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 16.1 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second 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)
Latest Articles
Executable Trust: A Formal Model and Architecture for Verifiable Digital Interactions
Future Internet 2026, 18(6), 321; https://doi.org/10.3390/fi18060321 (registering DOI) - 12 Jun 2026
Abstract
Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies support the correctness of individual components, they do not by themselves establish that an interaction as a whole is
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Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies support the correctness of individual components, they do not by themselves establish that an interaction as a whole is trustworthy. This limitation arises because real-world interactions consist of sequences of dependent steps, where inconsistencies may arise even when each step is locally valid. In this paper, we introduce the concept of executable trust, which models trust as a verifiable property of execution across complete interaction sequences. We formalize interactions as chains of TrustEvidence objects that capture step-level validity, constraint satisfaction, and cross-step dependencies. Based on this model, we show that step-level correctness alone is insufficient to characterize interaction-level trust under the stated execution assumptions. We further clarify the definition-induced modular structure of interaction-level trust and use a local failure-witness characterization to connect the formal model with scenario-based validation. We also present the Executable Trust Architecture (ETA), a five-layer architecture that operationalizes the proposed model through components for evidence generation, constraint enforcement, secure communication, and auditability. The feasibility of the approach is examined through scenario-based evaluation covering key trust properties—authenticity, integrity, privacy, and accountability—across nine scenarios comprising 68 test cases. The evaluation illustrates cases in which cross-step violations that pass conventional step-level verification are reflected as failures of ETA’s sequence-aware trust conditions under the evaluated assumptions.
Full article
(This article belongs to the Section Cybersecurity)
Open AccessArticle
Experimental Evaluation and Performance Analysis of 5G NSA Networks
by
Vasileios D. Batsios, Spiridoula V. Margariti, Constantinos T. Angelis and Eleftherios Stergiou
Future Internet 2026, 18(6), 320; https://doi.org/10.3390/fi18060320 - 12 Jun 2026
Abstract
5G technology was introduced in 2019 with the aim of transforming digital connectivity, enabling a new generation of communication capabilities, such as significantly faster mobile broadband, highly reliable low-latency links, and the capacity to support vast IoT deployments. However, the expected improvements promised
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5G technology was introduced in 2019 with the aim of transforming digital connectivity, enabling a new generation of communication capabilities, such as significantly faster mobile broadband, highly reliable low-latency links, and the capacity to support vast IoT deployments. However, the expected improvements promised by 5G technology do not seem to be reflected in actual usage. This study aims to address the issue of the real-world usage of 5G telecommunications networks and compare it with the theoretical specifications of the network as officially published by 3GPP. Specifically, the focus will be on the evaluation of the implementation of the 5G network in northwestern Greece, which operates in Non-Standalone (NSA) mode as of the date of this study’s completion. 5G Standalone (SA) networks were not available for public testing in this region during the data collection period. The analysis focuses on key performance indicators, including throughput, latency, stability, and coverage, to assess how effectively current deployments meet the expectations set by 5G standards. Results show that while 5G delivers notable improvements in peak data rates and latency, several practical limitations persist. NSA deployments remain constrained by their dependence on 4G infrastructure, resource sharing between LTE and 5G components affects performance under high-load conditions, and inconsistent coverage leads to significant variability in user experience. These findings highlight the gap between theoretical capabilities and operational performance, offering insights that can guide future network optimization and inform the transition toward 5G Standalone (SA) architectures.
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(This article belongs to the Special Issue 5G/6G and Beyond: The Future of Wireless Communications Systems)
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Open AccessReview
Secure V2X Communication in the Quantum Era: A Survey of Post-Quantum Authentication and Key Agreement (AKA) Protocols for Autonomous Vehicles
by
Weiqi Wang and Soo Fun Tan
Future Internet 2026, 18(6), 319; https://doi.org/10.3390/fi18060319 - 11 Jun 2026
Abstract
Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the
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Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era.
