Future Internet

23 pages, 6049 KB

Open AccessArticle

Seamless Inter-Domain Mobility with Hybrid SDN-LISP

by Kuljaree Tantayakul, Adisak Intana, Aung Aung and Riadh Dhaou

Future Internet 2026, 18(5), 227; https://doi.org/10.3390/fi18050227 - 22 Apr 2026

One of the challenges in managing mobility in a heterogeneous network domain remains a significant challenge in Software-Defined Networking (SDN). While SDN has effectively facilitated intra-domain mobility, inter-domain mobility has been a major issue, leading to service interruptions, packet loss, and unstable communication [...] Read more.

One of the challenges in managing mobility in a heterogeneous network domain remains a significant challenge in Software-Defined Networking (SDN). While SDN has effectively facilitated intra-domain mobility, inter-domain mobility has been a major issue, leading to service interruptions, packet loss, and unstable communication sessions. This article presents a new concept in mobility management: a hybrid SDN-LISP network that facilitates inter-domain communication by integrating SDN with the Locator/Identifier Separation Protocol (LISP). The main idea is to introduce a new event-based orchestration model that uses OpenFlow Packet-In messages to provide instantaneous updates to Endpoint Identifiers-to-Routing Locators (EID-to-RLOC) mappings, unlike traditional LISP, which relies on timers for updates. The proposed framework has been implemented and evaluated on a Mininet-WiFi testbed under various mobility conditions. The results obtained from the experimental evaluation reveal that packet loss is reduced by 92.32% when using the proposed framework over the conventional SDN Mobility approach. Although there is a slight increase in jitter overhead due to LISP encapsulation of 0.628 ms, the framework does not compromise Transmission Control Protocol (TCP) session continuity. In addition, the control plane synchronization time is also minimized to 277.5 ms. This reveals that the proposed framework is a stable mobility solution that does not depend on any conventional IP mobility solutions and can be used in future network environments requiring seamless inter-domain connectivity. Full article

(This article belongs to the Section Network Virtualization and Edge/Fog Computing)

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24 pages, 1534 KB

Open AccessArticle

Hybrid Energy-Aware Ranking and Optimization

by Zhiling Zeng, Yuxuan Jiang and Na Niu

Future Internet 2026, 18(5), 226; https://doi.org/10.3390/fi18050226 - 22 Apr 2026

Abstract

The increase in delay-sensitive application tasks requires heterogeneous edge clusters to maintain low online latency and energy efficiency without relying on rigid scheduling policies. To address this, we propose HERO (Hybrid Energy-aware Ranking and Optimization), a lightweight collaborative scheduling framework. HERO utilizes a [...] Read more.

The increase in delay-sensitive application tasks requires heterogeneous edge clusters to maintain low online latency and energy efficiency without relying on rigid scheduling policies. To address this, we propose HERO (Hybrid Energy-aware Ranking and Optimization), a lightweight collaborative scheduling framework. HERO utilizes a perturbation-based communication-aware multi-layer perceptron (MLP) predictor to quantify global time sensitivity and discover latent time slack in non-critical paths. A hybrid budget mechanism then converts this slack into customized DVFS decisions. These decisions are based on the inherent computational load and topological criticality to optimize energy consumption. A communication-aware hole-filling strategy dynamically recovers sporadic idle times fragmented by heterogeneous communication overhead. Extensive simulations were conducted across varying DAG depths, parallelism levels, and system utilizations. Compared to state-of-the-art algorithms (NSGA-II, SSA, TOM, and DPMC), HERO reduced the completion time by an average of 10.89% under high-density topologies, and achieved up to 4.04% energy savings across varying task depths. Full article

44 pages, 5149 KB

Open AccessFeature PaperArticle

Scheduling Jamming Resources in Complex Terrain: A Multi-Objective Air—Ground Collaborative Optimization Approach

by Haiyang You, Zhenhua Wei, Wenpeng Wu, Chenxi Li, Jianwei Zhan and Zhaoguang Zhang

Future Internet 2026, 18(5), 225; https://doi.org/10.3390/fi18050225 - 22 Apr 2026

Abstract

Addressing the high-dimensional, strongly constrained multi-objective optimization problem of air–ground collaborative jamming scheduling in complex terrain, existing methods are often limited by incomplete modeling and low optimization efficiency in discrete feasible regions. This paper proposes a Terrain-Aware Multi-Scale Discrete Operator (TA-MSDO). A joint [...] Read more.

