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Securing the SDN Data Plane in Emerging Technology Domains: A Review
Towards Proactive Domain Name Security: An Adaptive System for .ro domains Reputation Analysis
Intelligent Control Approaches for Warehouse Performance Optimisation in Industry 4.0 Using Machine Learning
AI-Driven Transformations in Manufacturing: Bridging Industry 4.0, 5.0, and 6.0 in Sustainable Value Chains
Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications

Journal Description

Future Internet

Future Internet is an international, peer-reviewed, open access journal on internet technologies and the information society, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Information Systems) / CiteScore - Q1 (Computer Networks and Communications)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Journal Clusters of Network and Communications Technology: Future Internet, IoT, Telecom, Journal of Sensor and Actuator Networks, Network, Signals.

Impact Factor: 3.6 (2024); 5-Year Impact Factor: 3.5 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1999-5903

Latest Articles

43 pages, 1322 KB

Open AccessArticle

Blockchain–AI–Geolocation Integrated Architecture for Mobile Identity and OTP Verification

by Gajasin Gamage Damith Sulochana and Dilshan Indraraj De Silva

Future Internet 2025, 17(12), 534; https://doi.org/10.3390/fi17120534 (registering DOI) - 23 Nov 2025

One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication [...] Read more.

One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators. Full article

(This article belongs to the Special Issue Advances in Wireless and Mobile Networking—2nd Edition)

23 pages, 2036 KB

Open AccessArticle

RIS-Assisted Joint Communication, Sensing, and Multi-Tier Computing Systems

by Yunzhe Wang and Minzheng Li

Future Internet 2025, 17(12), 533; https://doi.org/10.3390/fi17120533 (registering DOI) - 23 Nov 2025

This paper investigates the application of Reconfigurable Intelligent Surfaces (RIS) in Joint Communication, Sensing, and Multi-tier Computing (JCSMC). An RIS-assisted JCSMC framework is proposed, wherein a full-duplex multi-antenna Base Station (BS) is employed to sense targets and provide edge computation services to User [...] Read more.

This paper investigates the application of Reconfigurable Intelligent Surfaces (RIS) in Joint Communication, Sensing, and Multi-tier Computing (JCSMC). An RIS-assisted JCSMC framework is proposed, wherein a full-duplex multi-antenna Base Station (BS) is employed to sense targets and provide edge computation services to User Equipment (UE). To enhance computational efficiency, a Multi-Tier Computing (MTC) architecture is adopted, enabling joint processing of computing tasks through the deployment of both the BS and the Cloud Servers (CS). Meanwhile, this paper studies the potential advantages of RIS in the proposed framework. It can assist in enhancing the efficiency of resource sharing between sensing and computing functions and then maximize the ability of computing the offload. This study aims to maximize the computation rate by jointly optimizing the BS transmission beamformer, RIS reflection coefficients, and computational resource allocation. The ensuing non-convex optimization problems are addressed using an alternating optimization algorithm based on Block Coordinate Ascent (BCA) for partial offloading mode, which ensures convergence to a local optimum, then extending the proposed joint design algorithms to the scenario with imperfect Self-Interference Cancellation. The effectiveness of the proposed algorithm was confirmed by analyzing and contrasting the simulation results with the benchmark scheme. The simulation results show that, when the BS resources are limited, utilizing MTC architecture can significantly improve the computation rate. In addition, the proposed RIS-assisted JSCMC framework is superior to other benchmark schemes in dealing with resource utilization between different functions, achieving superior computing power while maintaining sensing quality. Full article

25 pages, 9168 KB

Open AccessArticle

A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment

by Aysha Alfaw, Mohsen Rouached and Aymen Akremi

Future Internet 2025, 17(12), 532; https://doi.org/10.3390/fi17120532 - 21 Nov 2025

Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats. This paper proposes a resilient deep learning framework for [...] Read more.

Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats. This paper proposes a resilient deep learning framework for Android malware detection, integrating multiple models and a CPU-aware selection algorithm to balance accuracy and efficiency on mobile devices. Two benchmark datasets (i.e., the Android Malware Dataset for Machine Learning and CIC-InvesAndMal2019) were used to evaluate five deep learning models: DNN, CNN, RNN, LSTM, and CNN-LSTM. The results show that CNN-LSTM achieves the highest detection accuracy of 97.4% on CIC-InvesAndMal2019, while CNN delivers strong accuracy of 98.07%, with the lowest CPU usage (5.2%) on the Android Dataset, making it the most practical for on-device deployment. The framework is implemented as an Android application using TensorFlow Lite, providing near-real-time malware detection with an inference time of under 150 ms and memory usage below 50 MB. These findings confirm the effectiveness of deep learning for mobile malware detection and demonstrate the feasibility of deploying resilient detection systems on resource-constrained devices. Full article

(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)

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21 pages, 8038 KB

Open AccessArticle

Semantic Data Federated Query Optimization Based on Decomposition of Block-Level Subqueries

by Yuan Yao and Yang Zhang

Future Internet 2025, 17(11), 531; https://doi.org/10.3390/fi17110531 - 20 Nov 2025

The digital age and the rise of Internet of Things technology have led to an explosion of data, including vast amounts of semantic data. In the context of large-scale semantic data graphs, centralized storage struggles to meet the efficiency requirements of the queries. [...] Read more.

The digital age and the rise of Internet of Things technology have led to an explosion of data, including vast amounts of semantic data. In the context of large-scale semantic data graphs, centralized storage struggles to meet the efficiency requirements of the queries. This has led to a shift towards distributed semantic data systems. In federated semantic data systems, ensuring both query efficiency and comprehensive results is challenging because of data independence and privacy constraints. To address this, we propose a query processing framework featuring a block-level star decomposition method for generating efficient query plans, augmented by auxiliary indexes to guarantee the completeness of the results. A specialized FEDERATEDAND BY keyword is introduced for federated environments, and a partition-based parallel assembly method accelerates the result integration. Our approach demonstrably improves query efficiency and is analyzed for its potential application in energy systems. Full article

(This article belongs to the Special Issue Internet of Things Technology and Service Computing)

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21 pages, 653 KB

Open AccessArticle

A Stateful Extension to P4THLS for Advanced Telemetry and Flow Control

by Mostafa Abbasmollaei, Tarek Ould-Bachir and Yvon Savaria

Future Internet 2025, 17(11), 530; https://doi.org/10.3390/fi17110530 - 20 Nov 2025

Programmable data planes are increasingly essential for enabling In-band Network Telemetry (INT), fine-grained monitoring, and congestion-aware packet processing. Although the P4 language provides a high-level abstraction to describe such behaviors, implementing them efficiently on FPGA-based platforms remains challenging due to hardware constraints and [...] Read more.

Programmable data planes are increasingly essential for enabling In-band Network Telemetry (INT), fine-grained monitoring, and congestion-aware packet processing. Although the P4 language provides a high-level abstraction to describe such behaviors, implementing them efficiently on FPGA-based platforms remains challenging due to hardware constraints and limited compiler support. Building on P4THLS framework, which leverages HLS for FPGA data-plane programmability, this paper extends the approach by introducing support for P4-style stateful objects and a structured metadata propagation mechanism throughout the processing pipeline. These extensions enrich pipeline logic with real-time context and flow-level state, thereby facilitating advanced applications while preserving programmability. The generated codebase remains extensible and customizable, allowing developers to adapt the design to various scenarios. We implement two representative use cases to demonstrate the effectiveness of the approach: an INT-enabled forwarding engine that embeds hop-by-hop telemetry into packets and a congestion-aware switch that dynamically adapts to queue conditions. Evaluation of an AMD Alveo U280 FPGA implementation reveals that incorporating INT support adds roughly 900 LUTs and 1000 Flip-Flops relative to the baseline switch. Furthermore, the proposed meter maintains rate measurement errors below 3% at 700 Mbps and achieves up to a 5× reduction in LUT and 2× reduction in Flip-Flop usage compared to existing FPGA-based stateful designs, substantially expanding the applicability of P4THLS for complex and performance-critical network functions. Full article

(This article belongs to the Special Issue Key Enabling Technologies for Beyond 5G Networks—2nd Edition)

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38 pages, 4889 KB

Open AccessArticle

Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest

by Brou Médard Kouassi, Abou Bakary Ballo, Kacoutchy Jean Ayikpa, Diarra Mamadou and Minfonga Zié Jérôme Coulibaly

Future Internet 2025, 17(11), 529; https://doi.org/10.3390/fi17110529 - 19 Nov 2025

The rapid growth of the Internet of Things (IoT) has created vast networks of interconnected devices that are increasingly exposed to cyberattacks. Ensuring the security of such distributed systems requires efficient and adaptive intrusion detection mechanisms. However, conventional methods face limitations in processing [...] Read more.

