Next Issue
Volume 18, September
Previous Issue
Volume 18, July
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
10.0
4.6
Journals
Future Internet
Volume 18
Issue 8
share announcement

Future Internet, Volume 18, Issue 8 (August 2026) – 63 articles

Cover Story (view full-size image): Sustainable IoT deployments demand network planning that weighs environmental impact, not only performance. This research examines how input representations shape learning-based, carbon footprint (CF)-aware gateway placement in LoRa multi-hop networks. A multilayer perceptron is retrained on end device coordinates, traffic weights, spatial sampling regions (SSRs) and a global CF estimate. SHAP, ablation and sensitivity analyses identify the CF estimate as the most influential input—a global signal driving large gateway relocations—while coordinates plus SSR summaries capture fine spatial detail and collective relay opportunities. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
27 pages, 3880 KB  
Article
Rail Bolt Defect Detection Method for Rail Transport Systems in Hilly and Mountainous Areas Based on LHFSE-YOLOv11
by Hao Chen, Jianquan Yao, Tianyou Ma, Jiahao Zheng and Jun Hu
Future Internet 2026, 18(8), 444; https://doi.org/10.3390/fi18080444 - 21 Aug 2026
Viewed by 223
Abstract
Objective: To address small bolt defect targets, complex background interference, and limited edge deployment in hilly and mountainous rail transport environments, a detection method balancing accuracy, lightweight design, and real-time performance was proposed. Methods: A track image dataset containing missing bolts, loose bolts, [...] Read more.
Objective: To address small bolt defect targets, complex background interference, and limited edge deployment in hilly and mountainous rail transport environments, a detection method balancing accuracy, lightweight design, and real-time performance was proposed. Methods: A track image dataset containing missing bolts, loose bolts, and missing nuts was constructed. Based on YOLOv11m, HFERBC3K2 was developed by replacing the standard bottleneck in C3K2 with a High-Frequency Enhancement Residual Block to strengthen edge, texture, and local structural feature extraction. A Spectral Enhanced Feed-Forward module was introduced into C2PSA to form SEFFNC2PSA, enhancing defect-related frequency components and suppressing background interference through adaptive frequency-domain modulation. The integrated model was named HFSE-YOLOv11. Channel-level structured pruning was then applied, and the model with a pruning ratio of 0.5 was named LHFSE-YOLOv11. Results: On the validation set, HFSE-YOLOv11 achieved 91.7% precision, 93.4% recall, 91.3% mAP@0.5, and 80.2% mAP@0.5:0.95, improving upon YOLOv11m by 3.6, 2.2, 1.2, and 3.1 percentage points, respectively. After pruning, LHFSE-YOLOv11 had 15.9 M parameters, 53.8 GFLOPs, and a 32.5 MB model size, representing reductions of 16.3%, 14.3%, and 11.7%, while mAP@0.5 and mAP@0.5:0.95 decreased by only 0.3 and 0.9 percentage points. On the independent test set, it achieved 91.7% precision, 93.4% recall, 91.0% mAP@0.5, 79.3% mAP@0.5:0.95, and 81.5 FPS, outperforming all compared models in the four detection metrics. Conclusion: LHFSE-YOLOv11 balances accuracy, efficiency, and model size, supporting deployment on vehicle-mounted inspection terminals and resource-constrained edge devices. Full article
(This article belongs to the Topic Smart Edge Devices: Design and Applications)
►▼ Show Figures

Graphical abstract

34 pages, 2336 KB  
Article
An Integrated University Digital Transformation Model Combining IT Governance, Interoperability, Cloud Security Assessment and Data Analytics: The UTMACH Case in Ecuador
by Jennifer Célleri-Pacheco, Fernanda Tusa Jumbo, Oswaldo Chuquirima Camacho, Santiago Rodríguez Yánez and Javier Andrade-Garda
Future Internet 2026, 18(8), 443; https://doi.org/10.3390/fi18080443 - 20 Aug 2026
Viewed by 384
Abstract
Digital transformation in higher education requires integrated designs connecting strategy, governance, infrastructure, interoperability, applications, cybersecurity, accessibility, analytics, and continuous improvement. This study analyzes the Integrated University Digital Transformation Model implemented at Universidad Técnica de Machala, Ecuador, between 2023 and April 2026. A qualitative [...] Read more.
Digital transformation in higher education requires integrated designs connecting strategy, governance, infrastructure, interoperability, applications, cybersecurity, accessibility, analytics, and continuous improvement. This study analyzes the Integrated University Digital Transformation Model implemented at Universidad Técnica de Machala, Ecuador, between 2023 and April 2026. A qualitative embedded case study with a design-science orientation examined institutional documents, technical records, governance evidence, cloud migration reports, interoperability mechanisms, system descriptions, analytics outputs, and cloud security assessment records through thematic analysis, process tracing, and artifact evaluation. Findings showed alignment between the Strategic Information Technology Plan and institutional planning; formal IT governance and information security structures; an API- and microservices-based architecture; a staged migration of 52 institutional servers in four operational lots, with documented post-migration monitoring indicating 99.85% average server uptime and no critical incidents attributable to the migration during the monitored period; an automated and traceable admission allocation process within a broader portfolio of integrated academic–administrative systems; and dashboards supporting evidence-informed decisions. MMGSI-Cloud complemented the architecture by assessing governance capabilities, identifying improvement priorities, and linking findings to an institutional roadmap. The case indicates that university digital transformation is strengthened when technological implementation is integrated with formal governance, systematic assessment, evidence-based planning, and institutional accountability. Full article
►▼ Show Figures

Graphical abstract

28 pages, 947 KB  
Article
An AI Answer-Validation Method Using Agentic RAG for Datasheet Inquiry for IoT Application System Deployment
by Dezheng Kong, Nobuo Funabiki, Htoo Htoo Sandi Kyaw, I Nyoman Darma Kotama and Zihao Zhu
Future Internet 2026, 18(8), 442; https://doi.org/10.3390/fi18080442 - 19 Aug 2026
Viewed by 426
Abstract
Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifications, communication interfaces, voltage ranges, and operating conditions from technical datasheets [...] Read more.
Internet of Things (IoT) application systems are increasingly adopted in factories, shops, offices, and governments. However, building such systems using various devices and modules remains difficult for non-experts, because they must confirm specifications, communication interfaces, voltage ranges, and operating conditions from technical datasheets before connecting devices. In previous studies, we have explored a generative AI-based answering tool for datasheet inquiry using Retrieval-Augmented Generation (RAG) for technical guidance of IoT application system deployment. However, the adopted top-kRAG pipeline often retrieves multiple related text chunks, which can cause the AI to confuse technically different specifications, such as power output voltage, signal output voltage, and input voltage range, and produce inaccurate answers. In addition, the AI may generate a hallucinated answer if the datasheet does not provide sufficient source information. In this paper, we propose an AI answer-validation method using agentic RAG for datasheet inquiry for IoT application system deployment. The method organizes datasheet information into structured specification data, including device models, field types, values, units, conditions, and source information. For question-answering, the agent coordinates structured fact query, top-k text retrieval, source checking, and rule-based compatibility comparison according to the question type. Instead of fully relying on the LLM to interpret retrieved chunks, this method adopts structured specifications and deterministic source checks before accepting the final answer. For evaluation, we constructed a dataset from 20 IoT datasheets, including 1000 question-answering tasks with three difficulty levels. Compared with conventional top-k RAG, the proposed method improved the correct answer rate from 0.686 to 0.958 for easy questions, from 0.549 to 0.969 for medium questions, and from 0.273 to 0.613 for hard questions, which confirms the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Future and Smart Internet of Things)
►▼ Show Figures