Addressing the high-dimensional, strongly constrained multi-objective optimization problem of air–ground collaborative jamming scheduling in complex terrain, existing methods are often limited by incomplete modeling and low optimization efficiency in discrete feasible regions. This paper proposes a Terrain-Aware Multi-Scale Discrete Operator (TA-MSDO). A joint optimization model integrating discrete terrain characteristics and practical combat constraints is first constructed. Then, by leveraging the topological adjacency of terrain units, TA-MSDO employs a block-level crossover and a multi-scale mutation mechanism, replacing traditional continuous genetic operations to enable efficient and directional exploration of the discrete feasible region. Integrating TA-MSDO into the NSGA-III framework yields the enhanced ENSGA3 algorithm. Experimental results in a typical hilly terrain scenario demonstrate that ENSGA3 achieves a statistically significant performance improvement over the decomposition-based MOEA/D algorithm in terms of maximum achievable suppression effectiveness and hypervolume. As a comprehensive metric integrating convergence and Pareto frontier coverage, hypervolume further verifies the superior comprehensive optimization capability of the proposed algorithm. Meanwhile, compared with other classic mainstream multi-objective optimization algorithms including NSGA-II, standard NSGA-III and SPEA2, the proposed algorithm exhibits clear positive advantages in the upper bound of suppression effectiveness for elite solutions and operational stability across random initializations, with a favorable trend in Pareto frontier coverage for multi-objective collaborative optimization. This work provides an effective solution for jamming resource scheduling in complex battlefield environments. Full article

(This article belongs to the Topic Applications of IoT in Multidisciplinary Areas)

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20 pages, 2659 KB

Open AccessArticle

A Security-Aware Ambient Intelligence Framework for Detecting Violent Language in Airline Customer Reviews

by Fahad Alanazi and Osama Rabie

Future Internet 2026, 18(5), 224; https://doi.org/10.3390/fi18050224 - 22 Apr 2026

Abstract

The aviation industry operates in a security-sensitive environment where customer feedback may contain not only expressions of satisfaction or dissatisfaction but also threatening or violent language with potential security implications. While conventional sentiment analysis effectively captures customer opinions, it remains insufficient for identifying [...] Read more.

The aviation industry operates in a security-sensitive environment where customer feedback may contain not only expressions of satisfaction or dissatisfaction but also threatening or violent language with potential security implications. While conventional sentiment analysis effectively captures customer opinions, it remains insufficient for identifying security-relevant linguistic cues that could signal risks requiring proactive intervention. This study addresses this gap by introducing a security-aware ambient intelligence framework for detecting violent language in airline customer reviews. This framework supports intelligent internet-based monitoring systems and real-time threat detection. We present the first annotated dataset of airline reviews specifically labeled for violent and threatening content, derived from 3629 reviews and balanced through manual resampling to achieve equal representation across positive, neutral, negative, and violent classes. The proposed framework employs VADER-based sentiment analysis for initial polarity estimation, combined with a validated annotation process to identify violent or threat-related content, followed by comprehensive feature engineering combining TF-IDF (2000 features) with text statistics and sentiment scores. We systematically evaluate individual classifiers (Random Forest, Decision Tree, SVM, Naive Bayes) against ensemble methods (Voting, Stacking, Boosting) using accuracy, precision, recall, F1-score, and ROC AUC metrics. Results demonstrate that Stacking achieves the highest raw performance (98.57% accuracy, F1-macro 0.9856), while Naive Bayes offers an optimal balance between effectiveness and computational efficiency (81.79% accuracy, F1-macro 0.8172, training time 0.03 s). This is the first dataset and framework designed for security-aware analysis of airline reviews. The selected Naive Bayes model achieves per-class F1-scores of 0.9978 for neutral, 0.7814 for negative, 0.7482 for violent, and 0.7415 for positive reviews, with a macro-average ROC AUC of 0.7123. The framework is deployed with serialized components enabling real-time prediction, supporting both single-review analysis and batch processing for integration into airline security monitoring systems. This work establishes a foundation for security-aware natural language processing in critical infrastructure contexts, bridging the gap between conventional sentiment analysis and proactive threat detection. Full article