Full article
(This article belongs to the Special Issue Future Industrial Networks: Technologies, Algorithms, and Protocols)
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Open AccessArticle
Enhancing Respiratory Disease Diagnosis with AI Lung Sound Analysis: A Web-Based Approach
by
Reshma Sreejith, R. Kanesaraj Ramasamy, Wan-Noorshahida Mohd-Isa and Junaidi Abdullah
Future Internet 2026, 18(6), 318; https://doi.org/10.3390/fi18060318 - 11 Jun 2026
Abstract
Accurate and timely diagnosis of respiratory diseases remains a critical challenge in clinical practice, particularly in resource-limited and remote healthcare settings. This study proposes a web-based automated respiratory disease classification system leveraging a hybrid Convolutional Neural Network–Long Short-Term Memory with Time-Distributed (CNN-LSTM-TD) architecture
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Accurate and timely diagnosis of respiratory diseases remains a critical challenge in clinical practice, particularly in resource-limited and remote healthcare settings. This study proposes a web-based automated respiratory disease classification system leveraging a hybrid Convolutional Neural Network–Long Short-Term Memory with Time-Distributed (CNN-LSTM-TD) architecture for lung sound analysis. The proposed model integrates three complementary time-frequency representations—Mel-Frequency Cepstral Coefficients (MFCCs), Mel-spectrograms, and Chroma Short-Time Fourier Transform (Chroma-STFT)—to comprehensively capture both local spectral characteristics and long-range temporal dependencies inherent in respiratory cycles. Specifically, the TimeDistributed CNN block extracts localised acoustic features from sequential frames, while the LSTM layer models their temporal evolution, enabling robust identification of pathological acoustic signatures such as wheezes and crackles. The model was rigorously evaluated on the benchmark ICBHI 2017 dataset across six diagnostic categories: healthy, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, upper respiratory tract infection (URTI), and bronchiectasis. The CNN-LSTM-TD model achieved an F1-score of 0.94, recall of 0.91, precision of 0.97, overall accuracy of 96.40%, and an AUC-ROC of 0.96, significantly outperforming standalone CNN, LSTM, and CNN-LSTM baseline models. The accompanying web interface supports audio file upload, real-time visualisation of waveforms and spectrograms, and confidence score reporting, collectively facilitating clinical decision support and telemedicine integration. These results demonstrate that the synergy of temporally aware deep feature extraction and accessible web deployment positions the proposed system as a clinically viable, scalable tool for automated respiratory disease diagnosis and remote patient monitoring.
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(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)
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Open AccessArticle
A Load-Aware Task Offloading Method for Mobile Edge Computing Under Eligibility Constraints
by
Yarong Liu, Zijian Che and Xiaolan Xie
Future Internet 2026, 18(6), 317; https://doi.org/10.3390/fi18060317 - 10 Jun 2026
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Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is
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Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is restrictive in heterogeneous MEC scenarios with task-node eligibility constraints. Under such constraints, a task can be processed by an edge server only when task attributes, service requirements, link conditions, and node states jointly satisfy the corresponding eligibility conditions. The feasible action set therefore varies over time, while offloading decisions are further coupled with edge-node-side queue competition and long-term load evolution. To address this problem, this paper proposes Resource-oriented Scheduling Coordination (RoSCo), a load-aware task offloading method with scheduling-level constraint handling for eligibility-constrained MEC systems. In this paper, scheduling coordination refers to the joint use of feasible-action control, priority-aware edge-node service-order modeling, and load-responsive feedback within the task offloading decision process; it does not denote inter-server communication, task aggregation, federated model aggregation, or a distributed coordination protocol. RoSCo constructs a dynamic feasible action set, applies eligibility-aware action masking to exclude infeasible offloading actions, incorporates priority-aware edge-node service-order information to characterize queueing competition among heterogeneous tasks, and designs a load-responsive reward to guide congestion mitigation and load balancing. A dueling double deep Q-network (D3QN) is adopted as the value-learning backbone, while the main methodological contribution lies in embedding task-specific feasible-action control, priority-aware node-side queue information, and load-responsive feedback into the constrained offloading process. Simulation results show that RoSCo reduces the task drop rate and edge-node load imbalance while maintaining competitive task completion delay and energy consumption, especially under high-load and sparse-eligibility conditions.
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Open AccessArticle
Evaluating Post-Quantum Cryptography in IoT Networks: Communication, Fragmentation, and Reliability
by
Eric Sakk, Guobin Xu, Jianzhou Mao and Shuangbao Wang
Future Internet 2026, 18(6), 316; https://doi.org/10.3390/fi18060316 - 10 Jun 2026
Abstract
Post-quantum cryptographic (PQC) algorithms are being developed to guard against quantum-computing attacks, but their behavior in constrained Internet of Things (IoT) environments remains an important topic of discussion. In this work, we study the impact of deploying PQC protocols in IoT networks using
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Post-quantum cryptographic (PQC) algorithms are being developed to guard against quantum-computing attacks, but their behavior in constrained Internet of Things (IoT) environments remains an important topic of discussion. In this work, we study the impact of deploying PQC protocols in IoT networks using the Open Quantum Safe (liboqs) framework. In particular, key encapsulation and digital signature schemes are evaluated in terms of their computational performance, communication costs, and energy consumption. Our results indicate that although PQC operations can be completed in microseconds using general-purpose processors, substantially larger key and ciphertext sizes introduce significant communication overhead. When mapped to common IoT protocols such as Bluetooth Low Energy (BLE), IEEE 802.15.4 (Zigbee), and LoRa, these larger payloads must be divided into multiple packets. In low-payload LoRa networks, for example, ML-KEM handshakes can require up to 62 packets. This level of fragmentation increases latency and energy consumption, thus potentially affecting reliability. Furthermore, when packet delivery probabilities approaching 99% are achieved, handshake success rates can drop to values approaching 50%. These results suggest that communication metrics, rather than computational performance, pose key challenges to PQC deployment in constrained IoT settings.
