Future Internet

19 pages, 1514 KiB

Open AccessArticle

A UAV Trajectory Optimization and Task Offloading Strategy Based on Hybrid Metaheuristic Algorithm in Mobile Edge Computing

by Yeqiang Zheng, An Li, Yihu Wen and Gaocai Wang

Future Internet 2025, 17(7), 300; https://doi.org/10.3390/fi17070300 - 3 Jul 2025

Abstract

In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in [...] Read more.

In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable a UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper has significantly higher success than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of UAV. Full article

(This article belongs to the Special Issue Task Offloading and Resource Allocation for IoT in Next-Generation Networking)

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25 pages, 1524 KiB

Open AccessArticle

Detecting Emerging DGA Malware in Federated Environments via Variational Autoencoder-Based Clustering and Resource-Aware Client Selection

by Ma Viet Duc, Pham Minh Dang, Tran Thu Phuong, Truong Duc Truong, Vu Hai and Nguyen Huu Thanh

Future Internet 2025, 17(7), 299; https://doi.org/10.3390/fi17070299 - 3 Jul 2025

Abstract

Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. Detecting such evolving threats is especially challenging in decentralized environments where raw traffic data cannot be aggregated due to privacy [...] Read more.

Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. Detecting such evolving threats is especially challenging in decentralized environments where raw traffic data cannot be aggregated due to privacy or policy constraints. To address this, we present FedSAGE, a security-aware federated intrusion detection framework that combines Variational Autoencoder (VAE)-based latent representation learning with unsupervised clustering and resource-efficient client selection. Each client encodes its local domain traffic into a semantic latent space using a shared, pre-trained VAE trained solely on benign domains. These embeddings are clustered via affinity propagation to group clients with similar data distributions and identify outliers indicative of novel threats without requiring any labeled DGA samples. Within each cluster, FedSAGE selects only the fastest clients for training, balancing computational constraints with threat visibility. Experimental results from the multi-zones DGA dataset show that FedSAGE improves detection accuracy by up to 11.6% and reduces energy consumption by up to 93.8% compared to standard FedAvg under non-IID conditions. Notably, the latent clustering perfectly recovers ground-truth DGA family zones, enabling effective anomaly detection in a fully unsupervised manner while remaining privacy-preserving. These foundations demonstrate that FedSAGE is a practical and lightweight approach for decentralized detection of evasive malware, offering a viable solution for secure and adaptive defense in resource-constrained edge environments. Full article

(This article belongs to the Special Issue Security of Computer System and Network)

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24 pages, 4350 KiB

Open AccessArticle

HECS4MQTT: A Multi-Layer Security Framework for Lightweight and Robust Encryption in Healthcare IoT Communications

by Saud Alharbi, Wasan Awad and David Bell

Future Internet 2025, 17(7), 298; https://doi.org/10.3390/fi17070298 - 30 Jun 2025

Abstract

Internet of Things (IoT) technology in healthcare has enabled innovative services that enhance patient monitoring, diagnostics and medical data management. However, securing sensitive health data while maintaining system efficiency of resource-constrained IoT devices remains a critical challenge. This work presents a comprehensive end-to-end [...] Read more.

Internet of Things (IoT) technology in healthcare has enabled innovative services that enhance patient monitoring, diagnostics and medical data management. However, securing sensitive health data while maintaining system efficiency of resource-constrained IoT devices remains a critical challenge. This work presents a comprehensive end-to-end IoT security framework for healthcare environments, addressing encryption at two key levels: lightweight encryption at the edge for resource-constrained devices and robust end-to-end encryption when transmitting data to the cloud via MQTT cloud brokers. The proposed system leverages multi-broker MQTT architecture to optimize resource utilization and enhance message reliability. At the edge, lightweight cryptographic techniques ensure low-latency encryption before transmitting data via a secure MQTT broker hosted within the hospital infrastructure. To safeguard data as it moves beyond the hospital to the cloud, stronger end-to-end encryption are applied to ensure end-to-end security, such as AES-256 and TLS 1.3, to ensure confidentiality and resilience over untrusted networks. A proof-of-concept Python 3.10 -based MQTT implementation is developed using open-source technologies. Security and performance evaluations demonstrate the feasibility of the multi-layer encryption approach, effectively balancing computational overhead with data protection. Security and performance evaluations demonstrate that our novel HECS4MQTT (Health Edge Cloud Security for MQTT) framework achieves a unique balance between efficiency and security. Unlike existing solutions that either impose high computational overhead at the edge or rely solely on transport-layer protection, HECS4MQTT introduces a layered encryption strategy that decouples edge and cloud security requirements. This design minimizes processing delays on constrained devices while maintaining strong cryptographic protection when data crosses trust boundaries. The framework also introduces a lightweight bridge component for re-encryption and integrity enforcement, thereby reducing broker compromise risk and supporting compliance with healthcare security regulations. Our HECS4MQTT framework offers a scalable, adaptable, and trust-separated security model, ensuring enhanced confidentiality, integrity, and availability of healthcare data while remaining suitable for deployment in real-world, latency-sensitive, and resource-limited medical environments. Full article