The rapid growth of the Internet of Things (IoT) has created vast networks of interconnected devices that are increasingly exposed to cyberattacks. Ensuring the security of such distributed systems requires efficient and adaptive intrusion detection mechanisms. However, conventional methods face limitations in processing large and complex feature spaces. To address this issue, this study proposes an optimized intrusion detection approach based on Top-K feature selection combined with ensemble learning models, evaluated on the CICIoMT2024 dataset. Three algorithms, XGBoost, LightGBM, and Random Forest, were trained and tested on IoT datasets using three feature configurations: Top-10, Top-15, and the complete feature set. The results show that the Random Forest model provides the best balance between accuracy and computational efficiency, achieving 91.7% accuracy and an F1-score of 93% with the Top-10 subset while reducing processing time by 35%. These findings demonstrate that the Top-K selection strategy enhances the interpretability and performance of IDSs in IoT environments. Future work will extend this framework to real-time adaptive detection and edge computing integration for large-scale IoT deployments. Full article

(This article belongs to the Special Issue Machine Learning and Internet of Things in Industry 4.0)

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22 pages, 1557 KB

Open AccessArticle

AI-Driven Damage Detection in Wind Turbines: Drone Imagery and Lightweight Deep Learning Approaches

by Ahmed Hamdi and Hassan N. Noura

Future Internet 2025, 17(11), 528; https://doi.org/10.3390/fi17110528 - 19 Nov 2025

Wind power plays an increasingly vital role in sustainable energy production, yet the harsh environments in which turbines operate often lead to mechanical or structural degradation. Detecting such faults early is essential to reducing maintenance expenses and extending operational lifetime. In this work, [...] Read more.

Wind power plays an increasingly vital role in sustainable energy production, yet the harsh environments in which turbines operate often lead to mechanical or structural degradation. Detecting such faults early is essential to reducing maintenance expenses and extending operational lifetime. In this work, we propose a deep learning-based image classification framework designed to assess turbine condition directly from drone-acquired imagery. Unlike object detection pipelines, which require locating specific damage regions, the proposed strategy focuses on recognizing global visual cues that indicate the overall turbine state. A comprehensive comparison is performed among several lightweight and transformer-based architectures, including MobileNetV3, ResNet, EfficientNet, ConvNeXt, ShuffleNet, ViT, DeiT, and DINOv2, to identify the most suitable model for real-time deployment. The MobileNetV3-Large network achieved the best trade-off between performance and efficiency, reaching 98.9% accuracy while maintaining a compact size of 5.4 million parameters. These results highlight the capability of compact CNNs to deliver accurate and efficient turbine monitoring, paving the way for autonomous, drone-based inspection solutions at the edge. Full article

(This article belongs to the Special Issue Navigation, Deployment and Control of Intelligent Unmanned Vehicles)

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12 pages, 439 KB

Open AccessArticle

Advancing Conversational Text-to-SQL: Context Strategies and Model Integration with Large Language Models

by Benjamin G. Ascoli and Jinho D. Choi

Future Internet 2025, 17(11), 527; https://doi.org/10.3390/fi17110527 - 18 Nov 2025

Conversational text-to-SQL extends the traditional single-turn SQL generation paradigm to multi-turn, dialogue-based scenarios, enabling users to pose and refine database queries interactively, and requiring models to track dialogue context over multiple user queries and system responses. Despite extensive progress in single-turn benchmarks such [...] Read more.