Full article
(This article belongs to the Special Issue Cyber Security in the New “Edge Computing and IoT” World, 2nd Edition)
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Open AccessArticle
Joint Optimization of Task Offloading and Image–Container Caching Based on Hierarchical Multi-Agent Reinforcement Learning in Containerized MEC Networks
by
Zihan Xu and Chengqun Wang
Future Internet 2026, 18(6), 315; https://doi.org/10.3390/fi18060315 - 10 Jun 2026
Abstract
Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an
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Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an offloaded task can be executed only when the required runtime container is active, and a newly activated container must be supported by a locally cached service image. This dependency couples task placement, runtime container caching, and persistent image caching under limited RAM and ROM resources. To address this challenge, this paper proposes HAM-MADDPG, a dependency-aware hierarchical action-masked multi-agent reinforcement learning algorithm for joint task offloading and image–container caching in containerized MEC networks. HAM-MADDPG decomposes the monolithic orchestration decision into three causally ordered policy layers: task offloading, runtime container caching, and persistent image caching. Each layer learns a structured subproblem conditioned on upstream realized decisions, while dynamic action masking and feasibility-aware action realization guide the learned policies toward executable decisions satisfying task–container and container–image constraints. Extensive simulations under dynamic service demands and heterogeneous edge resources show that HAM-MADDPG achieves more stable convergence than non-hierarchical reinforcement learning baselines and reduces long-term system latency by approximately 14–25% compared with representative heuristic and flat DRL baselines.
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(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
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Open AccessArticle
A Mixed-Reality Approach to Cardiovascular Anatomy Education
by
Shantanu Patil, Virinchi Lalwani, Bahar Uddin Mahmud, Steven M. Carr, Jade Woodcock, Kelsey Grellinger and Guan Yue Hong
Future Internet 2026, 18(6), 314; https://doi.org/10.3390/fi18060314 - 9 Jun 2026
Abstract
Mixed-reality (MR) technologies have enhanced anatomy education through immersive three-dimensional visualization; however, most existing systems lack tutoring capabilities that respond contextually during anatomical exploration. This paper presents a reproducible MR anatomy learning platform implemented on the Apple Vision Pro that integrates the open-source
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Mixed-reality (MR) technologies have enhanced anatomy education through immersive three-dimensional visualization; however, most existing systems lack tutoring capabilities that respond contextually during anatomical exploration. This paper presents a reproducible MR anatomy learning platform implemented on the Apple Vision Pro that integrates the open-source Z-Anatomy atlas, with cardiovascular anatomy as the case domain. The system supports interactive exploration through hand gestures and eye tracking, alongside natural-language voice interaction. To provide context-grounded tutoring, we incorporate a retrieval-augmented generation (RAG) voice assistant whose responses are bounded by the Terminologia Anatomica knowledge base and weighted by the learner’s current spatial focus, with spatially anchored labels supporting contextual understanding. The platform was profiled on Apple Vision Pro hardware using Xcode Instruments and exercised through scenario-based walkthroughs of representative anatomical exploration tasks; the system met its real-time interaction and rendering thresholds across eight integrated anatomical systems. By leveraging open-source content and a substitutable AI backend, the architecture reduces software-licensing and development costs by an estimated one to two orders of magnitude relative to comparable proprietary systems and ports across XR platforms via a single bridge layer.