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28 pages, 1805 KB

Open AccessArticle

Intelligent Threat Defense Mechanisms for 5G APIs

by Asif Yasin, Seyed Ebrahim Hosseini, Muhammad Nadeem and Shahbaz Pervez

Future Internet 2026, 18(5), 223; https://doi.org/10.3390/fi18050223 - 22 Apr 2026

Abstract

As 5G Standalone Core networks grow, Application Programming Interface (APIs) have become a key part of how network systems talk to each other. They allow different functions to share data and complete tasks quickly. However, this also makes them targets for attacks. 5G [...] Read more.

As 5G Standalone Core networks grow, Application Programming Interface (APIs) have become a key part of how network systems talk to each other. They allow different functions to share data and complete tasks quickly. However, this also makes them targets for attacks. 5G Standalone Core networks rely on Service-Based Architecture (SBA), where network functions communicate through exposed APIs. These APIs are attractive targets for cyberattacks because they are externally accessible, handle sensitive control-plane operations, and exchange structured data using Hypertext Transfer Protocol version 2 (HTTP/2) and JavaScript Object Notation (JSON) protocols. Most older security tools work using fixed rules, which cannot always detect new or changing threats. This study aimed to fix that gap by using Artificial Intelligence to make API security smarter. Two AI models were tested: Long Short-Term Memory (LSTM), which learns from past traffic and Reinforcement Learning (RL), which learns by adapting to network behavior. Both were used to assess API traffic and assign a real-time risk score. Synthetic traffic was created using Python, including both normal API calls and different types of attacks like Distributed Denial-of-Service (DDoS), brute force, and Structured Query Language (SQL) injection. The results show that both LSTM and RL models were better than traditional rule-based systems. They found more threats, gave fewer false alerts, and responded faster. RL was especially strong at handling unknown or changing attacks. Experimental results show that the proposed LSTM and RL models achieved approximately 95% detection accuracy, significantly outperforming the static rule-based baseline model, which achieved 58% accuracy. The results demonstrate the effectiveness of adaptive AI-based security mechanisms for detecting evolving API threats. This research shows that AI can help protect 5G APIs in a smarter and more flexible way. It can support telecom networks by making threat detection faster, more accurate, and ready for future challenges. Full article

(This article belongs to the Section Cybersecurity)

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21 pages, 843 KB

Open AccessArticle

Assessing Hierarchical Temporal Memory Against an LSTM Baseline for Short-Term Smart-Meter Load Forecasting

by Antón Román-Portabales and Martín López-Nores

Future Internet 2026, 18(4), 222; https://doi.org/10.3390/fi18040222 - 21 Apr 2026

Abstract

Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter data collected from domestic consumers in a medium-sized city in southern [...] Read more.

Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter data collected from domestic consumers in a medium-sized city in southern Spain. In particular, we assess Hierarchical Temporal Memory (HTM), a biologically inspired online sequence learner, against a family of Long Short-Term Memory (LSTM)-based recurrent baselines. HTM offers continual adaptation and avoids a separate training phase, whereas LSTM relies on offline supervised training and may require retraining or fine-tuning under distribution shift. For five-step-ahead forecasting, HTM achieved a test RMSE of 251 kWh (about 15% of average consumption). After hyperparameter optimization, the best tested LSTM configuration achieved a test RMSE of approximately 250 kWh under clean conditions, indicating nearly identical point accuracy between the two approaches. Under synthetic Gaussian-noise injection, however, HTM remained comparatively stable, whereas the optimized LSTM configuration degraded markedly under the tested perturbation protocol. In addition, HTM exhibited a lower runtime in the tested CPU-based implementation. These findings suggest that HTM is a viable online alternative for aggregated smart-meter forecasting, offering competitive accuracy together with a favorable operational profile under the specific evaluation setup considered here. Full article