Figure 1

42 pages, 823 KB  
Article
Hierarchical Scheduler with Adaptive Time-Budget Reallocation for Time-Triggered Edge-Fog-Cloud Architectures
by Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa and Roman Obermaisser
Future Internet 2026, 18(8), 441; https://doi.org/10.3390/fi18080441 - 18 Aug 2026
Viewed by 342
Abstract
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making [...] Read more.
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions. Full article
(This article belongs to the Special Issue Cloud Computing and Service Computing: Synergies, Innovations, and Future Directions)
►▼ Show Figures

Figure 1

26 pages, 6578 KB  
Article
RICO-3D: A Benchmark and Baseline Method for Semantic Segmentation of Urban Roadways
by Wided Hammedi, Olivier Hotel, Franck Roudet and David Excoffier
Future Internet 2026, 18(8), 440; https://doi.org/10.3390/fi18080440 - 18 Aug 2026
Viewed by 356
Abstract
This paper presents RICO-3D (Roadway Infrastructure in Context), a new large-scale Mobile Laser Scanning (MLS) dataset for semantic segmentation of French urban roadways, together with GA-Attention, a geometry-aware attention U-Net designed for this task. RICO-3D was acquired with a Leica Pegasus TRK300 mobile [...] Read more.
This paper presents RICO-3D (Roadway Infrastructure in Context), a new large-scale Mobile Laser Scanning (MLS) dataset for semantic segmentation of French urban roadways, together with GA-Attention, a geometry-aware attention U-Net designed for this task. RICO-3D was acquired with a Leica Pegasus TRK300 mobile mapping system across Marseille, Rennes, and Opoul-Périllos (France), and provides per-point geometry, RGB, intensity, GPS time, scan angle rank, and semantic labels for 6 classes: vegetation, road, pole, building, cable, and vehicle. The dataset contains 780,981,961 labeled points and captures realistic MLS challenges, including severe class imbalance, sparse thin structures, occlusions, and varying seasonal and weather conditions. GA-Attention combines enriched geometric descriptors, attentive local aggregation, saliency-guided downsampling, attention-gated skip fusion, and curriculum-based training within a point-based encoder-decoder framework. On RICO-3D, the proposed method achieves 83.36% overall accuracy and the best IoU for road (91.35%), pole (49.91%), and cable (56.08%), with an inference time of 8.17 s. On Toronto-3D, it reaches 82.18% overall accuracy and 56.50% mIoU. These results show the relevance of RICO-3D for infrastructure-oriented MLS segmentation and the effectiveness of GA-Attention for thin and under-represented roadway infrastructure classes. To support reproducible research, the RICO-3D dataset, source code, trained models, and evaluation scripts will be publicly available once the Orange’s legal and data-governance validation process has been completed. Full article
(This article belongs to the Special Issue Algorithms and Models for Next-Generation Vision Systems)
►▼ Show Figures

Figure 1

20 pages, 2023 KB  
Article
TAR-DT: A Trusted and Attack-Resilient Mechanism for Distributed DNN Training in Agentic Edge Intelligence
by Zhonghui Wu, Yunxiao Ma, Lu Lu, Han Xiao and Chao Liu
Future Internet 2026, 18(8), 439; https://doi.org/10.3390/fi18080439 - 17 Aug 2026
Viewed by 223
Abstract
As deep neural networks continue to scale and enable emerging applications such as agentic AI systems, training increasingly relies on distributed paradigms across heterogeneous edge devices. However, this shift introduces significant security challenges, particularly model poisoning attacks, which are largely underexplored in model-parallel [...] Read more.
As deep neural networks continue to scale and enable emerging applications such as agentic AI systems, training increasingly relies on distributed paradigms across heterogeneous edge devices. However, this shift introduces significant security challenges, particularly model poisoning attacks, which are largely underexplored in model-parallel settings. To address these challenges, we propose a trusted and attack-resilient mechanism for distributed DNN training that supports both data and model parallelism. The mechanism leverages a blockchain-enabled infrastructure to ensure the tamper-resistant and auditable execution of security-critical operations. It introduces a Loss-aware Credit Evaluation mechanism to assess agent reliability based on group-level training dynamics and a Shuffling-based Isolation Mechanism to progressively cluster and isolate malicious agents across training epochs. In addition, Byzantine-tolerant aggregation (BTA) is employed to further mitigate adversarial influence during model aggregation. Extensive experiments demonstrate that the proposed mechanism achieves superior robustness and efficiency compared with state-of-the-art methods under diverse poisoning attack scenarios. Full article
(This article belongs to the Special Issue Blockchain-Empowered Internet of Agents for Trusted and Autonomous Networked Systems)
►▼ Show Figures

Figure 1

4 pages, 142 KB  
Editorial
Virtual Reality and Metaverse: Impact on the Digital Transformation of Society—3rd Edition
by Diego Vergara
Future Internet 2026, 18(8), 438; https://doi.org/10.3390/fi18080438 - 17 Aug 2026
Viewed by 247
Abstract
Immersive technologies—including virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the metaverse—have undergone significant development over the past decade and are increasingly being integrated into a broad range of application domains [...] Full article
(This article belongs to the Special Issue Virtual Reality and Metaverse: Impact on the Digital Transformation of Society—3rd Edition)
39 pages, 604 KB  
Article
Computational Jurisprudence: Verifiable Law for Machine Societies
by Vladimir Stantchev
Future Internet 2026, 18(8), 437; https://doi.org/10.3390/fi18080437 - 16 Aug 2026
Viewed by 365
Abstract
Autonomous AI agents now hold funds, delegate authority to other agents, and transact at machine speed, while the governance apparatus meant to constrain them (policies, audits, compliance) remains documentation-based and limited by human latency. Better monitoring or filtering cannot close this mismatch: compliance [...] Read more.
Autonomous AI agents now hold funds, delegate authority to other agents, and transact at machine speed, while the governance apparatus meant to constrain them (policies, audits, compliance) remains documentation-based and limited by human latency. Better monitoring or filtering cannot close this mismatch: compliance must become a runtime, compositional, proof-carrying property of computation itself. We call the resulting discipline computational jurisprudence. This article is an integrative review of the four literatures the discipline must synthesize, namely, object-capability security; verifiable, proof-carrying, and zero-knowledge computation; policy-as-code and computational law; and agentic AI with its emerging payment protocols. Each supplies a mature mechanism the others lack, and none supplies a complete normative substrate. The synthesis is organized into three pillars: (i) a delegation calculus, under which authority can only attenuate as it propagates between agents, for which we prove monotone attenuation in the conjunctive caveat fragment and exhibit a counterexample outside it; (ii) runtime compliance proofs, a three-tier evidence regime (attested, optimistic, and zero-knowledge); and (iii) sealed delegation chains with graduated attribution, reconciling capability-based privacy with the accountability adjudication requires. A case study on agentic payments grounds the architecture and evaluates three components on two platforms, with five independent executions each: local capability verification against a centralized policy decision point, enforcement on the x402 payment path, and accumulator-based revocation. What the article offers is therefore a survey, a conceptual architecture with a formal core, and a partial evaluation of three components, not a fully implemented system; a status table marks that boundary component by component. Eight open problems define the research agenda. Full article
(This article belongs to the Section Cybersecurity)
►▼ Show Figures