Full article
(This article belongs to the Special Issue Virtual Reality and Metaverse: Impact on the Digital Transformation of Society—3rd Edition)
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Measuring Baseline Web Security Posture: Tier-1 HTTP Security Header Adoption in the Maldives and the WSHS-B Governance Metric
by
Leela Waheed and Ammar Alazab
Future Internet 2026, 18(6), 313; https://doi.org/10.3390/fi18060313 - 9 Jun 2026
Abstract
Baseline web security configuration is a critical but often overlooked component of national cybersecurity posture, particularly in small and developing digital economies. This study presents a systematic empirical assessment of Tier-1 HTTP security header adoption across 51 Maldivian government and public limited company
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Baseline web security configuration is a critical but often overlooked component of national cybersecurity posture, particularly in small and developing digital economies. This study presents a systematic empirical assessment of Tier-1 HTTP security header adoption across 51 Maldivian government and public limited company (PLC) websites, benchmarked against 20 internationally recognised secure domains. To enable reproducible, policy-relevant evaluation, this paper introduces the Website Security Header Score–Benchmark (WSHS-B), a normalised metric quantifying deployment of three foundational browser-enforced controls: Strict-Transport-Security, X-Frame-Options, and X-Content-Type-Options. Only 11.8% of evaluated Maldivian websites implement all three Tier-1 headers; 39.2% implement none and 49.0% partially comply. Benchmark domains achieve near-complete adoption (mean WSHS-B = 0.92) versus 0.35 for Maldivian websites, a gap of 57 percentage points. Mann–Whitney U tests confirm statistically significant differences between groups (p < 0.001), with large rank-based effect sizes (Cliff’s δ ≈ 0.80). Unlike practitioner-facing tools such as Mozilla Observatory and SecurityHeaders.com, which produce composite site-level grades, WSHS-B is purpose-built for population-level governance monitoring through binary, policy-enforceable indicators. To the authors’ knowledge, this is the first nationally scoped empirical baseline of Tier-1 header adoption in the Maldives and the first governance-aligned metric of its kind for small digital economies. The findings provide an evidence base for NCSS 2024–2029 implementation, including mandatory baseline standards, automated compliance monitoring, and targeted capacity development. The methodology is replicable across comparable Small Island Developing States using passive, open-source scanning.
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(This article belongs to the Section Cybersecurity)
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A Batch-Based VNF Deployment Mechanism for Privacy-Preserving Multi-Domain SFC Deployment Using Deep Reinforcement Learning
by
Arif Indra Irawan and Yukinobu Fukushima
Future Internet 2026, 18(6), 312; https://doi.org/10.3390/fi18060312 - 8 Jun 2026
Abstract
Future 6G networks require higher performance and wider service coverage. Multi-domain Service Function Chain (SFC) deployment enables service provisioning across multiple network domains to meet these demands. However, when collaboration occurs among different network operators, privacy-preserving mechanisms are required to protect sensitive information
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Future 6G networks require higher performance and wider service coverage. Multi-domain Service Function Chain (SFC) deployment enables service provisioning across multiple network domains to meet these demands. However, when collaboration occurs among different network operators, privacy-preserving mechanisms are required to protect sensitive information such as internal topology and resource availability. Existing SIRM-based mechanisms, such as the Privacy-Preserving Deployment Mechanism (PPDM), address this challenge but suffer from structural limitations: PPDM performs whole-chain feasibility evaluation with extensive virtual occupation. This paper proposes a -Batch Sequential Deployment mechanism for privacy-preserving multi-domain SFC deployment. Instead of evaluating whole-chain feasibility at once, the proposed -Batch mechanism partitions each incoming SFC into fixed-size VNF batches and constructs a batch-level SIRM. This design confines virtual occupation to the current batch and reduces both its magnitude and duration while remaining fully compatible with the SIRM privacy model and the hierarchical multi-domain control architecture. A Deep Q-Network (DQN) is employed to learn substrate node selection policies based solely on SIRM-based state information, without exposing domain-internal topology or resource details. Simulation results on a three-domain AARNET substrate topology demonstrate that the proposed mechanism consistently improves deployment robustness under varying traffic intensities and SFC lengths, including short (3–6 VNFs), medium (6–9 VNFs), and long (9–12 VNFs) service chains. Compared with PPDM, the proposed -Batch mechanism achieves higher acceptance ratios under moderate-to-heavy traffic while reducing end-to-end delay and improving average substrate resource utilization. Node selection analysis further shows that smaller batch sizes preserve feasibility through compact node reuse, whereas larger batch sizes encourage broader substrate exploration. Overall, the proposed -Batch mechanism enhances feasibility preservation and deployment robustness in privacy-preserving multi-domain SFC orchestration.
