutilized computing resources. By integrating virtualization (KVM and Proxmox) and containerization (Kubernetes and Docker), CommC creates a scalable, secure, and cost-efficient computing environment. The proposed system enables seamless resource sharing, ensuring high availability and fault tolerance for both containerized and virtualized workloads. To demonstrate its versatility, we deploy big data engines like Apache Spark alongside traditional web services, showcasing CommC’s ability to support diverse workloads efficiently. Our cost analysis reveals that CommC reduces computing expenses by up to 77.93% compared to cloud-based alternatives while also mitigating e-waste accumulation by extending the lifespan of existing hardware. This significantly improves environmental sustainability compared to cloud providers, where frequent hardware turnover contributes to rising carbon emissions. This research contributes to the fields of cloud computing, resource management, and sustainable IT infrastructure by providing a replicable, adaptable, and financially viable alternative to traditional cloud-based solutions. Future work will focus on automating resource allocation, enhancing real-time monitoring, and integrating advanced security mechanisms to further optimize performance and usability. Full article

(This article belongs to the Special Issue IoT–Edge–Cloud Computing and Decentralized Applications for Smart Cities)

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20 pages, 2207 KiB

Open AccessArticle

A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping

by Amanda Thomson, Leandros Maglaras and Naghmeh Moradpoor

Future Internet 2025, 17(3), 120; https://doi.org/10.3390/fi17030120 - 7 Mar 2025

Viewed by 797

Abstract

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and [...] Read more.

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article

(This article belongs to the Special Issue Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks)

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39 pages, 9925 KiB

Open AccessArticle

Dynamic Workload Management System in the Public Sector: A Comparative Analysis

by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou and Spyros Sioutas

Future Internet 2025, 17(3), 119; https://doi.org/10.3390/fi17030119 - 6 Mar 2025

Viewed by 673

Abstract

Efficient human resource management is critical to public sector performance, particularly in dynamic environments where traditional systems struggle to adapt to fluctuating workloads. The increasing complexity of public sector operations and the need for equitable task allocation highlight the limitations of conventional evaluation [...] Read more.

Efficient human resource management is critical to public sector performance, particularly in dynamic environments where traditional systems struggle to adapt to fluctuating workloads. The increasing complexity of public sector operations and the need for equitable task allocation highlight the limitations of conventional evaluation methods, which often fail to account for variations in employee performance and workload demands. This study addresses these challenges by optimizing load distribution through predicting employee capability using data-driven approaches, ensuring efficient resource utilization and enhanced productivity. Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. Performance is assessed through ten evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), ensuring a comprehensive assessment of accuracy, robustness, and bias. The results demonstrate ANFIS as the superior model, consistently outperforming other algorithms across all metrics. By synergizing fuzzy logic’s capacity to model uncertainty with neural networks’ adaptive learning, ANFIS effectively captures non-linear relationships and variations in employee performance, enabling precise capability predictions in dynamic environments. This research highlights the transformative potential of machine learning in public sector workforce management, underscoring the role of data-driven decision-making in improving task allocation, operational efficiency, and resource utilization. Full article

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41 pages, 603 KiB

Open AccessReview

Edge and Cloud Computing in Smart Cities

by Maria Trigka and Elias Dritsas

Future Internet 2025, 17(3), 118; https://doi.org/10.3390/fi17030118 - 6 Mar 2025

Cited by 2 | Viewed by 1833

Abstract

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. [...] Read more.

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. Cloud computing provides extensive computational capabilities and centralized data storage, whereas edge computing ensures localized processing to mitigate network congestion and latency. This survey presents an in-depth analysis of the integration of edge and cloud computing in smart cities, highlighting architectural frameworks, enabling technologies, application domains, and key research challenges. The study examines resource allocation strategies, real-time analytics, and security considerations, emphasizing the synergies and trade-offs between cloud and edge computing paradigms. The present survey also notes future directions that address critical challenges, paving the way for sustainable and intelligent urban development. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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25 pages, 818 KiB

Open AccessArticle

Emergency Messaging System for Urban Vehicular Networks Inspired by Social Insects’ Stigmergic Communication

by Ojilvie Avila-Cortés, Saúl E. Pomares Hernández, Julio César Pérez-Sansalvador and Lil María Xibai Rodríguez-Henríquez

Future Internet 2025, 17(3), 117; https://doi.org/10.3390/fi17030117 - 6 Mar 2025

Viewed by 431

Abstract

For occupant safety in vehicular networks, emergency messages derived from vehicular incidents should be exchanged only during their validity period and in zones containing involved entities. Problems arise for mobile entities in vehicular networks that change their location over time, where data may [...] Read more.