(This article belongs to the Special Issue Artificial Intelligence in Smart Grids)

24 pages, 2667 KB

Open AccessArticle

Hybrid Deep Neural Network-Based Modeling of Multimodal Emotion Recognition for Novice Drivers

by Jianzhuo Li, Ye Yu, Zhao Dai and Panyu Dai

Future Internet 2026, 18(4), 221; https://doi.org/10.3390/fi18040221 - 21 Apr 2026

Abstract

Driver emotion recognition is a crucial method for reducing traffic accidents. Most existing research focuses on experienced drivers as the primary research subjects, overlooking novice drivers, who are inexperienced in driving. However, novice drivers can easily lose control of their emotions due to [...] Read more.

Driver emotion recognition is a crucial method for reducing traffic accidents. Most existing research focuses on experienced drivers as the primary research subjects, overlooking novice drivers, who are inexperienced in driving. However, novice drivers can easily lose control of their emotions due to the high mental load during driving, which can lead to serious traffic accidents. Therefore, to recognize the emotions of novice drivers for timely warnings, we propose an emotion recognition model based on multimodal information. The model consists of a facial feature extraction module, an eye movement feature extraction module and a classifier. The facial feature extraction module uses the ViT-B/16 to extract the facial features of novice drivers. The eye movement feature extraction module is a hybrid network containing Bi-LSTM and Transformer. It extracts eye movement features of novice drivers. Facial features and eye movement features are fused and fed to the classifier. The classifier can output the five major emotion categories of surprise, anger, calm, happy, and other for novice drivers. The experimental results demonstrate that our model accurately recognizes the emotions of novice drivers with an accuracy of 98.72%, surpassing that of other models. Full article

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21 pages, 1071 KB

Open AccessArticle

A Tiered Classification Framework for Detecting and Diagnosing Man-in-the-Middle Attacks in Smart Grid Protocols

by Hassan N. Noura, Zaid Allal, Ola Salman and Khaled Chahine

Future Internet 2026, 18(4), 220; https://doi.org/10.3390/fi18040220 - 21 Apr 2026

Abstract

The increasing reliance on smart grid communication systems has significantly raised the demand for robust cybersecurity measures to defend against advanced threats. This paper proposes a two-tier classification framework to enhance the detection and diagnosis of man-in-the-middle attacks within smart grid communication protocols. [...] Read more.

The increasing reliance on smart grid communication systems has significantly raised the demand for robust cybersecurity measures to defend against advanced threats. This paper proposes a two-tier classification framework to enhance the detection and diagnosis of man-in-the-middle attacks within smart grid communication protocols. Initially, the model detects the presence of an attack and then identifies the specific type of man-in-the-middle attack through subsequent inferences. To achieve this, the “Man-in-the-Middle Attacks Targeting Modbus TCP/IP and MMS Protocols in the Smart Grid” dataset was carefully preprocessed and analyzed to better understand the underlying hidden characteristics. This understanding, coupled with existing works on fault detection and diagnosis, facilitated the engineering of new features from the original dataset. Four classifiers were employed in each tier: Random Forest, XGBoost, LightGBM, and CatBoost. The first tier exhibited exceptional performance, with the CatBoost framework achieving 99.6% accuracy. The second tier also demonstrated strong results, with the same model achieving 99.1% accuracy. Systematic model explainability was conducted using SHapley Additive exPlanations for both tiers and revealed that the highest accuracy was achieved using five features for the first and six for the second. The average inference time was approximately 4.76 milliseconds. The proposed framework is accurate, fast, interpretable, lightweight, and well-optimized for direct implementation in smart grid systems to detect and diagnose man-in-the-middle attacks. Full article

(This article belongs to the Special Issue Artificial Intelligence in Smart Grids)

5 pages, 158 KB

Open AccessEditorial

Editorial for the Special Issue “Artificial Intelligence: Innovation, Applications and Transformative Experiences”

by Diego Vergara and Pablo Fernández-Arias

Future Internet 2026, 18(4), 219; https://doi.org/10.3390/fi18040219 - 21 Apr 2026

Abstract

Artificial intelligence (AI) is driving profound changes in the digital transformation of society [...] Full article

(This article belongs to the Special Issue Artificial Intelligence: Innovation, Applications and Transformative Experiences)

44 pages, 2312 KB

Open AccessArticle

Classification Model of Emotional Tone in Hate Speech and Its Relationship with Inequality and Gender Stereotypes, Using NLP and Machine Learning Algorithms

by Aymé Escobar Díaz, Ricardo Rivadeneira, Walter Fuertes and Washington Loza

Future Internet 2026, 18(4), 218; https://doi.org/10.3390/fi18040218 - 20 Apr 2026

Abstract

Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open data sets [...] Read more.

Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open data sets (1,236,371 records; 1,003,991 after ETL) and represented the text using TF-IDF and contextual RoBERTa embeddings. We trained individual models (RoBERTa fine-tuned, Random Forest, and XGBoost) and a stacking metamodel (Gradient Boosting) that combines their probabilities. On the test set, the ensemble outperformed the base classifiers, achieving accuracy of 0.93 in hate detection and 0.90 in emotion classification, with an AUC of 0.98 for emotion classification. We implemented a RESTful API and a web client to validate the moderation flow before publication, along with an administration panel for auditing. Performance tests in a prototype deployment (Google Colab exposed through an Ngrok tunnel) provided proof-of-concept validation, revealing concurrency limitations from around 300 users due to infrastructure constraints. In general, the results indicate that incorporating emotional tone analysis improves the model’s ability to identify implicit hostility and offers a practical way to promote safer digital environments. The probabilistic outputs produced by the ensemble model were subsequently analyzed using the Bayesian Calibration and Optimal Design under Asymmetric Risk (BACON-AR) framework, which serves as a mathematical post hoc decision layer for evaluating classification behaviour under unequal error costs. Rather than modifying the trained architecture or improving its predictive performance, the framework identifies a cost-sensitive operating threshold that minimizes the total expected risk under the selected asymmetric cost configuration. The experiments were conducted using an English-language data set; therefore, the findings of this study are limited to hate speech detection in English. Full article

(This article belongs to the Section Techno-Social Smart Systems)

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5 pages, 181 KB

Open AccessEditorial

2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members

by Gianluigi Ferrari

Future Internet 2026, 18(4), 217; https://doi.org/10.3390/fi18040217 - 19 Apr 2026

Abstract

As indicated on the journal’s website, Future Internet fosters contributions to the future Internet ecosystem, which, in turn, is expected to lead to significant improvement in well-being in all spheres of human life (private, public, professional) [...] Full article

(This article belongs to the Special Issue 2024 and 2025 Feature Papers from Future Internet’s Editorial Board Members)

26 pages, 4830 KB

Open AccessArticle

A Physically Aware Residual Learning Framework for Outdoor Localization in LoRaWAN Networks

by Askhat Bolatbek, Ömer Faruk Beyca, Batyrbek Zholamanov, Madiyar Nurgaliyev, Gulbakhar Dosymbetova, Dinara Almen, Ahmet Saymbetov, Botakoz Yertaikyzy, Sayat Orynbassar and Ainur Kapparova

Future Internet 2026, 18(4), 216; https://doi.org/10.3390/fi18040216 - 18 Apr 2026

Abstract

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges [...] Read more.

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

(This article belongs to the Section Internet of Things)

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24 pages, 11332 KB

Open AccessArticle

Intelligent Optimization Methods for Cloud–Edge Collaborative Vehicular Networks via the Integration of Bayesian Decision-Making and Reinforcement Learning

by Youjian Yu, Zhaowei Song, Sifeng Zhu and Qinghua Zhang

Future Internet 2026, 18(4), 215; https://doi.org/10.3390/fi18040215 - 17 Apr 2026

Abstract

To improve vehicle user service quality and address data privacy and security issues in intelligent transportation vehicle networking systems, a three-tier communication architecture with cloud-edge-end collaboration was designed in this paper. A Bayesian decision criterion was utilized to divide user data segments into [...] Read more.