(This article belongs to the Special Issue Secure Integration of IoT and Cloud Computing)

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25 pages, 2065 KiB

Open AccessArticle

Lower-Latency Screen Updates over QUIC with Forward Error Correction

by Nooshin Eghbal and Paul Lu

Future Internet 2025, 17(7), 297; https://doi.org/10.3390/fi17070297 - 30 Jun 2025

Abstract

There are workloads that do not need the total data ordering enforced by the Transmission Control Protocol (TCP). For example, Virtual Network Computing (VNC) has a sequence of pixel-based updates in which the order of rectangles can be relaxed. However, VNC runs over [...] Read more.

There are workloads that do not need the total data ordering enforced by the Transmission Control Protocol (TCP). For example, Virtual Network Computing (VNC) has a sequence of pixel-based updates in which the order of rectangles can be relaxed. However, VNC runs over the TCP and can have higher latency due to unnecessary blocking to ensure total ordering. By using Quick UDP Internet Connections (QUIC) as the underlying protocol, we are able to implement a partial order delivery approach, which can be combined with Forward Error Correction (FEC) to reduce data latency. Our earlier work on consistency fences provides a mechanism and semantic foundation for partial ordering. Our new evaluation on the Emulab testbed, with two different synthetic workloads for streaming and non-streaming updates, shows that our partial order and FEC strategy can reduce the blocking time and inter-delivery time of rectangles compared to total delivery. For one workload, partially ordered data with FEC can reduce the 99-percentile message-blocking time to 0.4 ms versus 230 ms with totally ordered data. That workload was with 0.5% packet loss, 100 ms Round-Trip Time (RTT), and 100 Mbps bandwidth. We study the impact of varying the packet-loss rate, RTT, bandwidth, and CCA and demonstrate that partial order and FEC latency improvements grow as we increase packet loss and RTT, especially with the emerging Bottleneck Bandwidth and Round-Trip propagation time (BBR) congestion control algorithm. Full article

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21 pages, 2134 KiB

Open AccessArticle

Optimizing Trajectories for Rechargeable Agricultural Robots in Greenhouse Climatic Sensing Using Deep Reinforcement Learning with Proximal Policy Optimization Algorithm

by Ashraf Sharifi, Sara Migliorini and Davide Quaglia

Future Internet 2025, 17(7), 296; https://doi.org/10.3390/fi17070296 - 30 Jun 2025

Abstract

The experimentation of agricultural robots has been increasing in recent years, both in greenhouses and open fields. While agricultural robots are inherently useful for automating various farming tasks, their presence can also be leveraged to collect measurements along their paths. This approach enables [...] Read more.

The experimentation of agricultural robots has been increasing in recent years, both in greenhouses and open fields. While agricultural robots are inherently useful for automating various farming tasks, their presence can also be leveraged to collect measurements along their paths. This approach enables the creation of a complete and detailed picture of the climate conditions inside a greenhouse, reducing the need to distribute a large number of physical devices among the crops. In this regard, choosing the best visiting sequence of the Points of Interest (PoIs) regarding where to perform the measurements deserves particular attention. This trajectory planning has to carefully combine the amount and significance of the collected data with the energy requirements of the robot. In this paper, we propose a method based on Deep Reinforcement Learning enriched with a Proximal Policy Optimization (PPO) algorithm for determining the best trajectory an agricultural robot must follow to balance the number of measurements and autonomy adequately. The proposed approach has been applied to a real-world case study regarding a greenhouse in Verona (Italy) and compared with other existing state-of-the-art approaches. Full article

(This article belongs to the Special Issue Smart Technology: Artificial Intelligence, Robotics and Algorithms)

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22 pages, 1158 KiB

Open AccessArticle

FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments

by Bruno Ramos-Cruz, Francisco J. Quesada-Real, Javier Andreu-Pérez and Jessica Zaqueros-Martinez

Future Internet 2025, 17(7), 295; https://doi.org/10.3390/fi17070295 - 30 Jun 2025

Abstract

In the rapidly evolving landscape of the Internet of Things (IoT), managing the vast volumes of data generated by connected devices presents significant challenges, particularly in B5G IoT environments. One key issue is data redundancy, where identical data is stored several times because [...] Read more.