For occupant safety in vehicular networks, emergency messages derived from vehicular incidents should be exchanged only during their validity period and in zones containing involved entities. Problems arise for mobile entities in vehicular networks that change their location over time, where data may be further communicated in out-of-context space and time. Current solutions extend from the naive assumption of notifying every entity in the network about emergencies with data flooding and clusters and by means of specific communication only in the affected zones—geo-routing—of incidents’ relative data. However, delivering useless data to uninvolved entities results in wasted resources and more overheads in the former cases and the work of obtaining knowledge and secondary site services from neighbors in the latter. In this paper, we propose that the common task of disseminating emergency messages for occupant safety among entities should only be communicated only where and when useful, namely, if spatio-temporal constraints apply regarding those incidents. Our solution is inspired by the communication of working social insects that exchange data through pheromones regardless of closeness or knowledge among colony members for food retrieval. The results show that communication based on space–time constraints makes better use of resources than other solutions. Full article

(This article belongs to the Special Issue Intelligent Telecommunications Mobile Networks)

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19 pages, 1230 KiB

Open AccessArticle

A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios

by Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè

Future Internet 2025, 17(3), 116; https://doi.org/10.3390/fi17030116 - 6 Mar 2025

Viewed by 472

Abstract

Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar [...] Read more.

Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar measures to represent different dimensions of trust, which are then integrated into a single global trustworthiness value. Nevertheless, this scalar approach within the IoT context holds a few limitations that emphasize the need for models that can capture complex trust relationships beyond vector-based representations. To overcome these limitations, we already proposed a novel trust model where the trust perceived by one object with respect to another is represented by a directed, weighted graph. In this model, called T-pattern, the vertices represent individual trust dimensions, and the arcs capture the relationships between these dimensions. This model allows the IoT community to represent scenarios where an object may lack direct knowledge of a particular trust dimension, such as reliability, but can infer it from another dimension, like honesty. The proposed model can represent trust structures of the type described, where multiple trust dimensions are interdependent. This work represents a further contribution by presenting the first real implementation of the T-pattern model, where a neural-symbolic approach has been adopted as inference engine. We performed experiments that demonstrate the capability in inferring trust of both the T-pattern and this specific implementation. Full article

(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)

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36 pages, 21621 KiB

Open AccessArticle

CityBuildAR: Enhancing Community Engagement in Placemaking Through Mobile Augmented Reality

by Daneesha Ranasinghe, Nayomi Kankanamge, Chathura De Silva, Nuwani Kangana, Rifat Mahamood and Tan Yigitcanlar

Future Internet 2025, 17(3), 115; https://doi.org/10.3390/fi17030115 - 6 Mar 2025

Viewed by 1308

Abstract

Mostly, public places are planned and designed by professionals rather engaging the community in the design process. Even if the community engaged, the engagement process was limited to hand drawings, manual mappings, or public discussions, which limited the general public to visualize and [...] Read more.

Mostly, public places are planned and designed by professionals rather engaging the community in the design process. Even if the community engaged, the engagement process was limited to hand drawings, manual mappings, or public discussions, which limited the general public to visualize and well-communicate their aspirations with the professionals. Against this backdrop, this study intends to develop a mobile application called “CityBuildAR”, which uses Augmented Reality technology that allows the end user to visualize their public spaces in a way they want. CityBuildAR was developed by the authors using the Unity Real-Time Development Platform, and the app was developed for an Android Operating System. The app was used to assess community interests in designing open spaces by categorizing participants into three groups: those with limited, average, and professional knowledge of space design. The open cafeteria of the University of Moratuwa, Sri Lanka served as the testbed for this study. The study findings revealed that: (a) Mobile Augmented Reality is an effective way to engage people with limited knowledge in space design to express their design thinking, (b) Compared to professionals, the general public wanted to have more green elements in the public space; (c) Compared to the professionals, the general public who were not conversant with the designing skills found the app more useful to express their ideas. The study guides urban authorities in their placemaking efforts by introducing a novel approach to effectively capture community ideas for creating inclusive public spaces. Full article