Graphical abstract

17 pages, 307 KB  
Article
The Telephone AI Paradox: How Voice Agents Can Help Counter Unwanted Telemarketing Through Role-Based Automation, Transparency, and Governance
by Eldar Sultanow, Alexander Loosley, Alina Chircu, Jonas Arnold, Timon Bayer, Emilia Bauer, Yudha Hefitra Firdaus, Stoyan Ivanov, Elisa Rofalski, Serhat Ugur and Christian Czarnecki
Future Internet 2026, 18(8), 436; https://doi.org/10.3390/fi18080436 - 14 Aug 2026
Viewed by 527
Abstract
Unwanted telemarketing calls are a persistent source of consumer frustration and a legally regulated issue in Germany. At first glance, the idea of addressing this problem with AI-based voice technology appears contradictory: why should an automated caller help restore trust in a communication [...] Read more.
Unwanted telemarketing calls are a persistent source of consumer frustration and a legally regulated issue in Germany. At first glance, the idea of addressing this problem with AI-based voice technology appears contradictory: why should an automated caller help restore trust in a communication channel that has been damaged by aggressive outbound practices? This design-oriented case and prototype study argues that the paradox can be resolved through a different design logic. Rather than using AI to intensify persuasion, we present a role-based voice-agent architecture that constrains conversational behavior through narrow task boundaries, explicit escalation rules, and auditable data handling. The paper reports a transfer project involving FH Aachen students, Capgemini, and Fairdient GmbH. Methodologically, the work is positioned as a design-oriented case study with a prototype artifact. The contribution is threefold: first, we describe a three-agent architecture for outbound screening, consent-aware explanation, and inbound service; second, we derive governance principles for legally and ethically sensitive telephony, including transparency, bounded knowledge, privacy-preserving deployment, and human fallback; and third, we propose an evaluation framework covering conversion, compliance, hallucination control, user trust, and cost per validated outcome. The prototype does not yet claim large-scale field effectiveness. Instead, it offers a structured and empirically testable design for trustworthy voice automation in a domain where misuse, opacity, and user distrust are especially pronounced. Full article
(This article belongs to the Special Issue Human-Centered Artificial Intelligence—2nd Edition)
►▼ Show Figures

Graphical abstract

22 pages, 18257 KB  
Article
Deep-Learning-Based Multi-Camera Framework for Indoor Human Detection and Presence Management
by Thang Chien Vu, Dung The Nguyen, Long Quy Dinh, Mui Duc Nguyen, De Rosal Ignatius Moses Setiadi and Minh Tuan Nguyen
Future Internet 2026, 18(8), 435; https://doi.org/10.3390/fi18080435 - 13 Aug 2026
Viewed by 324
Abstract
Currently, in high-density indoor environments such as businesses and factories, managing human presence and access control remains a significant challenge. Traditional access control systems based on facial recognition or card scanning typically only record authentication events at the point of entry. Therefore, continuous [...] Read more.
Currently, in high-density indoor environments such as businesses and factories, managing human presence and access control remains a significant challenge. Traditional access control systems based on facial recognition or card scanning typically only record authentication events at the point of entry. Therefore, continuous monitoring, presence detection, or restricted area surveillance are limited. This paper proposes an integrated indoor person detection and management framework based on centralized multi-camera processing for deployment-oriented identification surveillance. The proposed framework combines SCRFD and ArcFace to perform enrollment-based face recognition and distinguish between enrolled and unknown identities. During the experimental evaluation, the facial recognition module using the SCRFD 2.5G configuration achieved a recognition accuracy of approximately 88.2%. YOLOv11n is integrated with DeepSORT to detect and continuously track individuals within the monitored area. Experimental results showed the system achieving an average processing performance of 10.6 FPS, demonstrating the feasibility of the proposed architecture for small- to medium-scale indoor surveillance applications. Additionally, this system framework integrates event-driven spatial analysis using virtual boundaries and surveillance zones to support entry/exit counting, presence monitoring, and intrusion detection in restricted areas. Experimental results demonstrate that the proposed system framework provides consistent identification monitoring performance, stable multi-object tracking capabilities, and an efficient event management mechanism for typical indoor surveillance scenarios. This work offers a centralized, deployment-oriented surveillance architecture suitable for practical indoor security management and access control applications utilizing multiple cameras. Full article
►▼ Show Figures

Figure 1

30 pages, 2004 KB  
Article
Enhanced Hybrid Retrieval-Augmented Model for Question Answering in High-Sensitivity Domains
by Ali Mohammed Aloqla, Reda Salama, Wajdi Alghamdi and Adnan Ahmed Abi Sen
Future Internet 2026, 18(8), 434; https://doi.org/10.3390/fi18080434 - 13 Aug 2026
Viewed by 323
Abstract
Arabic question-answering systems in high-sensitivity domains require not only accurate retrieval but also reliable evidence grounding and effective hallucination mitigation, as incorrect or unsupported responses may have serious consequences. Existing retrieval and generation approaches do not fully integrate reliable lexical retrieval, semantic understanding, [...] Read more.
Arabic question-answering systems in high-sensitivity domains require not only accurate retrieval but also reliable evidence grounding and effective hallucination mitigation, as incorrect or unsupported responses may have serious consequences. Existing retrieval and generation approaches do not fully integrate reliable lexical retrieval, semantic understanding, and evidence-based answer verification within a unified framework for these domains. To address this limitation, this study proposes an Enhanced Hybrid Retrieval-Augmented model that combines BM25-based lexical retrieval, dense semantic scoring, semi-structured metadata, domain-aware classification, source-trust and freshness indicators, and evidence-based answer verification. The model was evaluated on a corpus of 128,297 Arabic documents using 200 expert-validated questions and compared against BM25, Dense, and Classical Hybrid retrieval configurations. Expert assessment showed that the Enhanced Hybrid Model produced 198 fully grounded correct answers out of 200, achieving a grounded-correctness rate of 99.0%, compared with 96.5% for Classical Hybrid, 87.0% for BM25, and 86.5% for Dense. It also achieved the strongest answer-level Hit@5 performance, the best golden-answer ordering, and the highest dynamic mixture-based F1-score. These findings demonstrate that integrating reliable retrieval, metadata-aware ranking, and evidence-grounded answer verification can substantially improve the reliability and trustworthiness of Arabic question-answering systems in high-sensitivity domains, providing a practical foundation for future evidence-based intelligent information systems. Full article
(This article belongs to the Special Issue Advances in Intelligent Learning Systems)
►▼ Show Figures