In the rapidly evolving landscape of the Internet of Things (IoT), managing the vast volumes of data generated by connected devices presents significant challenges, particularly in B5G IoT environments. One key issue is data redundancy, where identical data is stored several times because it is captured by multiple sensors. To address this, we introduce “FODIT”, a filter-based module designed to optimize data storage in IoT systems. FODIT leverages probabilistic data structures, specifically filters, to improve storage efficiency and query performance. We hypothesize that applying these structures can significantly reduce redundancy and accelerate data access in resource-constrained IoT deployments. We validate our hypothesis through targeted simulations under a specific and rare configuration: high-frequency and high-redundancy environments, with controlled duplication rates between 4% and 8%. These experiments involve data storage in local databases, cloud-based systems, and distributed ledger technologies (DLTs). The results demonstrate FODIT’s ability to reduce storage requirements and improve query responsiveness under these stress-test conditions. Furthermore, the proposed approach has broader applicability, particularly in DLT-based environments such as blockchain, where efficient querying remains a critical challenge. Nonetheless, some limitations remain, especially regarding the current data structure used to maintain consistency with the DLT, and the need for further adaptation to real-world contexts with dynamic workloads. This research highlights the potential of filter-based techniques to improve data management in IoT and blockchain systems, contributing to the development of more scalable and responsive infrastructures. Full article

(This article belongs to the Special Issue Network Architectures and Protocols for Edge Learning in B5G IoT Systems)

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24 pages, 798 KiB

Open AccessArticle

ICRSSD: Identification and Classification for Railway Structured Sensitive Data

by Yage Jin, Hongming Chen, Rui Ma, Yanhua Wu and Qingxin Li

Future Internet 2025, 17(7), 294; https://doi.org/10.3390/fi17070294 - 30 Jun 2025

Abstract

The rapid growth of the railway industry has resulted in the accumulation of large structured data that makes data security a critical component of reliable railway system operations. However, existing methods for identifying and classifying often suffer from limitations such as overly coarse [...] Read more.

The rapid growth of the railway industry has resulted in the accumulation of large structured data that makes data security a critical component of reliable railway system operations. However, existing methods for identifying and classifying often suffer from limitations such as overly coarse identification granularity and insufficient flexibility in classification. To address these issues, we propose ICRSSD, a two-stage method for identification and classification in terms of the railway domain. The identification stage focuses on obtaining the sensitivity of all attributes. We first divide structured data into canonical data and semi-canonical data at a finer granularity to improve the identification accuracy. For canonical data, we use information entropy to calculate the initial sensitivity. Subsequently, we update the attribute sensitivities through cluster analysis and association rule mining. For semi-canonical data, we calculate attribute sensitivity by using a combination of regular expressions and keyword lists. In the classification stage, to further enhance accuracy, we adopt a dynamic and multi-granularity classified strategy. It considers the relative sensitivity of attributes across different scenarios and classifies them into three levels based on the sensitivity values obtained during the identification stage. Additionally, we design a rule base specifically for the identification and classification of sensitive data in the railway domain. This rule base enables effective data identification and classification, while also supporting the expiry management of sensitive attribute labels. To improve the efficiency of regular expression generation, we developed an auxiliary tool with the help of large language models and a well-designed prompt framework. We conducted experiments on a real-world dataset from the railway domain. The results demonstrate that ICRSSD significantly improves the accuracy and adaptability of sensitive data identification and classification in the railway domain. Full article

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23 pages, 5161 KiB

Open AccessArticle

AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things

by Talal S. Almuzaini and Andrey V. Savkin

Future Internet 2025, 17(7), 293; https://doi.org/10.3390/fi17070293 - 30 Jun 2025

Abstract

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for [...] Read more.