(This article belongs to the Special Issue Virtual Reality and Metaverse: Impact on the Digital Transformation of Society II)

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20 pages, 1552 KiB

Open AccessArticle

SwiftSession: A Novel Incremental and Adaptive Approach to Rapid Traffic Classification by Leveraging Local Features

by Tieqi Xi, Qiuhua Zheng, Chuanhui Cheng, Ting Wu, Guojie Xie, Xuebiao Qian, Haochen Ye and Zhenyu Sun

Future Internet 2025, 17(3), 114; https://doi.org/10.3390/fi17030114 - 3 Mar 2025

Viewed by 558

Abstract

Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed [...] Read more.

Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed to achieve real-time and accurate classification. SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. An initial classification is conducted based on the first K packets during the classification process. If the prediction meets the predefined probability threshold, processing stops; otherwise, additional packets are received. This progressive approach dynamically adjusts the required packets, enhancing classification efficiency. Experimental results show that traffic can be effectively classified by using only the initial K packets. Moreover, on most datasets, the classification time is reduced by more than 70%. Unlike existing methods, SwiftSession enhances the classification speed while ensuring classification accuracy. Full article

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28 pages, 368 KiB

Open AccessArticle

A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models

by Nicholas Jones, Md Whaiduzzaman, Tony Jan, Amr Adel, Ammar Alazab and Afnan Alkreisat

Future Internet 2025, 17(3), 113; https://doi.org/10.3390/fi17030113 - 3 Mar 2025

Viewed by 1961

Abstract

The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based attacks exploit weaknesses in [...] Read more.

The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based attacks exploit weaknesses in LLMs to manipulate outputs, leading to breaches of confidentiality, corruption of integrity, and disruption of availability. Despite their significance, existing research lacks a comprehensive framework to systematically understand and mitigate these threats. This paper addresses this gap by introducing a taxonomy of prompt attacks based on the Confidentiality, Integrity, and Availability (CIA) triad, an important cornerstone of cybersecurity. This structured taxonomy lays the foundation for a unique framework of prompt security engineering, which is essential for identifying risks, understanding their mechanisms, and devising targeted security protocols. By bridging this critical knowledge gap, the present study provides actionable insights that can enhance the resilience of LLM to ensure their secure deployment in high-stakes and real-world environments. Full article

(This article belongs to the Special Issue Generative Artificial Intelligence (AI) for Cybersecurity)

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28 pages, 8659 KiB

Open AccessArticle

A Regional Multi-Agent Air Monitoring Platform

by Stanimir Stoyanov, Emil Doychev, Asya Stoyanova-Doycheva, Veneta Tabakova-Komsalova, Ivan Stoyanov and Iliya Nedelchev

Future Internet 2025, 17(3), 112; https://doi.org/10.3390/fi17030112 - 3 Mar 2025

Viewed by 637

Abstract

Plovdiv faces significant air pollution challenges due to geographic, climatic, and industrial factors, making accurate air quality assessment critical. This study presents a hybrid multi-agent platform that integrates symbolic and sub-symbolic artificial intelligence to improve the reliability of air quality monitoring. The platform [...] Read more.

Plovdiv faces significant air pollution challenges due to geographic, climatic, and industrial factors, making accurate air quality assessment critical. This study presents a hybrid multi-agent platform that integrates symbolic and sub-symbolic artificial intelligence to improve the reliability of air quality monitoring. The platform features a BDI agent, developed using JaCaMo, for processing real-time sensor measurements and a ReAct agent, implemented with LangChain, to incorporate external data sources and perform advanced analytics. By combining these AI approaches, the platform enhances data integration, detects anomalies, and resolves discrepancies between conflicting air quality reports. Furthermore, its scalable and adaptable architecture lays the foundation for future advancements in environmental monitoring. This research represents the first stage in developing an AI-powered system that supports more objective and data-driven decision-making for air quality management in Plovdiv. Full article

(This article belongs to the Special Issue Intelligent Agents and Their Application)

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18 pages, 474 KiB

Open AccessArticle

Frame Aggregation with Simple Block Acknowledgement Mechanism to Provide Strict Quality of Service Guarantee to Emergency Traffic in Wireless Networks

by Shuaib K. Memon, Md Akbar Hossain and Nurul I. Sarkar

Future Internet 2025, 17(3), 111; https://doi.org/10.3390/fi17030111 - 3 Mar 2025

Viewed by 685

Abstract

This paper proposes a frame aggregation with a simple block acknowledgement (FASBA) mechanism to provide a strict QoS guarantee to life-saving emergency traffic in wireless local area networks. This work builds on our previous work on a multi-preemptive enhanced distributed channel access protocol [...] Read more.