Conversational text-to-SQL extends the traditional single-turn SQL generation paradigm to multi-turn, dialogue-based scenarios, enabling users to pose and refine database queries interactively, and requiring models to track dialogue context over multiple user queries and system responses. Despite extensive progress in single-turn benchmarks such as Spider and BIRD, and the recent rise of large language models, conversational datasets continue to pose challenges. In this paper, we spotlight model merging as a key strategy for boosting ESM performance on CoSQL and SParC. We present a new state-of-the-art system on the CoSQL benchmark, achieved by fine-tuning CodeS-7b under two paradigms for handling conversational history: (1) full history concatenation, and (2) question rewriting via GPT-based summarization. While each paradigm alone obtains competitive results, we observe that averaging the weights of these fine-tuned models can outperform both individual variants. Our findings highlight the promise of LLM-driven multi-turn SQL generation, offering a lightweight yet powerful avenue for improving conversational text-to-SQL. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) and Natural Language Processing (NLP))

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23 pages, 3571 KB

Open AccessArticle

Optimizing Cloud Service Composition with Cuckoo Optimization Algorithm for Enhanced Resource Allocation and Energy Efficiency

by Issam AlHadid, Evon Abu-Taieh, Mohammad Al Rawajbeh, Suha Afaneh, Mohammed E. Daghbosheh, Rami S. Alkhawaldeh, Sufian Khwaldeh and Ala’aldin Alrowwad

Future Internet 2025, 17(11), 526; https://doi.org/10.3390/fi17110526 - 18 Nov 2025

The composition of cloud services plays a vital role in optimizing resource allocation, load balancing, task scheduling, and energy management. However, it remains a significant challenge due to the dynamic nature of workloads and the variability in resource demands, where addressing these challenges [...] Read more.

The composition of cloud services plays a vital role in optimizing resource allocation, load balancing, task scheduling, and energy management. However, it remains a significant challenge due to the dynamic nature of workloads and the variability in resource demands, where addressing these challenges is essential for ensuring seamless service delivery. This research investigated the implementation of the Cuckoo Optimization Algorithm (COA) in a cloud computing environment to optimize service composition. In the proposed approach, each service was treated as an egg, where high-demand services represented the host’s original eggs, while low-demand services represented the cuckoo bird’s eggs that competed for the same resources. This implementation enabled the algorithm to balance workloads dynamically and allocate resources efficiently while optimizing load balancing, task scheduling, cost reduction, processing and response times, system stability, and energy management. The simulations were conducted using CloudSim 5.0, and the results were compared with the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms across key performance metrics. Experimental results clearly demonstrate that the COA outperformed both PSO and ACO across all evaluated metrics. The COA achieved higher efficiency in task scheduling, dynamic load balancing, and energy-aware resource allocation. It consistently maintained lower operational costs, reduced SLA violations, and achieved superior task completion and VM utilization rates. These findings underscore the COA’s potential as a robust and scalable approach for optimizing cloud service composition in dynamic and resource-constrained environments. Full article

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

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15 pages, 1126 KB

Open AccessArticle

Edge Caching Strategies for Seamless Handover in SDN-Enabled Connected Cars

by Kuljaree Tantayakul, Wasimon Panichpattanakul, Adisak Intana and Parin Sornlertlamvanich

Future Internet 2025, 17(11), 525; https://doi.org/10.3390/fi17110525 - 18 Nov 2025

This paper presents an analytical evaluation of three strategies designed to minimize service disruption during handovers in Software-Defined Networking (SDN)-based connected car networks: SDN Mobility with a Caching Policy, cache-miss Content Delivery Network (CDN), and cache-hit CDN. The performance trade-offs inherent in each [...] Read more.

This paper presents an analytical evaluation of three strategies designed to minimize service disruption during handovers in Software-Defined Networking (SDN)-based connected car networks: SDN Mobility with a Caching Policy, cache-miss Content Delivery Network (CDN), and cache-hit CDN. The performance trade-offs inherent in each approach have been quantitatively compared, and their multi-faceted performances have been analyzed. The evaluation includes a range of Session-to-Mobility Ratios (SMR), sensitivities of core network latency and L2 handover delays, and a realistic assessment of CDN effectiveness as a function of its cache-hit ratio. Our analysis conclusively shows that the availability of content on the network edge is the key factor for ensuring seamless connectivity. The study also identifies a quantitative crossover point 70% cache-hit ratio, where the proactive CDN’s performance surpasses that of reactive Caching Policy, establishing a clear target for predictive algorithms. Results confirm that while the cache-hit strategy offers the lowest disruption, the high data plane latency of a cache-miss 100 ms delay makes it unsuitable for vehicular applications, and the Caching Policy is limited by a 30 ms control plane overhead. This paper provides a robust framework for future vehicular network designs that proactive edge-based architectures are critical for stringent connectivity requirements. Full article