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments. Full article

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

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32 pages, 4804 KiB

Open AccessArticle

Towards Automatic Detection of Pneumothorax in Emergency Care with Deep Learning Using Multi-Source Chest X-ray Data

by Santiago Ibañez Caturla, Juan de Dios Berná Mestre and Oscar Martinez Mozos

Future Internet 2025, 17(7), 292; https://doi.org/10.3390/fi17070292 - 29 Jun 2025

Abstract

Pneumothorax is a potentially life-threatening condition defined as the collapse of the lung due to air leakage into the chest cavity. Delays in the diagnosis of pneumothorax can lead to severe complications and even mortality. A significant challenge in pneumothorax diagnosis is the [...] Read more.

Pneumothorax is a potentially life-threatening condition defined as the collapse of the lung due to air leakage into the chest cavity. Delays in the diagnosis of pneumothorax can lead to severe complications and even mortality. A significant challenge in pneumothorax diagnosis is the shortage of radiologists, resulting in the absence of written reports in plain X-rays and, consequently, impacting patient care. In this paper, we propose an automatic triage system for pneumothorax detection in X-ray images based on deep learning. We address this problem from the perspective of multi-source domain adaptation where different datasets available on the Internet are used for training and testing. In particular, we use datasets which contain chest X-ray images corresponding to different conditions (including pneumothorax). A convolutional neural network (CNN) with an EfficientNet architecture is trained and optimized to identify radiographic signs of pneumothorax using those public datasets. We present the results using cross-dataset validation, demonstrating the robustness and generalization capabilities of our multi-source solution across different datasets. The experimental results demonstrate the model’s potential to assist clinicians in prioritizing and correctly detecting urgent cases of pneumothorax using different integrated deployment strategies. Full article

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

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20 pages, 2579 KiB

Open AccessArticle

ERA-MADDPG: An Elastic Routing Algorithm Based on Multi-Agent Deep Deterministic Policy Gradient in SDN

by Wanwei Huang, Hongchang Liu, Yingying Li and Linlin Ma

Future Internet 2025, 17(7), 291; https://doi.org/10.3390/fi17070291 - 29 Jun 2025

Abstract

To address the fact that changes in network topology can have an impact on the performance of routing, this paper proposes an Elastic Routing Algorithm based on Multi-Agent Deep Deterministic Policy Gradient (ERA-MADDPG), which is implemented within the framework of Multi-Agent Deep Deterministic [...] Read more.

To address the fact that changes in network topology can have an impact on the performance of routing, this paper proposes an Elastic Routing Algorithm based on Multi-Agent Deep Deterministic Policy Gradient (ERA-MADDPG), which is implemented within the framework of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in deep reinforcement learning. The algorithm first builds a three-layer architecture based on Software-Defined Networking (SDN). The top-down layers are the multi-agent layer, the controller layer, and the data layer. The architecture’s processing flow, including real-time data layer information collection and dynamic policy generation, enables the ERA-MADDPG algorithm to exhibit strong elasticity by quickly adjusting routing decisions in response to topology changes. The actor-critic framework combined with Convolutional Neural Networks (CNN) to implement the ERA-MADDPG routing algorithm effectively improves training efficiency, enhances learning stability, facilitates collaboration, and improves algorithm generalization and applicability. Finally, simulation experiments demonstrate that the convergence speed of the ERA-MADDPG routing algorithm outperforms that of the Multi-Agent Deep Q-Network (MADQN) algorithm and the Smart Routing based on Deep Reinforcement Learning (SR-DRL) algorithm, and the training speed in the initial phase is improved by approximately 20.9% and 39.1% compared to the MADQN algorithm and SR-DRL algorithm, respectively. The elasticity performance of ERA-MADDPG is quantified by re-convergence speed: under 5–15% topology node/link changes, its re-convergence speed is over 25% faster than that of MADQN and SR-DRL, demonstrating superior capability to maintain routing efficiency in dynamic environments. Full article

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24 pages, 649 KiB

Open AccessSystematic Review

Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review

by Juan Ochoa-Aldeán, Carlos Silva-Cárdenas, Renato Torres, Jorge Ivan Gonzalez and Sergio Fortes

Future Internet 2025, 17(7), 290; https://doi.org/10.3390/fi17070290 - 28 Jun 2025

Abstract

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of [...] Read more.

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration. Full article

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

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33 pages, 8285 KiB

Open AccessArticle

TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing

by Hisham Ali, William J. Buchanan, Jawad Ahmad, Marwan Abubakar, Muhammad Shahbaz Khan and Isam Wadhaj

Future Internet 2025, 17(7), 289; https://doi.org/10.3390/fi17070289 - 27 Jun 2025

Abstract

We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines Ciphertext-Policy [...] Read more.