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(This article belongs to the Special Issue Software-Defined Networking and Network Function Virtualization)
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Topology-Aware Joint Control Plane Placement and Assignment for Resilient Hierarchical Cloud–Edge Networks
by
Samer Mohammed Rasool, Yassine Boujelben and Faouzi Zarai
Future Internet 2026, 18(6), 311; https://doi.org/10.3390/fi18060311 - 8 Jun 2026
Abstract
Hierarchical cloud–edge networks rely on distributed control planes to manage large-scale heterogeneous infrastructures, where controller placement and node assignment strongly affect latency, load balancing, and resilience. Existing methods typically decouple these decisions and provide limited guarantees under controller failures or topology constraints. We
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Hierarchical cloud–edge networks rely on distributed control planes to manage large-scale heterogeneous infrastructures, where controller placement and node assignment strongly affect latency, load balancing, and resilience. Existing methods typically decouple these decisions and provide limited guarantees under controller failures or topology constraints. We introduce a topology-aware joint optimization framework for controller placement and node assignment in hierarchical cloud–edge networks. The problem is formulated as a multi-objective integer linear program capturing latency, load balancing, and control continuity. To ensure scalability, we design a two-phase heuristic: structurally important controller candidates are selected using graph-based metrics, including node degree and k-core decomposition, followed by a redundancy-aware proximity assignment strategy that preserves connectivity under single-controller failures. Experiments on synthetic hierarchical and random topologies with up to 500 nodes show that the proposed approach achieves optimality gaps below 10% with execution times under 10 ms. It improves load distribution and reduces control latency compared to baseline methods while maintaining resilience under controller failures. Results show that exploiting topological structure in joint placement and assignment enables efficient and resilient control plane design for hierarchical cloud–edge networks, supporting near-real-time reconfiguration.
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(This article belongs to the Special Issue Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for a Hyperconnected World)
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Open AccessArticle
TLS-Aware Anomaly Detection for Encrypted IoT Traffic Using a β-Variational Autoencoder with ANOVA–Mutual Information Feature Selection
by
Muhammad Nouman, Raja Ujjan and Muhsin Hassanu
Future Internet 2026, 18(6), 310; https://doi.org/10.3390/fi18060310 - 8 Jun 2026
Abstract
The rapid growth of the Internet of Things (IoT) has increased dependency on Transport Layer Security (TLS) for securing device communications, enhancing confidentiality while reducing the visibility required by traditional intrusion detection systems. As payload inspection becomes impractical in encrypted environments, anomaly detection
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The rapid growth of the Internet of Things (IoT) has increased dependency on Transport Layer Security (TLS) for securing device communications, enhancing confidentiality while reducing the visibility required by traditional intrusion detection systems. As payload inspection becomes impractical in encrypted environments, anomaly detection must instead rely on flow-level statistics and TLS metadata. This is challenging because IoT traffic is heterogeneous, non-stationary, and distributionally inconsistent across datasets, while many existing studies rely on single-dataset evaluation and therefore provide limited evidence of real-world generalisation. We introduce a TLS-aware anomaly detection framework that combines a -Variational Autoencoder ( -VAE) with a hybrid ANOVA–Mutual Information (ANOVA–MI) feature-selection pipeline. The incremental contribution lies not in the individual use of these components, but in their integrated application to encrypted IoT anomaly detection under strict cross-dataset evaluation, where feature filtering, probabilistic latent regularisation, and threshold transferability are jointly examined without retraining or recalibration on target datasets. The framework models benign encrypted IoT traffic using probabilistic latent representations and identifies anomalies through reconstruction-error-based scoring. Network flows from the BoT-IoT, IoT-23, and ToN-IoT datasets were processed using Zeek and CICFlowMeter to construct a unified metadata feature space incorporating flow statistics and TLS attributes such as JA3 and JA3S fingerprints. The model was trained on benign BoT-IoT traffic and evaluated in both in-dataset and cross-dataset scenarios. The model achieves strong in-dataset performance on BoT-IoT (ROC-AUC ; F1 ) and retains robust anomaly-ranking and threshold-based detection capability under cross-dataset domain shift (IoT-23: ROC-AUC , F1 ; ToN-IoT: ROC-AUC , F1 ). A comparative evaluation against deterministic autoencoders and classical baselines further indicates that the proposed -VAE achieves stronger cross-dataset anomaly-ranking performance than the compared methods. These findings support the suitability of probabilistic latent modelling for privacy-preserving anomaly detection in encrypted IoT environments.