Figure 1

29 pages, 716 KB  
Article
Threat Actor Attribution Applying a Tactics–Techniques–Procedures Approach: An Empirical Investigation
by Shaheen Hussain and Krassie Petrova
Future Internet 2026, 18(8), 433; https://doi.org/10.3390/fi18080433 - 13 Aug 2026
Viewed by 563
Abstract
The increasing frequency and growing impact of cyberattacks have led organizations to adopt proactive defense approaches to cybersecurity risk mitigation, especially in the case of advanced persistent threats (APTs). The correct identification of the specific malicious actors behind a cyberattack is important for [...] Read more.
The increasing frequency and growing impact of cyberattacks have led organizations to adopt proactive defense approaches to cybersecurity risk mitigation, especially in the case of advanced persistent threats (APTs). The correct identification of the specific malicious actors behind a cyberattack is important for the success of incident response and for the investigative work of the security operations center (SOC) team. This research explores the capabilities and limitations of a machine learning (ML) approach to identifying malicious actors and the threats they pose (threat actor attribution) based on the tactics, techniques, and procedures (TTP) observed in specific cybersecurity incidents and on the incident context (the geographical location and industry affiliation of the victims targeted in the attack). A large language model (LLM) was used to extract TTPs from the MITRE ATT&CK database of cybersecurity incidents. The experiments included modeling threat actor attribution using five ML algorithms: k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and naïve Bayes (NB), with different methods applied for feature selection and weighting. The results indicated that model accuracy and other performance metrics were significantly improved when the input dataset included both TTP and contextual features. The KNN and SVM models produced the best performance results; the highest classification accuracy achieved was 93.19%. The outcomes of this study may be applied by cybersecurity professionals to identify malicious actors, estimate the number and types of data points that are required to adequately attribute a cyberattack to an actor, and improve the accuracy of the classification by weighting the input dataset features. Full article
(This article belongs to the Special Issue Machine Learning and Internet of Things in Industry 4.0—2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 4839 KB  
Article
IoT-Based Automation of a Reverse-Osmosis Desalination Process in the Galápagos Islands
by José Varela-Aldás, Cristian Gallardo, Carlos Bran, Francisco Yumbla and Carolina Del-Valle-Soto
Future Internet 2026, 18(8), 432; https://doi.org/10.3390/fi18080432 - 13 Aug 2026
Viewed by 286
Abstract
Reliable drinking-water production is difficult on remote islands where brackish-water delivery is intermittent, technical personnel are scarce, and reverse-osmosis plants are manually operated. This study presents an operational characterization of an Internet of Things (IoT) retrofit deployed in Santa Cruz, Galápagos; it is [...] Read more.
Reliable drinking-water production is difficult on remote islands where brackish-water delivery is intermittent, technical personnel are scarce, and reverse-osmosis plants are manually operated. This study presents an operational characterization of an Internet of Things (IoT) retrofit deployed in Santa Cruz, Galápagos; it is not a controlled before-and-after effectiveness evaluation. An ESP32-based M5Stack Tough controller, distributed ESP-NOW sensing nodes, relay–contactor interfaces, a binary pressure permissive, and a ThingSpeak cloud layer were integrated without replacing the existing pumps and membranes. The exported primary-flow channel contained 4,603,989 numeric observations, including 500 pre-official test readings. Operational analyses used 4,603,489 numeric observations from the official monitoring period; 4,603,340 values remained after nominal-range filtering, and positive flow had a median of 12 L/min (interquartile range: 11–15 L/min). Among 332 logged high-pressure commands, 326 were preceded by a low-pressure command (98.2% unbounded command-state consistency), whereas 275 occurred within a 120 s analytical bound (82.8%). The median low-to-high command delay was 27 s (interquartile range: 11–70 s). Four organizational representatives completed a published 41-item Industry 4.0 maturity instrument before and after deployment; the self-reported overall mean was 0.26 at baseline and 1.95 post-deployment, and these results are interpreted descriptively. Energy-consumption and production data were confidential and unavailable to the authors, while water-quality variables were not measured. The contribution is therefore a long-duration, local-first legacy retrofit with auditable telemetry and explicit limitations, rather than a claim of optimized desalination performance. Full article
(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems, 3rd Edition)
►▼ Show Figures

Figure 1

26 pages, 827 KB  
Article
C-GUARD: Context-Adaptive Conformal Gating for Improving Robustness Against Evasive Windows PE Malware
by Muhammad Imran, Malik Al-Essa, Felice Franchini and Giuseppe Pirlo
Future Internet 2026, 18(8), 431; https://doi.org/10.3390/fi18080431 - 13 Aug 2026
Viewed by 376
Abstract
Machine Learning (ML)-based malware detectors perform exceptionally well on standard benchmarks, but their robustness to evasive malware remains fragile. On EMBER2024, strong tree-based models achieve high accuracy on clean test sets yet miss a large number of evasive Windows PE samples. Through SHAP-based [...] Read more.
Machine Learning (ML)-based malware detectors perform exceptionally well on standard benchmarks, but their robustness to evasive malware remains fragile. On EMBER2024, strong tree-based models achieve high accuracy on clean test sets yet miss a large number of evasive Windows PE samples. Through SHAP-based explainability, matched error comparisons, feature-family analysis, and margin diagnostics on the validation split, we identify consistent attribution and decision-margin differences between baseline true negatives and false negatives. We further show that false negatives occur at different decision depths, including both near-boundary cases and samples misclassified with high benign confidence. We propose C-GUARD, a context-adaptive conformal gated detector that preserves the optimized high-performing baseline while selectively invoking an auxiliary rescue detector on a targeted subset of baseline negative predictions. C-GUARD combines out-of-fold rescue learning to distinguish baseline false negatives from true negatives with adaptive gating under an explicit false-positive budget. A one-sided intervention rule preserves all baseline malware decisions. On the EMBER2024 standard test set, C-GUARD recovers 178 additional malware samples at the cost of 82 additional false positives. On the evasive challenge set, it recovers 17 additional evasive malware samples. Full article
(This article belongs to the Special Issue Adversarial Attacks and Cyber Security)
►▼ Show Figures