This paper proposes a frame aggregation with a simple block acknowledgement (FASBA) mechanism to provide a strict QoS guarantee to life-saving emergency traffic in wireless local area networks. This work builds on our previous work on a multi-preemptive enhanced distributed channel access protocol called MP-EDCA. The main difference between FASBA and MP-EDCA is that MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g., ambulance calls), especially in high-load conditions. Our proposed FASBA protocol solves the problems of achieving a strict QoS guarantee to life-saving emergency traffic. The strict QoS guarantee is achieved by aggregating multiple frames with a two-bit block acknowledgement for transmissions. FASBA assures guaranteed network services by reducing MAC overheads; consequently, it offers higher throughput, lower packet delays, and accommodates a larger number of life-saving emergency nodes during emergencies. The performance of the proposed FASBA is validated by Riverbed Modeler and MATLAB 2024a-based simulation. Results obtained show that the proposed FASBA offers about 30% lower delays, 17% higher throughput, and 60% lower retransmission attempts than MP-EDCA under high-traffic loads. Full article

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24 pages, 9612 KiB

Open AccessArticle

Developing an Urban Digital Twin for Environmental and Risk Assessment: A Case Study on Public Lighting and Hydrogeological Risk

by Vincenzo Barrile, Emanuela Genovese, Clemente Maesano, Sonia Calluso and Maurizio Pasquale Manti

Future Internet 2025, 17(3), 110; https://doi.org/10.3390/fi17030110 - 1 Mar 2025

Viewed by 827

Abstract

Improvements in immersive technology are opening up new opportunities for land management and urban planning, enabling the creation of detailed virtual models for examining and simulating real-world short-, medium-, and long-term scenarios. The goal of this research is to present the creation of [...] Read more.

Improvements in immersive technology are opening up new opportunities for land management and urban planning, enabling the creation of detailed virtual models for examining and simulating real-world short-, medium-, and long-term scenarios. The goal of this research is to present the creation of an urban digital twin based on a virtual reality city replica, that models and visualizes the urban environment in three dimensions using advanced geomatics techniques and IoT technologies. The methodology focuses on two case studies that utilize environmental analysis and virtual simulation: assessing hydrogeological risk and evaluating public light pollution. The Cesium platform was employed to build high-precision 3D models based on topographic, meteorological, and infrastructure data. The proposed methodology calculated a correlation between light pollution and CO₂ equal to 0.51 and a correlation between precipitation, slope, and risk area higher than 0.80. The most critical and high-risk classes are as follows: Dense Discontinuous Urban Fabric, Roads and Associated Lands, Pastures, and Forests. Results show how an urban digital twin can be a powerful tool for monitoring and territorial planning, with concrete applications in the public and risk management fields. This study also highlights the importance of geomatics technologies in the creation of realistic and functional virtual environments for the assessment and sustainable management of urban resources. Full article

(This article belongs to the Special Issue Advances in Smart Environments and Digital Twin Technologies)

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20 pages, 833 KiB

Open AccessArticle

Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT

by Rana Albelaihi

Future Internet 2025, 17(3), 109; https://doi.org/10.3390/fi17030109 - 1 Mar 2025

Viewed by 665

Abstract

This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited [...] Read more.

This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited resources in IoT environments pose significant challenges to the efficiency and reliability of FL. MACS is designed to maximize client participation while ensuring timely updates under computational and communication constraints. The proposed approach incorporates a Mobility Prediction Model to forecast client connectivity and resource availability and a Resource-Aware Client Evaluation mechanism to assess eligibility based on predicted latencies. MACS optimizes client selection, improves convergence rates, and enhances overall system performance by employing these predictive capabilities and a dynamic resource allocation strategy. The evaluation includes comparisons with advanced baselines such as Reinforcement Learning-based FL (RL-based) and Deep Learning-based FL (DL-based), in addition to Static and Random selection methods. For the CIFAR dataset, MACS achieved a final accuracy of 95%, outperforming Static selection (85%), Random selection (80%), RL-based FL (90%), and DL-based FL (93%). Similarly, for the MNIST dataset, MACS reached 98% accuracy, surpassing Static selection (92%), Random selection (88%), RL-based FL (94%), and DL-based FL (96%). Additionally, MACS consistently required fewer iterations to achieve target accuracy levels, demonstrating its efficiency in dynamic IoT environments. This strategy provides a scalable and adaptable solution for sustainable federated learning across diverse IoT applications, including smart cities, healthcare, and industrial automation. Full article