(This article belongs to the Special Issue Software-Defined Networking and Network Function Virtualization)

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31 pages, 4478 KB

Open AccessArticle

Explainable Artificial Intelligence System for Guiding Companies and Users in Detecting and Fixing Multimedia Web Vulnerabilities on MCS Contexts

by Sergio Alloza-García, Iván García-Magariño and Raquel Lacuesta Gilaberte

Future Internet 2025, 17(11), 524; https://doi.org/10.3390/fi17110524 - 17 Nov 2025

In the evolving landscape of Mobile Crowdsourcing (MCS), ensuring the security and privacy of both stored and transmitted multimedia content has become increasingly challenging. Factors such as human mobility, device heterogeneity, dynamic topologies, and data diversity exacerbate the complexity of addressing these concerns [...] Read more.

In the evolving landscape of Mobile Crowdsourcing (MCS), ensuring the security and privacy of both stored and transmitted multimedia content has become increasingly challenging. Factors such as human mobility, device heterogeneity, dynamic topologies, and data diversity exacerbate the complexity of addressing these concerns effectively. To tackle these challenges, this paper introduces CSXAI (Crowdsourcing eXplainable Artificial Intelligence)—a novel explainable AI system designed to proactively assess and communicate the security status of multimedia resources downloaded in MCS environments. While CSXAI integrates established attack detection techniques, its primary novelty lies in its synthesis of these methods with a user-centric XAI framework tailored for the specific challenges of MCS frameworks. CSXAI intelligently analyzes potential vulnerabilities and threat scenarios by evaluating website context, attack impact, and user-specific characteristics. The current implementation focuses on the detection and explanation of three major web vulnerability classes: Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), and insecure File Upload. The proposed system not only detects digital threats in advance but also adapts its explanations to suit both technical and non-technical users, thereby enabling informed decision-making before users access potentially harmful content. Furthermore, the system offers actionable security recommendations through clear, tailored explanations, enhancing users’ ability to implement protective measures across diverse devices. The results from real-world testing suggest a notable improvement in users’ ability to understand and mitigate security risks in MCS environments. By combining proactive vulnerability detection with user-adaptive, explainable feedback, the CSXAI framework shows promise in empowering users to enhance their security posture effectively, even with minimal cybersecurity expertise. These findings underscore the potential of CSXAI as a reliable and accessible solution for tackling cybersecurity challenges in dynamic, multimedia-driven ecosystems. Quantitative results showed high user satisfaction and interpretability (SUS = 79.75 ± 6.40; USE subscales = 5.32–5.88). Full article

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22 pages, 915 KB

Open AccessArticle

Performance Evaluation Methodology for Federated XR Network Digital Twins in AI-Aware 6G Networks

by Xavier Calle-Heredia and Xavier Hesselbach

Future Internet 2025, 17(11), 523; https://doi.org/10.3390/fi17110523 - 17 Nov 2025

Network digital twins (NDTs) are emerging as key enablers of 6G networks integrating artificial Intelligence (AI) techniques. NDT systems offer novel features, including real-time monitoring, simulation, enhanced network planning, autonomous management, seamless integration with emerging technologies such as extended reality (XR), among others. [...] Read more.

Network digital twins (NDTs) are emerging as key enablers of 6G networks integrating artificial Intelligence (AI) techniques. NDT systems offer novel features, including real-time monitoring, simulation, enhanced network planning, autonomous management, seamless integration with emerging technologies such as extended reality (XR), among others. When NDTs converge with XR, NDTs can be customized with additional interactive services that are not available in the original network. In this work, artificial intelligence (AI) strategies are applied to a set of XR functions within federated NDTs. While existing NDT approaches follow a one-to-one (1:1) model, where a single NDT instance is deployed from an original network, the one-to-many (1:N) federation model requires the orchestration of multiple XR-tailored NDT instances. The federation of NDTs can be applied across diverse 6G use cases, including telemedicine, UAV management, Industry 4.0, and the remote driving of complex vehicles. Ensuring the optimal operation of the NDT federation requires a methodology tailored to the requirements of each use case. This paper introduces a score-based performance analysis to quantify the benefits achieved through NDT federation. Unlike existing models for the digital twin (DT) federation, this paper introduces a KPI-based rational model that quantifies the trade-off between federation benefits and the associated operational complexity. A mathematical analysis is performed to validate the consistency of the score formula both in general terms and within the context of each specific use case. Full article