Figure 1

35 pages, 2265 KB  
Article
MIRA: Safety-Constrained Multi-Agent Reinforcement Learning for Joint Prescriptive Maintenance and Production Rescheduling in Industrial IoT
by Md. Ashraful Babu, Ali AlArjani and Mohamed Lahby
Future Internet 2026, 18(8), 430; https://doi.org/10.3390/fi18080430 - 13 Aug 2026
Viewed by 322
Abstract
Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning architecture for joint prescriptive maintenance, production rescheduling, and event-triggered communication. Machine condition was estimated from [...] Read more.
Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning architecture for joint prescriptive maintenance, production rescheduling, and event-triggered communication. Machine condition was estimated from CNC milling data using temporal convolutional models; because predictive uncertainty failed a predefined validation gate, the controller used deterministic health estimates. Evaluation covered five controllers, six simulated scenarios, and 1800 matched episodes. Relative to Graph-MAPPO, MIRA reduced operational cost by 9.38%, weighted tardiness by 28.10%, unexpected failures by 17.39%, message count by 84.98%, and transmitted data by 83.83%, while increasing on-time completion by 23.55%, without a detectable difference in corrected critical-message recall. Across the three independently trained seeds, failures, safety violations, and message count favored MIRA consistently, whereas cost and tardiness favored MIRA in two seeds. Disabling the execution shield increased safety violations from 0 to 3.56 per episode. Post-training variation in the projected-health safe-start threshold from 0.124 to 0.132 produced no safety violations and only small changes in aggregate operational outcomes. Cross-domain health transfer to PHM 2010 failed without adaptation. The results support simulator-level decision coordination, while broader replication, variable-size deployment, and factory validation remain necessary. Full article
(This article belongs to the Special Issue Distributed Intelligence for IoT and Smart Systems)
►▼ Show Figures

Figure 1

24 pages, 1334 KB  
Article
Pricing Diagnostic Value Under a Clinical Deadline: A Triage- Aware Truthful Auction for Semantic Medical-Image Transmission in Healthcare IoT
by Yongwen Liu, Rui Chen, Yaoli Xu and Kailai Zhou
Future Internet 2026, 18(8), 429; https://doi.org/10.3390/fi18080429 - 12 Aug 2026
Viewed by 270
Abstract
Telemedicine in emergency and remote care relays medical images from ambulances and rural clinics to a hospital edge-computing server over a congested wireless uplink. Existing work prices such transmissions per bit or per quality-of-experience; neither metric captures the clinical value of a medical [...] Read more.
Telemedicine in emergency and remote care relays medical images from ambulances and rural clinics to a hospital edge-computing server over a congested wireless uplink. Existing work prices such transmissions per bit or per quality-of-experience; neither metric captures the clinical value of a medical transmission. Diagnostic utility vanishes below a modality-specific acceptability floor rather than degrading gracefully, the deadline is determined by triage acuity rather than by the network, and a missed finding is far costlier than a false alarm. A per-bit clearing price therefore disadvantages the node that has expended local compute to produce a compact, diagnostically sufficient stream. We propose SemAuc, a triage-aware truthful mechanism for medical-image admission over a rate-splitting uplink, in which the shared semantic knowledge base rides the common stream, and case-specific residuals ride private streams. SemAuc filters tiers below the diagnostic floor and beyond the clinical deadline, reserves a regulated-price lane for life-threatening cases, and allocates remaining capacity through a single-parameter contestable auction whose bid-independent pre-selection step satisfies the conditions of Myerson’s lemma. The contestable lane is dominant-strategy truthful, individually rational, near-linear in the number of nodes, and achieves a constant-factor density-greedy welfare guarantee; the clinical lanes follow from triage policy without disturbing these properties. Diagnostic value is grounded by an offline kernel fitted on BraTS and CheXpert. On a Rayleigh-faded uplink at two hundred contending nodes, SemAuc preserves the high-acuity diagnostic service-level objective where bit-centric benchmarks fail, and tracks the offline optimum. Full article
►▼ Show Figures

Figure 1

27 pages, 21729 KB  
Article
Industrial Internet-Oriented Unsupervised Hydro-Turbine Bearing Fault Diagnosis via Prototype-Disentangled Conditional Wasserstein Domain Adaptation
by Xueyi Li, Binghao Hu, Jiannan Dong and Zhilin Dong
Future Internet 2026, 18(8), 428; https://doi.org/10.3390/fi18080428 - 12 Aug 2026
Viewed by 289
Abstract
With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce [...] Read more.
With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article
(This article belongs to the Topic Digital and Smart Technologies for Industry 4.0 / 5.0)
►▼ Show Figures

Figure 1

23 pages, 7009 KB  
Article
LLM-Based Semantic Navigation on a Low-Cost ROS Mobile Robot: A Hybrid Edge–Cloud Architecture
by Marius-Adrian Păun, Florin Dragomir, Otilia Elena Dragomir, Eugenia Mincă and Octavian Gabriel Duca
Future Internet 2026, 18(8), 427; https://doi.org/10.3390/fi18080427 - 12 Aug 2026
Viewed by 406
Abstract
Autonomous mobile robots require robust perception and high-level reasoning to operate in complex indoor environments. While the Robot Operating System (ROS) provides a modular framework for mapping and navigation, classical pipelines lack semantic understanding and natural-language interaction. This paper presents a semantic-aware autonomous [...] Read more.
Autonomous mobile robots require robust perception and high-level reasoning to operate in complex indoor environments. While the Robot Operating System (ROS) provides a modular framework for mapping and navigation, classical pipelines lack semantic understanding and natural-language interaction. This paper presents a semantic-aware autonomous navigation framework implemented on a ROS 1 (Melodic) mobile robot equipped with a two-dimensional light detection and ranging (LiDAR) sensor and an RGB-D camera. The system integrates LiDAR-based simultaneous localization and mapping (SLAM), the ROS navigation stack (move_base), and a lightweight You Only Look Once (YOLO) object detector for real-time on-board perception, and it anchors detections into the metric map to build a semantic map. A large language model (LLM) interprets natural-language instructions and converts them into structured navigation goals. Perception and control run entirely on-board the Jetson Nano, whereas the LLM is invoked episodically as a cloud service, yielding a hybrid embedded/cloud architecture. In indoor trials over a semantic map of two object classes, the system grounded all ten multilingual commands to the correct objects at a reasoning cost of about one second, and safely rejected a command referring to an unmapped object. We present this as an in-depth single-platform case study: owing to the 4 GB memory budget, the on-board detector and the full navigation stack are time-multiplexed rather than run continuously in parallel; nonetheless, a single degraded end-to-end trial confirmed that perception, online semantic anchoring, language grounding, and navigation compose within one continuous session. The framework offers a low-cost, extensible basis for language-guided robots in smart environments. Full article
(This article belongs to the Special Issue Mobile Robotics and Autonomous System)
►▼ Show Figures