(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)

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19 pages, 2746 KiB

Open AccessArticle

Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events

by J. D. Morillo Reina and T. J. Mateo Sanguino

Future Internet 2025, 17(3), 108; https://doi.org/10.3390/fi17030108 - 1 Mar 2025

Viewed by 1297

Abstract

Log files are essential assets for IT engineers engaged in the security of server and computer systems. They provide crucial information for identifying malicious events, conducting cybersecurity incident analyses, performing audits, system maintenance, and ensuring compliance with security regulations. Nevertheless, there is still [...] Read more.

Log files are essential assets for IT engineers engaged in the security of server and computer systems. They provide crucial information for identifying malicious events, conducting cybersecurity incident analyses, performing audits, system maintenance, and ensuring compliance with security regulations. Nevertheless, there is still the possibility of deliberate data manipulation by own personnel, especially with regard to system access and configuration changes, where error tracking or debugging traces are vital. To address tampering of log files, this work proposes a solution to ensure data integrity, immutability, and non-repudiation through different blockchain-based public registry systems. This approach offers an additional layer of security through a decentralized, tamper-resistant ledger. To this end, this manuscript aims to provide a solid guideline for creating secure log storage systems. For this purpose, methodologies and experiments using two different blockchains are presented to demonstrate their effectiveness in various contexts, such as transactions with and without metadata. The findings suggest that Solana’s response times make it well suited for environments with moderately critical records requiring certification. In contrast, Cardano shows higher response times, thus making it suitable for less frequent events with metadata that requires legitimacy. Full article

(This article belongs to the Special Issue Future Directions in Blockchain Technologies)

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48 pages, 1061 KiB

Open AccessReview

Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things

by John Mulo, Hengshuo Liang, Mian Qian, Milon Biswas, Bharat Rawal, Yifan Guo and Wei Yu

Future Internet 2025, 17(3), 107; https://doi.org/10.3390/fi17030107 - 1 Mar 2025

Viewed by 1348

Abstract

Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, [...] Read more.

Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things, 3rd Edition)

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32 pages, 2442 KiB

Open AccessArticle

Federated Learning System for Dynamic Radio/MEC Resource Allocation and Slicing Control in Open Radio Access Network

by Mario Martínez-Morfa, Carlos Ruiz de Mendoza, Cristina Cervelló-Pastor and Sebastia Sallent-Ribes

Future Internet 2025, 17(3), 106; https://doi.org/10.3390/fi17030106 - 26 Feb 2025

Viewed by 879

Abstract

The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource [...] Read more.

The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource utilization, posing challenges in meeting the stringent requirements of next-generation applications. The Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms, enabling disaggregation, virtualization, and real-time adaptability—which are key to achieving ultra-low latency, enhanced bandwidth efficiency, and intelligent resource management in future cellular systems. This paper presents a Federated Deep Reinforcement Learning (FDRL) framework for dynamic radio and edge computing resource allocation and slicing management in O-RAN environments. An Integer Linear Programming (ILP) model has also been developed, resulting in the proposed FDRL solution drastically reducing the system response time. On the other hand, unlike centralized Reinforcement Learning (RL) approaches, the proposed FDRL solution leverages Federated Learning (FL) to optimize performance while preserving data privacy and reducing communication overhead. Comparative evaluations against centralized models demonstrate that the federated approach improves learning efficiency and reduces bandwidth consumption. The system has been rigorously tested across multiple scenarios, including multi-client O-RAN environments and loss-of-synchronization conditions, confirming its resilience in distributed deployments. Additionally, a case study simulating realistic traffic profiles validates the proposed framework’s ability to dynamically manage radio and computational resources, ensuring efficient and adaptive O-RAN slicing for diverse and high-mobility scenarios. Full article

(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)

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21 pages, 553 KiB

Open AccessArticle

User-Generated Content and Its Impact on Purchase Intent for Tourism Products: A Comparative Analysis of Millennials and Centennials on TikTok

by Eva Correia Ramos and Célia M. Q. Ramos

Future Internet 2025, 17(3), 105; https://doi.org/10.3390/fi17030105 - 25 Feb 2025

Viewed by 1486

Abstract

In an increasingly technological society, online social networks are essential to support consumer purchasing decisions, primarily through User Generated Content (UGC). In this research, we look at the influence of UGC on purchase intent applied to the tourism product on the TikTok social [...] Read more.