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(This article belongs to the Section Cybersecurity)
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Open AccessArticle
Multi-Agent Intelligent System for Dynamic Predictive Evaluation of National and Regional Labour Markets in Bulgaria
by
Ivona Plamenova Velkova and Valentin Stefanov Kisimov
Future Internet 2026, 18(6), 309; https://doi.org/10.3390/fi18060309 - 7 Jun 2026
Abstract
Reliable public-sector labour-market forecasting requires models that can be updated as data sources, AI tools, and labour-market signals evolve. This paper proposes a provider-independent multi-agent framework for dynamic predictive evaluation of national and regional labour markets in Bulgaria. Implemented as a Model Context
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Reliable public-sector labour-market forecasting requires models that can be updated as data sources, AI tools, and labour-market signals evolve. This paper proposes a provider-independent multi-agent framework for dynamic predictive evaluation of national and regional labour markets in Bulgaria. Implemented as a Model Context Protocol (MCP) server, the system coordinates specialised agents for data ingestion, preprocessing, semantic extraction, AI-adjusted transformation modelling, automated model evaluation, and reporting through stable input–output contracts. The empirical application integrates Bulgarian Employment Agency administrative registered-unemployment indicators, Eurostat labour-market data, World Bank macroeconomic data, and textual, audio, and video evidence on AI, skills, and employment change. The analysis covers the period 2015–2030. Observed official data are used up to 2025 for model construction and validation, while the 2026–2030 values are reported only as forecast and scenario projections. For youth unemployment among persons aged 24 years or younger, the semantic-enhanced model achieves the best predictive accuracy (RMSE = 0.2033; MAE = 0.1457), representing a small improvement over the structured baseline (RMSE = 0.2057; MAE = 0.1462) and a substantial RMSE reduction relative to the persistence benchmark (RMSE = 0.4750; MAE = 0.2891). The AI-adjusted coefficient does not reduce holdout error relative to the semantic-enhanced model, but provides an explicit and sensitivity-tested mechanism for regional scenario interpretation. Regional forecasts indicate persistent spatial inequality, with the Northwest remaining the highest-risk region and the Southwest the lowest-risk region.
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(This article belongs to the Special Issue Intelligent Agents and Their Application)
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Open AccessArticle
Features over Architecture: Physics-Informed Anomaly Detection in Industrial Control Systems
by
Khaled Chahine and Hassan N. Noura
Future Internet 2026, 18(6), 308; https://doi.org/10.3390/fi18060308 - 6 Jun 2026
Abstract
Industrial control systems (ICS) are increasingly targeted by cyberattacks that manipulate physical processes while evading data-driven detectors trained on raw time-series data. This paper extracts 34–41 control-theoretic features, including tracking error, valve mismatch, sensor liveness, and their temporal derivatives, from Proportional–Integral–Derivative (PID) control
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Industrial control systems (ICS) are increasingly targeted by cyberattacks that manipulate physical processes while evading data-driven detectors trained on raw time-series data. This paper extracts 34–41 control-theoretic features, including tracking error, valve mismatch, sensor liveness, and their temporal derivatives, from Proportional–Integral–Derivative (PID) control loops and evaluates them using an Isolation Forest combined with a maximum z-score. On HAI 21.03, Stage 1 achieves a PA-F1 score of 0.8945, detecting 48 out of 50 attacks. On HAI 23.05, Stage 1 attains a PA-F1 score of 0.9210, surpassing seven deep-learning baselines by at least 23 PA-F1 points; the closest baseline, a learned Graph Neural Network (GNN), achieves 0.6890. Re-implementations of ConvBiLSTM-AE (PA-F1 = 0.6689) and TranAD (PA-F1 = 0.6838) on the same evaluation split confirm this performance gap. A controlled USAD experiment, with PA-F1 = 0.7343 for physics features versus 0.6687 for raw Supervisory Control and Data Acquisition (SCADA), demonstrates that the extracted features provide the detection signal independently of the model architecture. Adding a bidirectional Gated Recurrent Unit (GRU) refinement stage improves PA-F1 by 8.1 percentage points on HAI 21.03, but the same stage reduces it by 6.8 percentage points on HAI 23.05, where attacks manifest as brief perturbations; four alternative Stage 2 designs reproduce this degradation. We therefore characterize temporal refinement as beneficial only for sustained-deviation attacks and identify Stage 1 as the primary deployable detector. This study is the first to apply physics-informed features, report both PA-F1 and eTaPR on HAI 23.05, and perform per-window error diagnosis on this dataset. Results show that 10 of 15 detected windows are covered by fewer than 10% of their timesteps, revealing a structural tension between PA-F1 and eTaPR.