To improve vehicle user service quality and address data privacy and security issues in intelligent transportation vehicle networking systems, a three-tier communication architecture with cloud-edge-end collaboration was designed in this paper. A Bayesian decision criterion was utilized to divide user data segments into fine-grained slices based on their privacy levels, and differential privacy techniques were applied to protect the offloaded data. To achieve multi-objective optimization between user service quality and data privacy and security, the problem was formulated as a constrained Markov decision process. A communication model, a caching model, a latency model, an energy consumption model, and a data-fragment privacy protection model were designed. Additionally, a deep reinforcement learning algorithm based on the actor–critic approach was proposed for the collaborative and centralized training of multiple intelligent agents (CTMA-AC), enabling multi-objective optimization decision-making for the protection of offloaded private user data. Simulation experiments demonstrate that the proposed multi-agent collaborative privacy data offloading protection strategy can effectively safeguard private user data while ensuring high service quality. Full article

(This article belongs to the Section Network Virtualization and Edge/Fog Computing)

32 pages, 3196 KB

Open AccessArticle

A Distributed Energy Trading Framework Based on All-Optical Multicast Communication

by Xuxun Ye and Anliang Cai

Future Internet 2026, 18(4), 214; https://doi.org/10.3390/fi18040214 - 17 Apr 2026

Abstract

The millisecond-level volatile fluctuations in workloads in large-scale intelligent computing clusters pose significant challenges to traditional electricity markets. Constrained by optical–electrical–optical conversion bottlenecks, these markets struggle to achieve real-time response and risk substantial social welfare loss. Leveraging existing fiber-optic infrastructure to build All-Optical [...] Read more.

The millisecond-level volatile fluctuations in workloads in large-scale intelligent computing clusters pose significant challenges to traditional electricity markets. Constrained by optical–electrical–optical conversion bottlenecks, these markets struggle to achieve real-time response and risk substantial social welfare loss. Leveraging existing fiber-optic infrastructure to build All-Optical Networks (AONs) presents a cost-effective evolutionary path. This paper develops a distributed energy trading strategy based on all-optical multicast. By utilizing the physical multicast properties of the underlying light-tree architecture instead of traditional protocols, the proposed strategy bypasses end-to-end latency constraints. This enables rapid transaction synchronization and dynamic tracking of social welfare optima within millisecond-level time-slots. Simulation results demonstrate that the proposed scheme elevates the transaction saturation threshold by two orders of magnitude compared with traditional strategies, effectively breaking the physical locking effect of latency on system throughput. Across various topologies, the social welfare gains exceed those of conventional schemes by more than 20 times. This study validates the engineering value of all-optical architectures for high-frequency trading and provides critical technical support for ultra-dynamic power trading algorithms. Full article

(This article belongs to the Section Smart System Infrastructure and Applications)

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31 pages, 753 KB

Open AccessArticle

Heterogeneity-Aware Dynamic Federated Split Learning with Adaptive Compression (HADFL-AC) Edge–Cloud Inference in IoT Environments

by Norah Alrusayni and Asma A. Al-Shargabi

Future Internet 2026, 18(4), 213; https://doi.org/10.3390/fi18040213 - 17 Apr 2026

Abstract

In resource-constrained environments, distributed split learning allows for collaborative training; however, the system suffers from high communication overhead and is sensitive to system heterogeneity. Despite advances in IoT data reduction and distributed learning, existing approaches treat heterogeneity, adaptability, and communication efficiency as separate [...] Read more.

In resource-constrained environments, distributed split learning allows for collaborative training; however, the system suffers from high communication overhead and is sensitive to system heterogeneity. Despite advances in IoT data reduction and distributed learning, existing approaches treat heterogeneity, adaptability, and communication efficiency as separate problems. As a result, the Heterogeneity-Aware Dynamic Federated Split Learning with Adaptive Compression (HADFL-AC) framework is proposed, enabling adaptive adjustment of communication payloads to instantaneous bandwidth conditions during training. This approach distinguishes itself by focusing on feature-representation-level adaptation, offering seamless transitions between linear PCA, nonlinear Tiny Autoencoder (TinyAE), and hybrid PCA–AE compression methods without requiring changes to architecture or retraining. Experiments were conducted using the CIFAR10 and CI=NIC datasets with a lightweight ResNet-18 backbone under Dirichlet-based non-IID data partitioning and fluctuating network scenarios. HADFL-AC achieves significant communication reductions of 80.86% on CIFAR-10 and 77.2% on CINIC-10, as well as significant reductions in training time and energy consumption. In addition, the framework achieved these gains while maintaining competitive performance, reaching 79.58% on CIFAR-10 and exhibiting stable convergence on CINIC-10. Consequently, the results demonstrate that leveraging network heterogeneity as an adaptive signal facilitates efficient and scalable distributed learning while effectively balancing communication efficiency and model accuracy. Full article