We introduce TrustShare, a novel blockchain-based framework designed to enable secure, privacy-preserving, and trust-aware cyber threat intelligence (CTI) sharing across organizational boundaries. Leveraging Hyperledger Fabric, the architecture supports fine-grained access control and immutability through smart contract-enforced trust policies. The system combines Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with temporal, spatial, and controlled revelation constraints to grant data owners precise control over shared intelligence. To ensure scalable decentralized storage, encrypted CTI is distributed via the IPFS, with blockchain-anchored references ensuring verifiability and traceability. Using STIX for structuring and TAXII for exchange, the framework complies with the GDPR requirements, embedding revocation and the right to be forgotten through certificate authorities. The experimental validation demonstrates that TrustShare achieves low-latency retrieval, efficient encryption performance, and robust scalability in containerized deployments. By unifying decentralized technologies with cryptographic enforcement and regulatory compliance, TrustShare sets a foundation for the next generation of sovereign and trustworthy threat intelligence collaboration. Full article

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

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27 pages, 8848 KiB

Open AccessArticle

Empirical Investigation on Practical Robustness of Keystroke Recognition Using WiFi Sensing for Future IoT Applications

by Haoming Wang, Aryan Sharma, Deepak Mishra, Aruna Seneviratne and Eliathamby Ambikairajah

Future Internet 2025, 17(7), 288; https://doi.org/10.3390/fi17070288 - 27 Jun 2025

Abstract

The widespread use of WiFi Internet-of-Things (IoT) devices has rendered them valuable tools for detecting information about the physical environment. Recent studies have demonstrated that WiFi Channel State Information (CSI) can detect physical events like movement, occupancy increases, and gestures. This paper empirically [...] Read more.

The widespread use of WiFi Internet-of-Things (IoT) devices has rendered them valuable tools for detecting information about the physical environment. Recent studies have demonstrated that WiFi Channel State Information (CSI) can detect physical events like movement, occupancy increases, and gestures. This paper empirically investigates the conditions under which WiFi sensing technology remains effective for keystroke detection. To achieve this timely goal of assessing whether it can raise any privacy concerns, experiments are conducted using commodity hardware to predict the accuracy of WiFi CSI in detecting keys pressed on a keyboard. Our novel results show that, in an ideal setting with a robotic arm, the position of a specific key can be predicted with

99 %

accuracy using a simple machine learning classifier. Furthermore, human finger localisation over a key and actual key-press recognition is also successfully achieved, with

94 %

and

89 %

reduced accuracy values, respectively. Moreover, our detailed investigation reveals that to ensure high accuracy, the gap distance between each test object must be substantial, while the size of the test group should be limited. Finally, we show WiFi sensing technology has limitations in small-scale gesture recognition for generic settings where proper device positioning is crucial. Specifically, detecting keyed words achieves an overall accuracy of

94 %

for the forefinger and

87 %

for multiple fingers when only the right hand is used. Accuracy drops to

56 %

when using both hands. We conclude WiFi sensing is effective in controlled indoor environments, but it has limitations due to the device location and the limited granularity of sensing objects. Full article

(This article belongs to the Special Issue Efficient and Secure Wireless Communications and Networking: Architecture and Applications)

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27 pages, 2004 KiB

Open AccessArticle

Cross-Lingual Cross-Domain Transfer Learning for Rumor Detection

by Eliana Providel, Marcelo Mendoza and Mauricio Solar

Future Internet 2025, 17(7), 287; https://doi.org/10.3390/fi17070287 - 26 Jun 2025

Abstract

This study introduces a novel method that merges propagation-based transfer learning with word embeddings for rumor detection. This approach aims to use data from languages with abundant resources to enhance performance in languages with limited availability of annotated corpora in this task. Furthermore, [...] Read more.

This study introduces a novel method that merges propagation-based transfer learning with word embeddings for rumor detection. This approach aims to use data from languages with abundant resources to enhance performance in languages with limited availability of annotated corpora in this task. Furthermore, we augment our rumor detection framework with two supplementary tasks—stance classification and bot detection—to reinforce the primary task of rumor detection. Utilizing our proposed multi-task system, which incorporates cascade learning models, we generate several pre-trained models that are subsequently fine-tuned for rumor detection in English and Spanish. The results show improvements over the baselines, thus empirically validating the efficacy of our proposed approach. A Macro-F1 of 0.783 is achieved for the Spanish language, and a Macro-F1 of 0.945 is achieved for the English language. Full article

(This article belongs to the Special Issue AI Based Natural Language Processing: Emerging Approaches and Applications)

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18 pages, 1059 KiB

Open AccessArticle

Exponential Backoff and Its Security Implications for Safety-Critical OT Protocols over TCP/IP Networks

by Matthew Boeding, Paul Scalise, Michael Hempel, Hamid Sharif and Juan Lopez, Jr.