Figure 1

20 pages, 2712 KB  
Article
An Algebra for Two-Layer Cloud Filtering: Detecting Redundancy, Shadowing, and Dominance Anomalies Across Stateless Network ACLs and Stateful Security Groups
by Thawatchai Chomsiri and Suwichai Phunsa
Future Internet 2026, 18(8), 426; https://doi.org/10.3390/fi18080426 - 11 Aug 2026
Viewed by 302
Abstract
Traffic inside a cloud Virtual Private Cloud (VPC) is filtered by two layers with fundamentally different semantics: a stateless, ordered, first-match Network ACL (NACL) and a stateful, unordered, allow-only Security Group (SG). Existing analyzers decide point-to-point reachability using satisfiability solvers, Datalog engines, or [...] Read more.
Traffic inside a cloud Virtual Private Cloud (VPC) is filtered by two layers with fundamentally different semantics: a stateless, ordered, first-match Network ACL (NACL) and a stateful, unordered, allow-only Security Group (SG). Existing analyzers decide point-to-point reachability using satisfiability solvers, Datalog engines, or binary decision diagrams, but do not identify, at the rule level, which rules are dead, redundant, or dominated, nor explain why. We provide a closed-form set algebra over the two layers. Representing each rule field by its boundaries makes a rule a hyper-rectangle and a layer a union of boxes; the effective admitted region Φ = A(N) ∩ A(G) is then a finite union of disjoint boxes computable from rule endpoints alone. We define a taxonomy of single- and cross-layer anomalies—shadowed NACL rules, dead SG rules, Φ-redundant rules, Φ-ineffective NACL allows, and layer disagreement—characterize each by a decidable region predicate, and prove an exact iff-condition for SG Φ-redundancy. A boundary-only detection algorithm is sound and complete for the exactly decidable anomaly classes, running in O((k + t)^d) time for fixed dimension d, and the disjoint box decomposition of Φ gives a minimal anomaly-free form that is unique up to merging adjacent coplanar boxes. A single-file implementation matches brute force on millions of packets, staying orders of magnitude below the worst-case bound; the parametric model extends unchanged to IPv6 and ICMP. Full article
(This article belongs to the Collection Information Systems Security)
►▼ Show Figures

Figure 1

47 pages, 9271 KB  
Review
AI-Driven Mobility Management in 5G and 6G Wireless Networks: A Survey
by Hafiz M. Asif, Abdulraqeb Alhammadi, Naser Tarhuni and Mohammed M. Bait-Suwailam
Future Internet 2026, 18(8), 425; https://doi.org/10.3390/fi18080425 - 11 Aug 2026
Viewed by 437
Abstract
Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly [...] Read more.
Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks. Full article
(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)
►▼ Show Figures

Figure 1

47 pages, 7088 KB  
Article
APCI: A Complexity-Aware Framework for Computational Software Effort Estimation in Machine Learning-Based Intrusion Detection Systems
by Vignaraj Ananth Vikraman, Sumendra Yogarayan, Kalaiarasi Sonai Muthu and Manikandan Thirumalaisamy
Future Internet 2026, 18(8), 424; https://doi.org/10.3390/fi18080424 - 11 Aug 2026
Viewed by 333
Abstract
The increasing adoption of machine learning and deep learning techniques in intrusion detection systems (IDSs) has substantially increased the computational complexity of model development due to large-scale datasets, sophisticated model architectures, extensive hyperparameter optimization, and repeated experimentation. Despite these demands, existing IDS research [...] Read more.
The increasing adoption of machine learning and deep learning techniques in intrusion detection systems (IDSs) has substantially increased the computational complexity of model development due to large-scale datasets, sophisticated model architectures, extensive hyperparameter optimization, and repeated experimentation. Despite these demands, existing IDS research primarily emphasizes detection performance while providing limited support for estimating the computational effort required during model development and evaluation. This study proposes the Adaptive Project Complexity Index (APCI), a complexity-aware framework for estimating Computational Software Effort (CSE), a computational resource-based effort metric derived from model training and execution characteristics to support planning and resource estimation in machine learning-based IDS development. To support this objective, a complexity- and effort-oriented benchmark comprising 1040 IDS project instances was constructed using diverse datasets, model architectures, feature configurations, and hyperparameter settings. Statistical analysis demonstrated a strong positive relationship between APCI and CSE, with a Pearson correlation coefficient of 0.834. Building upon this benchmark, multiple machine learning models were evaluated to predict CSE from project characteristics available before implementation, with LightGBM achieving the best predictive performance (R2 = 0.963). Furthermore, explainability analysis identified the dominant computational effort drivers and enabled the development of APCI-Adaptive, improving the APCI–CSE correlation from 0.834 to 0.904 (8.4%). By integrating dataset complexity, model complexity, and computational resource requirements within a unified framework, APCI enables computational software effort estimation prior to IDS implementation, supporting resource planning, experimental design, and complexity-aware project management for machine learning-based IDS development. Consequently, APCI provides a practical decision-support framework for resource planning, experimental design, and complexity-aware project management in modern machine learning-based cybersecurity applications. Full article
(This article belongs to the Special Issue Anomaly and Intrusion Detection in Networks)
►▼ Show Figures

Figure 1

41 pages, 8288 KB  
Article
A Reproducible Blockchain-Anchored Proof-of-Charge Platform for Auditable EV Charging Receipts
by Nexhibe Sejfuli-Ramadani, Valentina Angelkoska, Florim Idrizi, Valentin Rakovic, Erenis Ramadani and Aleksandar Risteski
Future Internet 2026, 18(8), 423; https://doi.org/10.3390/fi18080423 - 10 Aug 2026
Viewed by 393
Abstract
Public electric vehicle (EV) charging increasingly relies on internet-connected platforms for metering, billing, roaming, and settlement. However, final billing records and charge detail records often provide limited evidence that a session result can be independently linked to the ordered metering data from which [...] Read more.
Public electric vehicle (EV) charging increasingly relies on internet-connected platforms for metering, billing, roaming, and settlement. However, final billing records and charge detail records often provide limited evidence that a session result can be independently linked to the ordered metering data from which it was derived. This paper presents a reproducible blockchain-anchored Proof-of-Charge platform for generating tamper-evident and auditable EV charging receipts. The platform converts charging-session data into canonical receipts, computes cryptographic commitments over receipt content and ordered meter values, aggregates receipt hashes using a temporally ordered and domain-separated Merkle profile, and anchors compact batch commitments in a smart contract while keeping detailed records off-chain. A working prototype implements cross-language canonicalization checks, membership-proof generation, structured storage, batch anchoring, verification services, synthetic workload generation, dataset export, and local blockchain deployment. Across 50 measured runs covering 10 to 1000 receipts, all count reconciliations, batch-root checks, and on-chain comparisons passed. Mean receipt-pipeline latency ranged from 7.152 to 7.853 ms per receipt, with throughput from 127.54 to 141.23 receipts/s. A focused 1000-leaf proof sample produced a 2064-byte proof with ten sibling hashes. The results show that the platform can generate, anchor, and verify auditable EV charging receipts with reproducible performance while keeping detailed charging data off-chain. The proposed architecture provides a practical digital trust layer for internet-enabled EV charging and V2G-ready settlement workflows. Full article
(This article belongs to the Topic Blockchain for Sustainable Supply Chains: Enhancing Transparency from Producer to Consumer)
►▼ Show Figures