Full article
(This article belongs to the Special Issue Information and Future Internet Security, Trust and Privacy—4th Edition)
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Open AccessReview
AIoT-Based Security Systems for Smart Homes and Smart Buildings: A Tertiary Study
by
Francesco Pilotti, Aurora Pavone, Lia Di Sabatino Farinelli, Simone Tinelli and Gaetanino Paolone
Future Internet 2026, 18(6), 307; https://doi.org/10.3390/fi18060307 - 5 Jun 2026
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The rapid evolution of Smart Homes and Smart Buildings is driven by the transition from the Internet of Things (IoT) to the Artificial Intelligence of Things (AIoT). Within this scenario, Security Systems are particularly critical and data-intensive systems. Despite extensive research, a high-level
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The rapid evolution of Smart Homes and Smart Buildings is driven by the transition from the Internet of Things (IoT) to the Artificial Intelligence of Things (AIoT). Within this scenario, Security Systems are particularly critical and data-intensive systems. Despite extensive research, a high-level synthesis focusing exclusively on the synergy between AIoT and Security Systems in Smart Home and Smart Building application domains is still lacking. To bridge this gap, this paper presents a systematic Tertiary Study (TS) following a well-known research protocol. 13 Secondary Studies (SSs) were synthesized and discussed from an initial pool of 139 publications (years 2024–2025). Findings reveal that monitoring is the most addressed system, followed by security and alarm, while surveillance and access control remain comparatively underexplored. Moreover, results highlight a definitive shift toward Edge and Fog computing to meet latency and privacy requirements, whereas Deep Learning and Ensemble Learning techniques predominate for anomaly detection and predictive maintenance. This study identifies open challenges and future research directions, providing a foundational roadmap for resilient, cognitive-driven security infrastructures in smart environments.
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Open AccessArticle
Secure Federated Intrusion Detection for Resource-Constrained IoT Devices Using Lightweight Cryptography: A Hardware-Validated Study
by
Yerlan Tursynbek, Nurtay Albanbay, Djamel Djenouri, Shahid Latif, Ainur Akhmediyarova, Zhibek Alibiyeva, Janna Alimkulova and Dina Oralbekova
Future Internet 2026, 18(6), 306; https://doi.org/10.3390/fi18060306 - 5 Jun 2026
Abstract
Federated learning (FL) enables distributed model training in IoT environments while keeping raw data on local devices. However, protecting model-update exchange is difficult on microcontroller-class devices due to strict latency, memory, and energy constraints. Existing studies often evaluate lightweight cryptography outside complete FL
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Federated learning (FL) enables distributed model training in IoT environments while keeping raw data on local devices. However, protecting model-update exchange is difficult on microcontroller-class devices due to strict latency, memory, and energy constraints. Existing studies often evaluate lightweight cryptography outside complete FL pipelines or on more powerful hardware, leaving its practical overhead on MCU-class devices insufficiently explored. This paper presents an end-to-end, hardware-validated secure framework for exchanging model updates in federated learning on resource-constrained IoT microcontrollers. Implemented on ESP32-based edge devices, the framework combines lightweight block ciphers (SPECK, SIMON, and PRESENT), HMAC-SHA256 for integrity verification, and ECDH-HKDF for session-key establishment. The evaluation assessed latency, throughput, RAM/ROM footprint, and energy consumption. Results show that SPECK provides the lowest overhead (0.13 µs/byte, 8.68 MB/s, 138.3 mJ), SIMON offers intermediate performance (0.41 µs/byte, 1.96 MB/s, 184.9 mJ), and PRESENT incurs the highest computational cost (89.37 µs/byte, 0.011 MB/s, 446.2 mJ). In the CICIoT2023 federated intrusion-detection evaluation, the secure model maintained stable convergence and achieved 85.43% accuracy after 20 rounds, remaining close to the centralized baseline. These findings demonstrate the practical feasibility of secure model-update exchange in FL on real IoT microcontrollers and provide hardware-grounded guidance for cipher selection under tight resource budgets.
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(This article belongs to the Section Cybersecurity)
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Open AccessArticle
A Stability-Centric Framework for Lightweight and Explainable Intrusion Detection
by
Abdalilah Alhalangy, Saleh Abdulrahman Alkhamis and Eman Abouelkheir
Future Internet 2026, 18(6), 305; https://doi.org/10.3390/fi18060305 - 5 Jun 2026
Abstract
Effective intrusion detection for Internet of Things (IoT) environments requires balancing predictive performance, resource efficiency, and interpretability—particularly in real-world deployments where traffic distributions and attack scenarios vary. While many studies report near-perfect detection on benchmark datasets, this often overlooks model stability under distribution
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Effective intrusion detection for Internet of Things (IoT) environments requires balancing predictive performance, resource efficiency, and interpretability—particularly in real-world deployments where traffic distributions and attack scenarios vary. While many studies report near-perfect detection on benchmark datasets, this often overlooks model stability under distribution shifts. This paper addresses this gap by introducing a stability-focused evaluation of lightweight, explainable intrusion detection models using compact IoT-23 scenarios and a constrained set of 14 connection-level features for interpretability. Four lightweight models—logistic regression, random forest, XGBoost, and LightGBM—are assessed within a unified pipeline. Beyond standard internal validation, we implement a strict cross-scenario evaluation framework featuring a fully unseen malware capture. Our proposed Internal–External Stability Gap (IESG) framework, enhanced with normalized and multi-metric measures, highlights the degradation in consistency between internal and external metrics. Surprisingly, even models with high internal F1 scores (up to 0.9994) may experience considerable drops in external macro-F1 and specificity, exposing weaknesses in conventional evaluation. Experimentally, LightGBM provides the best trade-off between performance and compactness (606 KB) and shows the smallest stability gap for malicious detection. Nevertheless, all models show reduced balanced performance under scenario shift, underscoring that deployment readiness hinges on stability under changing conditions. Feature ablation reveals that leveraging high-impact features, such as port information, can boost internal accuracy at the expense of generalization. In summary, we demonstrate that while lightweight models deliver strong detection, only those proven stable across scenarios are viable for real-world IoT intrusion detection. Our evaluation framework offers a practical, interpretable tool for assessing model robustness.