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

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24 pages, 555 KB

Open AccessArticle

Community-Aware Network Dismantling via Gateways: Large-Scale Evaluation on LFR Benchmarks

by Jan Sawicki, Maria Ganzha, Marcin Paprzycki, Jihui Han and Subhajit Sahu

Future Internet 2026, 18(4), 212; https://doi.org/10.3390/fi18040212 - 16 Apr 2026

Abstract

Network dismantling—the targeted removal of nodes to degrade large-scale connectivity—plays a central role in resilience analysis, epidemic containment, and systemic-risk mitigation. Recent work shows that dismantling performance depends strongly on mesoscale modular structure, suggesting that community-aware strategies may offer advantages over classical centrality-based [...] Read more.

Network dismantling—the targeted removal of nodes to degrade large-scale connectivity—plays a central role in resilience analysis, epidemic containment, and systemic-risk mitigation. Recent work shows that dismantling performance depends strongly on mesoscale modular structure, suggesting that community-aware strategies may offer advantages over classical centrality-based heuristics. In this work, we perform a large-scale, systematic evaluation of dismantling strategies and introduce gateways as a new mesoscale dismantling concept. While similar experiments exist using degree- and betweenness-based dismantling strategies, we check a new strategy based on gateways, which capture asymmetric entry points into communities and generalize the notion of inter-community connectors. Furthermore, we process a massive dataset of 568,584 LFR benchmark graphs, covering a wide range of degree distributions, community sizes, and mixing parameters. For evaluation, we use both extrinsic (ARI, NMI, FMI, VI) and intrinsic (Modularity, Coverage, Performance, Average Conductance, Average Internal Density) metrics. We find that across parameter regimes and evaluation metrics, classical strategies (degree, betweenness, community connections) and gateway-based dismantling exhibit broadly similar performance. Our results also corroborate recent findings that dismantling effectiveness is robust to the specific partitioning algorithm and that inter-community connectivity plays a dominant role in global fragmentation. The evaluation provides large-scale evidence that gateway-aware dismantling captures an operationally relevant mesoscale mechanism as good as previous approaches and motivates further empirical studies on real networks and cost-aware settings. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Online Social Networks)

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24 pages, 5998 KB

Open AccessArticle

A Wearable System for Real-Time Fall Detection on Resource-Constrained Devices

by Timothy Malche, Govind Murari Upadhyay, Sumegh Tharewal, Vipin Balyan, Vikash Kumar Mishra, Gunjan Gupta and Pramod Kumar Soni

Future Internet 2026, 18(4), 211; https://doi.org/10.3390/fi18040211 - 16 Apr 2026

Abstract

In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory, power, and processing capacity are limited. The [...] Read more.

In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory, power, and processing capacity are limited. The system works by collecting body motion data using accelerometer sensors placed on the human body. The data is then processed using a feedforward neural network trained on preprocessed signals. The trained model is quantized so that it can run on low-power embedded hardware with small memory size. The model performs inference directly on the device. This reduces latency and avoids sending raw sensor data to the cloud. When a fall is detected, the result is sent through Bluetooth to a gateway. The gateway forwards the data to a cloud server using the MQTT protocol. The cloud stores the data and supports monitoring and analysis. The experimental results show that the quantized TinyML model achieves 98.40% accuracy with more than 80% F1-score and more than 99% recall. The deployed model uses only ∼5 KB of RAM and ∼40 KB of flash memory. The inference time is 7 ms per class. These results show that wearable sensing with quantized TinyML models and IoT communication can provide fast and reliable fall detection for real-world safety monitoring systems. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)