Figure 1

38 pages, 4711 KB  
Article
Explainable Multi-Objective Quantum-Inspired Fuzzy Optimization of Rule Bases for Scalable Load Balancing in Multi-Factor Computing Environments
by Akmal Akhatov, Maruf Tojiyev, Jura Kuvandikov, Sanjar Kenjaev, Dilmurod Khasanov, Abdutolib Parmonov, Oybek Primqulov, Odil Shaymatov and Farkhod Akhmedov
Future Internet 2026, 18(8), 422; https://doi.org/10.3390/fi18080422 - 10 Aug 2026
Viewed by 382
Abstract
The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy [...] Read more.
The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy logic is an effective tool for modeling such uncertainty; however, the expansion of linguistic variables often leads to a rule-explosion problem, which increases computational complexity and reduces the real-time applicability of fuzzy load-balancing systems. This study proposes an explainable multi-objective quantum-inspired fuzzy optimization approach for scalable load balancing in complex computing environments. The proposed model integrates fuzzy inference with a Grover-inspired classical search strategy to optimize the selection of fuzzy rule subsets. The Grover-inspired component is implemented as a classical simulation rather than a gate-based quantum circuit. A multi-objective evaluation function is formulated to jointly assess rule accuracy, coverage, interpretability, and compactness. This formulation enables the model to reduce redundant fuzzy rules while preserving decision transparency and maintaining reliable load distribution performance. The proposed approach is evaluated in a simulated cloud computing environment with heterogeneous servers and dynamic request arrival patterns. Comparative experiments are conducted against classical load-balancing strategies, conventional fuzzy load balancing, and evolutionary fuzzy optimization methods, including GA-FLB and PSO-FLB. The experimental results show that the proposed model reduces the size of the fuzzy rule base while maintaining competitive response time, load distribution quality, SLA compliance, and decision interpretability. These findings indicate that the integration of Grover-inspired classical search mechanisms with fuzzy reasoning provides a promising direction for developing scalable, compact, and explainable load-balancing models for next-generation intelligent computing systems. Full article
►▼ Show Figures

Figure 1

18 pages, 1133 KB  
Article
Bimodal Speech Emotion Recognition Using a Hybrid CNN-LSTM Architecture with Sentiment Fusion
by Tze-Syn Yap and Lee-Yeng Ong
Future Internet 2026, 18(8), 421; https://doi.org/10.3390/fi18080421 - 10 Aug 2026
Viewed by 260
Abstract
Speech emotion recognition (SER) is a fundamental task in affective computing; however, traditional unimodal approaches often struggle to capture the complex emotional cues present in spontaneous conversational speech. Bimodal frameworks that integrate acoustic and textual information have therefore emerged to provide complementary semantic [...] Read more.
Speech emotion recognition (SER) is a fundamental task in affective computing; however, traditional unimodal approaches often struggle to capture the complex emotional cues present in spontaneous conversational speech. Bimodal frameworks that integrate acoustic and textual information have therefore emerged to provide complementary semantic and acoustic representations. This study proposes a bimodal SER framework based on a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture. Using the Multimodal EmotionLines Dataset (MELD), the framework combines temporal acoustic features, statistical acoustic features, and predicted textual sentiment. Experimental results indicate that the proposed model achieves reliable recognition of majority emotion classes but exhibits limited performance on underrepresented minority classes due to severe class imbalance. To better understand the contribution of each modality, feature sufficiency and feature necessity analyses were conducted. Furthermore, an evaluation of alternative fusion strategies showed that the expressive attention networks did not provide meaningful performance improvements over simple feature concatenation. These findings suggest that class imbalance, rather than fusion complexity, remains the primary limitation in conversational SER, highlighting the importance of addressing data imbalance before pursuing more sophisticated multimodal architectures. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Natural Language Processing (NLP))
►▼ Show Figures

Graphical abstract

17 pages, 1789 KB  
Article
Bandwidth-Efficient Transmission of HRV Features Using PhysioNet ECG Data for IoT-Based Wearable Health Monitoring
by Naoya Morikawa and Emi Yuda
Future Internet 2026, 18(8), 420; https://doi.org/10.3390/fi18080420 - 9 Aug 2026
Viewed by 393
Abstract
In IoT health monitoring using electrocardiograms (ECGs), the surge in data transmission volume poses a significant challenge. This study utilized PhysioNet ECG data to compare the transmission volumes of raw ECG signals, R-R intervals (RRIs), and HRV metrics (SDNN, RMSSD, and LF/HF), thereby [...] Read more.
In IoT health monitoring using electrocardiograms (ECGs), the surge in data transmission volume poses a significant challenge. This study utilized PhysioNet ECG data to compare the transmission volumes of raw ECG signals, R-R intervals (RRIs), and HRV metrics (SDNN, RMSSD, and LF/HF), thereby evaluating the effectiveness of communication optimization. The results demonstrated that transmitting RRI data and HRV metrics reduced data volume by approximately 99% and over 99.9%, respectively, compared to transmitting raw ECG data. These results suggest that the proposed approach could contribute to improved energy efficiency and reduced transmission latency in wearable devices, supporting its potential feasibility for bandwidth-constrained IoMT deployments. Full article
(This article belongs to the Special Issue The Future Internet of Medical Things, 3rd Edition)
►▼ Show Figures

Figure 1

29 pages, 400 KB  
Review
AI Agents and the Progressive Intermediation Between Users and Online Services: A Critical Analysis
by Marco Rondina, Antonio Vetrò, Juan Carlos De Martin, Manuela Bargis and Gabriele Elia
Future Internet 2026, 18(8), 419; https://doi.org/10.3390/fi18080419 - 8 Aug 2026
Viewed by 473
Abstract
The disruptive potential of AI agents is becoming increasingly evident, impacting the very nature of the interactions between users and online services and content. Representing a new form of intermediation, these agents are transforming the way users access and interact with online resources, [...] Read more.
The disruptive potential of AI agents is becoming increasingly evident, impacting the very nature of the interactions between users and online services and content. Representing a new form of intermediation, these agents are transforming the way users access and interact with online resources, while also reshaping the distribution of informational power, economic value, and access in the digital ecosystem. This paper analyses the evolution of intermediation mechanisms on the internet through a critical, non-systematic review guided by Critical Systems Heuristics. We trace the progressive layering of intermediation mechanisms to online content/services from the technical infrastructures, and we argue that AI agents constitute a structurally distinct intermediation layer that introduces new dynamics of power, opacity, value distribution, and access to the digital ecosystem, with significant implications for users, online services, and the broader society. We are currently crossing a potentially significant evolutionary shift in the Internet, where AI agent intermediation could progressively exert a substantial influence on how information is exchanged. This paper offers a framework with which to discuss the implications of this potential evolution and to guide future research, public discussion, and policy interventions in this area. Full article
►▼ Show Figures