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(This article belongs to the Special Issue Advanced Cybersecurity, Threat Detection, and Digital Forensics for IoT Systems)
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Open AccessArticle
Dual-Stream Wavelet Network for Early Knee Osteoarthritis Grading in IoT-Enabled Smart Clinics
by
Lassaad Ben Ammar, Altahir Saad and Ahod Alghuried
Future Internet 2026, 18(6), 304; https://doi.org/10.3390/fi18060304 - 4 Jun 2026
Abstract
Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings.
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Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services.
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(This article belongs to the Special Issue Distributed Intelligence for IoT and Smart Systems)
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Open AccessSystematic Review
Explainable and Human-Centered AIoT: A Systematic Review of Integration, Interaction, and Impact
by
Adolfo A. Jurado Rosas, Marina Fernández Miranda, Gladys L. Peña Pazos, Elberth E. García Panta, Carlos A. Ramos Reyes, Milagros P. Córdova de Chang, José H. Chang Valdiviezo, Olga P. Gamarra Chirinos and Carlos E. Esquerre Aguirre
Future Internet 2026, 18(6), 303; https://doi.org/10.3390/fi18060303 - 4 Jun 2026
Abstract
This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer
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This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer Nature Link. The inclusion criteria prioritized open-access, peer-reviewed English articles published between 2020 and 2025 that addressed AIoT architectures and explainability mechanisms. The screening procedure involved a dual independent review process, followed by a rigorous methodological quality assessment to minimize the risk of bias, culminating in a final sample of 40 studies from an initial pool of 971 records. The findings reveal a structural paradox: while intelligent systems achieve greater operational autonomy, legal and moral accountability remains inexorably bound to the human operator. Furthermore, 77.5% of the evaluated implementations employ superficial explainability, functioning merely as a psychological buffer to manage automation anxiety rather than providing a genuine interactive control mechanism. It is concluded that programming based on HCAI principles must shift from a post hoc feature to an inherent architectural requirement. Establishing explainability by design is imperative to guarantee an interactive audit capability that comprehensively safeguards operational integrity and preserves human agency, although the exclusive reliance on open-access literature limits visibility into proprietary commercial models.
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(This article belongs to the Special Issue Information Networks with Human-Centric LLMs)
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Open AccessArticle
Secure Federated Learning Algorithms for Vertical and Combined Data Partitioning
by
Amir Anees, Ding Ming, Gnana Bharathy and Lois Holloway
Future Internet 2026, 18(6), 302; https://doi.org/10.3390/fi18060302 - 3 Jun 2026
Abstract
With the growing need for collaborative machine learning across institutions holding sensitive data, ensuring data privacy without compromising model performance has become an important challenge. This work introduces secure federated learning algorithms that use encryption and masking techniques to protect the privacy of
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With the growing need for collaborative machine learning across institutions holding sensitive data, ensuring data privacy without compromising model performance has become an important challenge. This work introduces secure federated learning algorithms that use encryption and masking techniques to protect the privacy of data during collaborative model training. Three federated learning algorithms were developed: one for vertical federated learning and two combining horizontal and vertical data partitioning. The proposed algorithms are designed such that participating clients communicate only with the server, even when data exchange between clients is required. This exchange occurs through the server with the help of encryption and masking. The performance of the algorithms, evaluated in terms of accuracy and loss, shows competitive results. The accuracy remains unchanged compared to the centralised scenario for the vertical federated learning algorithm and one of the combined federated learning algorithms, and it remains highly competitive with the other combined federated learning algorithm. The privacy analyses conducted as part of this work demonstrate no risk of data leakage ensuring that no party involved can infer sensitive information.
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(This article belongs to the Special Issue Federated Neural Networks: Design and Deployment)
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