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

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21 pages, 4379 KB

Open AccessArticle

ReHAb Playground: A DL-Based Framework for Game-Based Hand Rehabilitation

by Samuele Rasetto, Giorgia Marullo, Ludovica Adamo, Federico Bordin, Francesca Pavesi, Chiara Innocente, Enrico Vezzetti and Luca Ulrich

Future Internet 2025, 17(11), 522; https://doi.org/10.3390/fi17110522 - 17 Nov 2025

Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built [...] Read more.

Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built in the Unity Game Engine. The system utilizes a YOLOv10n model for hand gesture classification and MediaPipe Hands for 3D hand landmark extraction. Three mini-games were developed to target specific motor and cognitive functions: Cube Grab, Coin Collection, and Simon Says. Key gameplay parameters, namely repetitions, time limits, and gestures, can be tuned according to therapeutic protocols. Experiments with healthy participants were conducted to establish reference performance ranges based on average completion times and standard deviations. The results showed a consistent decrease in both task completion and gesture times across trials, indicating learning effects and improved control of gesture-based interactions. The most pronounced improvement was observed in the more complex Coin Collection task, confirming the system’s ability to support skill acquisition and engagement in rehabilitation-oriented activities. ReHAb Playground was conceived with modularity and scalability at its core, enabling the seamless integration of additional exercises, gesture libraries, and adaptive difficulty mechanisms. While preliminary, the findings highlight its promise as an accessible, low-cost rehabilitation platform suitable for home use, capable of monitoring motor progress over time and enhancing patient adherence through engaging, game-based interactions. Future developments will focus on clinical validation with patient populations and the implementation of adaptive feedback strategies to further personalize the rehabilitation process. Full article

(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)

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25 pages, 12497 KB

Open AccessArticle

Hybrid Sensor Fusion Beamforming for UAV mmWave Communication

by Yuya Sugimoto and Gia Khanh Tran

Future Internet 2025, 17(11), 521; https://doi.org/10.3390/fi17110521 - 17 Nov 2025

Resilient autonomous inter-Unmanned Aerial Vehicle (UAV) communication is critical for applications like drone swarms. While conventional Global Navigation Satellite System (GNSS)-based beamforming is effective at long ranges, it suffers from significant pointing errors at close range due to latency, low update rates and [...] Read more.

Resilient autonomous inter-Unmanned Aerial Vehicle (UAV) communication is critical for applications like drone swarms. While conventional Global Navigation Satellite System (GNSS)-based beamforming is effective at long ranges, it suffers from significant pointing errors at close range due to latency, low update rates and the inherent GNSS positioning error. To overcome these limitations, this paper proposes a novel hybrid beamforming system that enhances resilience by adaptively switching between two methods. For short-range operations, our system leverages Light Detection and Ranging (LiDAR)–camera sensor fusion for high-accuracy, low-latency UAV tracking, enabling precise millimeter-wave (mmWave) beamforming. For long-range scenarios beyond the camera’s detection limit, it intelligently switches to a GNSS-based method. The switching threshold is determined by considering both the sensor’s effective range and the pointing errors caused by GNSS latency and a UAV velocity. Simulations conducted in a realistic urban model demonstrate that our hybrid approach compensates for the weaknesses of each individual method. It maintains a stable, high-throughput link across a wide range of distances, achieving superior performance and resilience compared to systems relying on a single tracking method. This paves the way for advanced autonomous drone network operations in dynamic environments. Full article

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18 pages, 2540 KB

Open AccessArticle

HEXADWSN: Explainable Ensemble Framework for Robust and Energy-Efficient Anomaly Detection in WSNs

by Rahul Mishra, Sudhanshu Kumar Jha, Shiv Prakash and Rajkumar Singh Rathore

Future Internet 2025, 17(11), 520; https://doi.org/10.3390/fi17110520 - 15 Nov 2025

Wireless Sensor Networks (WSNs) have a decisive share in various monitoring and control systems. However, their distributed and resource-constrained nature makes them vulnerable to anomalies caused by factors such as environmental noise, sensor faults, and cyber intrusions. In this paper, HEXADWSN, a hybrid [...] Read more.