Figure 1

30 pages, 9149 KB  
Article
Assessment of Fine Motor Skills Using Digital Serious Games: A Comparative Study with the Conventional Box and Block Test
by Dimitrios N. Soumis and Nikolaos D. Tselikas
Future Internet 2026, 18(8), 418; https://doi.org/10.3390/fi18080418 - 7 Aug 2026
Viewed by 423
Abstract
The assessment and rehabilitation of fine motor skills are essential in clinical practice, particularly for individuals with neurological or musculoskeletal impairments. The Box and Block Test (BBT) is a widely used standard tool; however, it lacks digital capabilities for automated measurement and remote [...] Read more.
The assessment and rehabilitation of fine motor skills are essential in clinical practice, particularly for individuals with neurological or musculoskeletal impairments. The Box and Block Test (BBT) is a widely used standard tool; however, it lacks digital capabilities for automated measurement and remote monitoring. This study presents two digital serious games designed to assess and support fine motor skills. The first game is a direct digital adaptation of the BBT, while the second mimics its core movements while introducing enhanced interaction features and flexibility. A total of 50 participants performed the conventional BBT and both digital games. The present evaluation represents a preliminary validation in healthy adults and provides a foundation for future clinical studies involving individuals with motor impairments. Performance metrics were collected and analyzed to compare outcomes and examine correlations between real-world and digital assessments. The results indicate significant correlations between BBT scores and performance in both digital serious games, supporting their validity as assessment tools. Additionally, the second game provided richer interaction data, suggesting potential for more comprehensive evaluation. These findings highlight the potential of serious games as reliable and engaging tools for fine motor assessment and rehabilitation in both clinical and remote settings. Full article
(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction—2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 1490 KB  
Article
Wi-CSNet: A Spatio-Temporal Model for CSI-Based Human Activity Recognition
by Zhongjian Gao, Ruige Zhang, Yuwei Cai, Lianhui Zheng, Han Yang and Yao Li
Future Internet 2026, 18(8), 417; https://doi.org/10.3390/fi18080417 - 6 Aug 2026
Viewed by 258
Abstract
Human Activity Recognition (HAR) based on Channel State Information (CSI) has attracted considerable attention as a privacy-preserving sensing paradigm. However, CSI-based HAR faces several challenges, including environmental noise, long-range temporal dependencies, and the anisotropic structure of CSI tensors. To address these challenges, this [...] Read more.
Human Activity Recognition (HAR) based on Channel State Information (CSI) has attracted considerable attention as a privacy-preserving sensing paradigm. However, CSI-based HAR faces several challenges, including environmental noise, long-range temporal dependencies, and the anisotropic structure of CSI tensors. To address these challenges, this paper presents Wi-CSNet, a lightweight CSI-oriented framework that integrates Discrete Wavelet Transform (DWT) preprocessing, asymmetric-stride convolutions, and a Cross-Scanning State Space Duality (CS-SSD) block derived from Mamba2. DWT preprocessing compresses temporal signals while preserving motion-related trends and reducing input dimensionality. Asymmetric-stride convolutions balance feature scales across heterogeneous CSI dimensions, while the lightweight CS-SSD module captures global dependencies with only a 0.63% parameter overhead. Extensive experiments demonstrate that Wi-CSNet achieves accuracies of 97.81% on HHI, 99.92% on UT-HAR, and 100% on NTU-HAR. These results confirm the effectiveness and robustness of Wi-CSNet for fine-grained CSI-based activity recognition in complex environments. Full article
(This article belongs to the Section Internet of Things)
►▼ Show Figures

Figure 1

17 pages, 599 KB  
Article
Comparative Performance Evaluation of Six Federated Learning Frameworks Under Locked FedAvg: Native SDKs and a Shared Reference Harness for Edge-Oriented 6G Applications
by Vasileios D. Batsios and Constantinos T. Angelis
Future Internet 2026, 18(8), 416; https://doi.org/10.3390/fi18080416 - 6 Aug 2026
Viewed by 283
Abstract
Federated learning (FL) enables privacy-preserving collaborative training at the network edge, a core capability envisioned for sixth-generation (6G) wireless systems. While surveys and scale-oriented benchmarks advance FL methodology, documented, head-to-head comparisons of mainstream Python frameworks under identical FedAvg settings remain scarce. We benchmark [...] Read more.
Federated learning (FL) enables privacy-preserving collaborative training at the network edge, a core capability envisioned for sixth-generation (6G) wireless systems. While surveys and scale-oriented benchmarks advance FL methodology, documented, head-to-head comparisons of mainstream Python frameworks under identical FedAvg settings remain scarce. We benchmark six frameworks—Flower, TensorFlow Federated (TFF), FedML, NVIDIA FLARE, OpenFL, and PySyft—distinguishing two native SDK integrations (Flower, TFF) from four runs of a shared PyTorch FedAvg reference harness (FedML, NVIDIA FLARE, OpenFL, PySyft) in a controlled two-phase study on a Proxmox virtualized testbed with containerized runners, formalize the FedAvg objective and communication-cost model, and position our contribution against prior surveys, scale benchmarks, and single-framework documentation. Each framework–dataset pair is repeated over five IID partitions (random seeds 42–46); we report round-10 mean ± standard deviation for accuracy, wall time, and simulated communication volume. Phase 1 (MNIST) confirms protocol fairness (99.22±0.07–99.29±0.06% accuracy) with moderate wall-time spread; Phase 2 (CIFAR-10) exposes stack-dependent accuracy gaps (TFF 71.16±0.23% vs. ≈68% for PyTorch runners). We report per-round accuracy and loss curves with variability bands, wall-time comparisons, and simulated parameter traffic for all six frameworks across nine figures. The experimental protocol, model topology, and hyperparameters are specified in full; per-round JSON metrics and global model checkpoints are published. The study provides a documented baseline for 6G edge framework selection and for follow-on network-constrained and security experiments. Full article
(This article belongs to the Special Issue 5G/6G and Beyond: The Future of Wireless Communications Systems)
►▼ Show Figures

Figure 1

26 pages, 648 KB  
Article
SDN-Enabled Digital Twin Networks Architecture for Proactive Detection and Mitigation of Malicious Encrypted Traffic
by Xavier Hesselbach and Juan Parada Claro
Future Internet 2026, 18(8), 415; https://doi.org/10.3390/fi18080415 - 5 Aug 2026
Viewed by 491
Abstract
Classification algorithms for encrypted traffic are an effective means of improving the security of private networks. However, they operate a posteriori because they require collecting flow level features (packet length, inter-arrival time, and flow identifiers), which can only be extracted after observing multiple [...] Read more.
Classification algorithms for encrypted traffic are an effective means of improving the security of private networks. However, they operate a posteriori because they require collecting flow level features (packet length, inter-arrival time, and flow identifiers), which can only be extracted after observing multiple packets before reaching a classification decision. Furthermore, to achieve accurate classification, the analyzed flows must be processed by the target server or by a server that faithfully replicates its behavior. This paper proposes a Digital Twin Network (DTN) architecture to prevent unknown traffic from being initially processed by the Original Network, thereby reducing the risk of attacks that cannot be immediately detected by encrypted traffic classification. The architecture integrates SDN and P4 to implement the monitoring and traffic redirection required by the DTN, and a proof-of-concept prototype is presented to validate its functionality. This work identifies a trade-off regarding access to previously unknown external services due to redirection via the DTN until classification is complete, which can introduce significant delays. Full article
(This article belongs to the Special Issue Anomaly and Intrusion Detection in Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-63
Previous Issue
Next Issue
Back to TopTop