Future Internet 2025, 17(7), 286; https://doi.org/10.3390/fi17070286 - 26 Jun 2025

Abstract

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication [...] Read more.

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks. Full article

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

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22 pages, 2732 KiB

Open AccessArticle

AI-Based Learning Recommendations: Use in Higher Education

by Prabin Dahal, Saptadi Nugroho, Claudia Schmidt and Volker Sänger

Future Internet 2025, 17(7), 285; https://doi.org/10.3390/fi17070285 - 26 Jun 2025

Abstract

We propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis [...] Read more.

We propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis on learning support and learner motivation, incorporating various recommendation types such as content-based, collaborative, and session-based recommendations to provide the next learning resources given by lecturers and retrieved from the content curation of Open Educational Resources (OER) for the learners. In addition, we integrated a chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with AI-based recommendations to provide an effective learning experience. Full article

(This article belongs to the Special Issue Deep Learning in Recommender Systems)

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30 pages, 9859 KiB

Open AccessArticle

Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector

by Germán Rodríguez-Galán, Eduardo Benavides-Astudillo, Daniel Nuñez-Agurto, Pablo Puente-Ponce, Sonia Cárdenas-Delgado and Mauricio Loachamín-Valencia

Future Internet 2025, 17(7), 284; https://doi.org/10.3390/fi17070284 - 26 Jun 2025

Abstract

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server [...] Read more.

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article

(This article belongs to the Section Cybersecurity)

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33 pages, 1292 KiB

Open AccessSystematic Review

Metaverse Architectures: A Comprehensive Systematic Review of Definitions and Frameworks

by Cemile Boztas, Essam Ghadafi and Rasha Ibrahim

Future Internet 2025, 17(7), 283; https://doi.org/10.3390/fi17070283 - 26 Jun 2025

Abstract

The metaverse is a multidimensional reality space that represents the evolving interface between humans, digital systems, and spatial computing. This research aims to provide a comprehensive synthesis of metaverse definitions and architectural frameworks through the analysis of academic literature. A systematic literature review [...] Read more.

The metaverse is a multidimensional reality space that represents the evolving interface between humans, digital systems, and spatial computing. This research aims to provide a comprehensive synthesis of metaverse definitions and architectural frameworks through the analysis of academic literature. A systematic literature review of 103 peer-reviewed studies was conducted, from which 33 explicit definitions and 25 architectural models were selected for detailed analysis. A mixed-methods qualitative approach was employed, combining a systematic literature analysis, word frequency analysis, and a thematic analysis to develop an expanded conceptual and architectural understanding. The analysis of 33 definitions revealed eight recurring conceptual themes, which were synthesised into five overarching categories: conceptual and linguistic constructs, spatial-environmental design, technological orientation, economic and governance systems, and social and human interaction. A total of 25 studies proposing architectural models are categorised into three thematic groups: technology-based, domain-based and layered architectures. A significant contribution of this study is the categorisation and comparative evaluation of foundational architectural models, specifically the extensively referenced three-, four-, and five-layer frameworks. The findings are structured around two primary areas of focus: the conceptual definition and the comparative analysis of established metaverse architecture models, focusing on a set of well-established models that exhibit conceptual clarity, structural coherence, and widespread acceptance in the literature. This integrated methodological approach not only offers a dual-perspective analysis that bridges conceptualisation and system design but also introduces an analytical framework that supports future standardisation and governance discussions. Full article

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24 pages, 7080 KiB

Open AccessReview

Responsible Resilience in Cyber–Physical–Social Systems: A New Paradigm for Emergent Cyber Risk Modeling

by Theresa Sobb, Nour Moustafa and Benjamin Turnbull

Future Internet 2025, 17(7), 282; https://doi.org/10.3390/fi17070282 - 25 Jun 2025

Abstract

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human [...] Read more.

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

(This article belongs to the Special Issue Internet of Things and Cyber-Physical Systems, 3rd Edition)

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