Wireless Sensor Networks (WSNs) have a decisive share in various monitoring and control systems. However, their distributed and resource-constrained nature makes them vulnerable to anomalies caused by factors such as environmental noise, sensor faults, and cyber intrusions. In this paper, HEXADWSN, a hybrid ensemble learning-based explainable anomaly detection framework for anomaly detection to improve reliability and interpretability in WSNs, has been proposed. The proposed framework integrates an ensemble learning approach using Autoencoders, Isolation Forests, and One-Class SVMs to achieve robust detection of time-series-based irregularities in the Intel Lab dataset. The framework uses stack and vote ensemble learning. The stack ensemble achieved the highest overall performance, indicating strong effectiveness in detecting varied anomaly patterns. The voting ensemble demonstrated moderate results and offered a balance between detection rate and computation, whereas LSTM, which is efficient at capturing temporal dependencies, exhibited a relatively low performance in the processed dataset. SHAP, LIME, and Permutation Feature Importance techniques are employed for model explainability. These techniques offer insights into feature relevance and anomalies at global and local levels. The framework also measures the mean energy consumption for anomalous and normal data. The interpretability results identified that temperature, humidity, and voltage are the most influential features. HEXADWSN establishes a scalable and explainable foundation for anomaly detection in WSNs, striking a balance between accuracy, interpretability, and energy management insights. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)

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17 pages, 756 KB

Open AccessArticle

A DLT-Aware Performance Evaluation Framework for Virtual-Core Speedup Modeling

by Zile Xiang and Thomas G. Robertazzi

Future Internet 2025, 17(11), 519; https://doi.org/10.3390/fi17110519 - 14 Nov 2025

Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers [...] Read more.

Scheduling computing is a well-studied area focused on improving task execution by reducing processing time and increasing system efficiency. Divisible Load Theory (DLT) provides a structured analytical framework for distributing partitionable computational and communicational loads across processors, and its adaptability has allowed researchers to integrate it with other models and modern technologies. Building on this foundation, previous studies have shown that Amdahl-like laws can be effectively combined with DLT to produce more realistic performance models. This paper further develops analytical models that further extend such integration by incorporating Gustafson’s Law and Juurlink’s Law into DLT to capture broader scaling behaviors. It also extends the analysis to workload distribution in virtual multicore systems, providing a more structured basis for evaluating parallel performance. Methods include analytically computing speedup as a function of the number of cores and the parallelizable fraction under different scheduling strategies, with comparisons across workload conditions. Results show that combining DLT with speedup laws and virtual core design offers a deeper and more structured approach for analytical parallel system evaluation. While the analysis remains theoretical, the proposed framework establishes a mathematical foundation for future empirical validation, heterogeneous workload modeling, and sensitivity analysis. Full article

(This article belongs to the Special Issue Parallel and Distributed Systems)

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17 pages, 3261 KB

Open AccessArticle

Scalable Generation of Synthetic IoT Network Datasets: A Case Study with Cooja

by Hrant Khachatrian, Aram Dovlatyan, Greta Grigoryan and Theofanis P. Raptis

Future Internet 2025, 17(11), 518; https://doi.org/10.3390/fi17110518 - 13 Nov 2025

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This [...] Read more.

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks. Full article

(This article belongs to the Special Issue Industrial Internet of Things (IIoT): Trends and Technologies—2nd Edition)

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33 pages, 2519 KB

Open AccessArticle

Ontology-Driven Multi-Agent System for Cross-Domain Art Translation

by Viktor Matanski, Anton Iliev, Nikolay Kyurkchiev and Todorka Terzieva

Future Internet 2025, 17(11), 517; https://doi.org/10.3390/fi17110517 - 12 Nov 2025

Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a [...] Read more.

Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a novel approach combining a multi-agent system (MAS) architecture with an ontology-guided semantic representation to achieve cross-domain art translation while preserving the original artwork’s meaning and emotional impact. In our concept, specialized agents decompose the task: a Perception Agent extracts symbolic descriptors from the source artwork, a Translation Agent maps these descriptors using shared knowledge base, a Generator Agent creates the target-modality artwork, and a Curator Agent evaluates and refines the output for coherence and style alignment. This modular design, inspired by human creative workflows, allows complex artistic concepts (themes, moods, motifs) to carry over across modalities in a consistent and interpretable way. We implemented a prototype supporting translations between painting and poetry, leveraging state-of-the-art generative models. Preliminary results indicate that our ontology-driven MAS produces cross-domain translations that preserve key semantic elements and affective tone of the input, offering a new path toward explainable and controllable creative AI. Finally, we discuss a case study and potential applications from educational tools to synesthetic VR experiences and outline future research directions for enhancing the realm of intelligent agents. Full article

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

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22 pages, 1664 KB

Open AccessArticle

A Blockchain-Enabled Decentralized Zero-Trust Architecture for Anomaly Detection in Satellite Networks via Post-Quantum Cryptography and Federated Learning

by Sridhar Varadala and Hao Xu

Future Internet 2025, 17(11), 516; https://doi.org/10.3390/fi17110516 - 12 Nov 2025

The rapid expansion of satellite networks for advanced communication and space exploration has ensured that robust cybersecurity for inter-satellite links has become a critical challenge. Traditional security models rely on centralized trust authorities, and node-specific protections are no longer sufficient, particularly when system [...] Read more.

The rapid expansion of satellite networks for advanced communication and space exploration has ensured that robust cybersecurity for inter-satellite links has become a critical challenge. Traditional security models rely on centralized trust authorities, and node-specific protections are no longer sufficient, particularly when system failures or attacks affect groups of satellites or agent clusters. To address this problem, we propose a blockchain-enabled decentralized zero-trust model based on post-quantum cryptography (BEDZTM-PQC) to improve the security of satellite communications via continuous authentication and anomaly detection. This model introduces a group-based security framework, where satellite teams operate under a zero-trust architecture (ZTA) enforced by blockchain smart contracts and threshold cryptographic mechanisms. Each group shares the responsibility for local anomaly detection and policy enforcement while maintaining decentralized coordination through hierarchical federated learning, allowing for collaborative model training without centralizing sensitive telemetry data. A post-quantum cryptography (PQC) algorithm is employed for future-proof communication and authentication protocols against quantum computing threats. Furthermore, the system enhances network reliability by incorporating redundant communication channels, consensus-based anomaly validation, and group trust scoring, thus eliminating single points of failure at both the node and team levels. The proposed BEDZTM-PQC is implemented in MATLAB, and its performance is evaluated using key metrics, including accuracy, latency, security robustness, trust management, anomaly detection accuracy, performance scalability, and security rate with respect to different numbers of input satellite users. 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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27 pages, 548 KB

Open AccessArticle

Social Engineering with AI

by Alexandru-Raul Matecas, Peter Kieseberg and Simon Tjoa

Future Internet 2025, 17(11), 515; https://doi.org/10.3390/fi17110515 - 12 Nov 2025

The new availability of powerful Artificial Intelligence (AI) as an everyday copilot has instigated a new wave of attack techniques, especially in the area of Social Engineering (SE). The possibility of generating a multitude of different templates within seconds in order to carry [...] Read more.

The new availability of powerful Artificial Intelligence (AI) as an everyday copilot has instigated a new wave of attack techniques, especially in the area of Social Engineering (SE). The possibility of generating a multitude of different templates within seconds in order to carry out an SE-attack lowers the entry barrier for potential threat actors. Still, the question remains whether this can be done using openly available tools without specialized expert skill sets on the attacker side, and how these compare to each other. This paper conducts three experiments based on a blueprint from a real-world CFO fraud attack, which utilized two of the most used social engineering attacks, phishing and vishing, and investigates the success rate of these SE attacks based on utilizing different available LLMs. The third experiment centers around the training of an AI-powered chatbot to act as a social engineer and gather sensitive information from interacting users. As this work focuses on the offensive side of SE, all conducted experiments return promising results, proving not only the ability and effectiveness of AI technology to act unethically, but also the little to no implied restrictions. Based on a reflection on the findings and potential countermeasures available, this research provides a deeper understanding of the development and deployment of AI-enhanced SE attacks, further highlighting potential dangers, as well as mitigation methods against this “upgraded” type of threat. Full article

(This article belongs to the Special Issue Securing Artificial Intelligence Against Attacks)

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