In an increasingly technological society, online social networks are essential to support consumer purchasing decisions, primarily through User Generated Content (UGC). In this research, we look at the influence of UGC on purchase intent applied to the tourism product on the TikTok social network. In this sense, a survey was applied to TikTok users aged between 18 and 42 to compare their behaviour with that of the two generations: Millennials and Centennials. The results indicate a relationship of influence between credibility and the usefulness of information and between usefulness and social influence on the intention to buy tourism products and services. In addition, a comparison was made between the results of the sample of individuals belonging to the Millennial Generation and the sample of individuals belonging to the Centennial Generation, with the main discrepancy in the results being the relationship between the need for information and the usefulness of information. These insights pave the way for further research aimed at establishing more robust conclusions in this area. Full article

(This article belongs to the Special Issue Advances in Smart Environments and Digital Twin Technologies)

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15 pages, 1933 KiB

Open AccessArticle

Assessing Browser Security: A Detailed Study Based on CVE Metrics

by Oleksii Chalyi, Kęstutis Driaunys and Vytautas Rudžionis

Future Internet 2025, 17(3), 104; https://doi.org/10.3390/fi17030104 - 25 Feb 2025

Viewed by 1011

Abstract

This study systematically evaluates the vulnerabilities of modern web browsers using developed indices derived from the CVE database, including I_CVE, I_CVSS, I_R and I_T. These indices incorporate metrics such as vulnerability severity and risks, along with [...] Read more.

This study systematically evaluates the vulnerabilities of modern web browsers using developed indices derived from the CVE database, including I_CVE, I_CVSS, I_R and I_T. These indices incorporate metrics such as vulnerability severity and risks, along with browser popularity, to enable a balanced comparison of browser security. The results highlight significant differences in browser security: while Google Chrome and Samsung Internet exhibited lower threat indices, Mozilla Firefox demonstrated consistently higher scores, indicating greater exposure to risks. These observations a slightly contradict widespread opinion. The findings emphasize the importance of timely software updates in mitigating vulnerabilities, as many incidents were linked to outdated browser versions. This study also introduces a robust methodology for assessing browser threats, providing a framework for future research. Potential applications include developing browser-based penetration testing systems to simulate phishing and data extraction scenarios, offering insights into user-specific risks and broader organizational impacts. By combining theoretical analysis with practical implications, this work contributes to advancing browser security and lays the foundation for future applied research in cybersecurity. Full article

(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—3rd Edition)

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15 pages, 1925 KiB

Open AccessArticle

Gamitest: A Game-like Online Student Assessment System

by Jakub Swacha and Artur Kulpa

Future Internet 2025, 17(3), 103; https://doi.org/10.3390/fi17030103 - 24 Feb 2025

Viewed by 472

Abstract

The widespread availability of mobile devices has led to the emergence of multiple gamified web and mobile applications for online assessment of students during classes. Their common weak side is that they focus mostly on positive reinforcement, without exploiting the pedagogical potential of [...] Read more.

The widespread availability of mobile devices has led to the emergence of multiple gamified web and mobile applications for online assessment of students during classes. Their common weak side is that they focus mostly on positive reinforcement, without exploiting the pedagogical potential of loss and failure experience; thus, they are far from an actual game-like experience. In this paper, we present Gamitest, a course-subject-agnostic web application for student assessment that features an original game-like scheme to improve students’ perception of engagement and fun, as well as to reduce their examination stress. The results of the survey-based evaluation of the tool indicate that its design goals have been met and allow us to recommend it for consideration in various forms of student assessment, as well as provide grounds for future work on analyzing the tool’s effects on learning. Full article

(This article belongs to the Topic Advances in Online and Distance Learning)

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Future Internet, Volume 17, Issue 3 (March 2025) – 37 articles

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