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28 pages, 2054 KB

Open AccessArticle

A Hybrid CNN–LSTM–Attention Framework for Intrusion Detection in Smart Mobility Networks

by Otuekong Ekpo, Valentina Casola, Alessandra De Benedictis, Philip Asuquo and Bright Agbor

Future Internet 2026, 18(4), 210; https://doi.org/10.3390/fi18040210 - 15 Apr 2026

Abstract

Smart cities are increasingly dependent on interconnected transportation systems; however, this connectivity exposes smart mobility networks to significant cybersecurity risks. Traditional Intrusion Detection Systems are ill-equipped for this environment, as they are designed for isolated systems or fixed network boundaries. Thus, they struggle [...] Read more.

Smart cities are increasingly dependent on interconnected transportation systems; however, this connectivity exposes smart mobility networks to significant cybersecurity risks. Traditional Intrusion Detection Systems are ill-equipped for this environment, as they are designed for isolated systems or fixed network boundaries. Thus, they struggle to secure the complex and heterogeneous smart mobility networks, where various protocols and resource-constrained edge devices require more adaptive solutions. To address this limitation, we propose a novel hybrid deep learning framework that combines convolutional neural networks for spatial feature extraction, long short-term memory networks for temporal pattern recognition, and an attention mechanism for adaptive feature weighting, together forming a context-aware Intrusion Detection System. Our approach is evaluated across six benchmark datasets spanning vehicular networks, IoT ecosystems, cloud computing, and 5G environments—VeReMi Extension, CICIoV2024, Edge-IIoTset, UNSW-NB15, Car Hacking, and 5G-NIDD—a deliberately diverse selection that represents the heterogeneous nature of real-world smart mobility networks. Empirical evaluation using three different random seeds reveals the proposed framework achieves detection accuracy exceeding 98% on each dataset, a mean F1 score of 98.94%, and an inference latency of just 4.96 ms per sample. Our results show that the proposed model achieves consistently high detection performance across six heterogeneous benchmark datasets, making it a potentially robust candidate for real-time intrusion detection in smart mobility systems. Full article

(This article belongs to the Special Issue Cybersecurity in the Era of Smart Cities)

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30 pages, 712 KB

Open AccessReview

AI Risk Governance for Advancing Digital Sovereignty in Data-Driven Systems: An Integrated Multi-Layer Framework

by Segun Odion and Santosh Reddy Addula

Future Internet 2026, 18(4), 209; https://doi.org/10.3390/fi18040209 - 15 Apr 2026

Abstract

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control [...] Read more.

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control is meaningful has become a central concern for states and institutions at every level of development. Existing frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, have made real progress toward structured AI governance. However, none treats digital sovereignty as a first-order goal, nor do they provide integrated cross-layer guidance applicable across the diverse institutional landscape found worldwide. From this synthesis, we develop the Integrated AI Risk Governance Framework (IARGF): a four-layer structure covering policy and regulations, institutional oversight, technical controls, and operational execution, organized around five risk categories—technical, ethical, security, systemic, and sovereignty-related. A comparative analysis with major existing frameworks highlights the IARGF’s unique contributions, especially its explicit focus on sovereignty, adaptability across different institutional capacities, and recursive feedback mechanisms that connect all four governance layers. The framework is analyzed across three domains—healthcare AI, financial services, and critical infrastructure—to demonstrate its practical utility. Results confirm that governance effectiveness is a system property, not just a feature of individual layers; that digital sovereignty is both a governance goal and a distinct risk dimension with specific technical and institutional needs; and that context-aware, capacity-scaled governance is a design requirement, not a political compromise. The IARGF is presented as a conceptual governance model based on a systematic literature review rather than an empirically validated tool, and it remains to be tested in actual organizational settings. Its main contribution is the comprehensive theoretical integration of sovereignty, institutional capacity, and inter-layer governance dynamics, rather than proven performance advantages over existing models. Future research should aim to validate this framework through longitudinal case studies, expert panels, and retrospective failure analyses. Full article

(This article belongs to the Special Issue Security and Privacy in AI-Powered Systems)

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