Future Internet

30 pages, 5361 KiB

Open AccessArticle

Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration

by Negin Jahanbakhsh, Mario Vega-Barbas, Iván Pau, Lucas Elvira-Martín, Hirad Moosavi and Carolina García-Vázquez

Future Internet 2025, 17(5), 198; https://doi.org/10.3390/fi17050198 (registering DOI) - 29 Apr 2025

Abstract

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, [...] Read more.

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)

31 pages, 1996 KiB

Open AccessArticle

Leveraging Blockchain Technology for Secure 5G Offloading Processes

by Cristina Regueiro, Santiago de Diego and Borja Urkizu

Future Internet 2025, 17(5), 197; https://doi.org/10.3390/fi17050197 (registering DOI) - 29 Apr 2025

Abstract

This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, [...] Read more.

This paper presents a secure 5G offloading mechanism leveraging Blockchain technology and Self-Sovereign Identity (SSI). The advent of 5G has significantly enhanced the capabilities of all sectors, enabling innovative applications and improving security and efficiency. However, challenges such as limited infrastructure, signal interference, and high upgrade costs persist. Offloading processes already address these issues but they require more transparency and security. This paper proposes a Blockchain-based marketplace using Hyperledger Fabric to optimize resource allocation and enhance security. This marketplace facilitates the exchange of services and resources among operators, promoting competition and flexibility. Additionally, the paper introduces an SSI-based authentication system to ensure privacy and security during the offloading process. The architecture and components of the marketplace and authentication system are detailed, along with their data models and operations. Performance evaluations indicate that the proposed solutions do not significantly degrade offloading times, making them suitable for everyday applications. As a result, the integration of Blockchain and SSI technologies enhances the security and efficiency of 5G offloading. Full article

(This article belongs to the Special Issue 5G Security: Challenges, Opportunities, and the Road Ahead)

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27 pages, 960 KiB

Open AccessArticle

Ephemeral Node Identifiers for Enhanced Flow Privacy

by Gregor Tamati Haywood and Saleem Noel Bhatti

Future Internet 2025, 17(5), 196; https://doi.org/10.3390/fi17050196 - 28 Apr 2025

Viewed by 24

Abstract

The Internet Protocol (IP) uses numerical address values carried in IP packets at the network layer to allow correct forwarding of packets between source and destination. Those address values must be kept visible in all parts of the network. By definition, those addresses [...] Read more.

The Internet Protocol (IP) uses numerical address values carried in IP packets at the network layer to allow correct forwarding of packets between source and destination. Those address values must be kept visible in all parts of the network. By definition, those addresses must carry enough information to identify the source and destination for the communication. This means that successive flows of IP packets can be correlated—it is possible for an observer of the flows to easily link them to an individual source and so, potentially, to an individual user. To alleviate this privacy concern, it is desirable to have ephemeral address values—values that have a limited lifespan and so make flow correlation more difficult for an attacker. However, the IP address is also used in the end-to-end communication state for transport layer flows so must remain consistent to allow correct operation at the transport layer. We present a solution to this tension in requirements by the use of ephemeral Node Identifier (eNID) values in IP packets as part of the address value. We have implemented our approach as an extension to IPv6 in the FreeBSD14 operating system kernel. We have evaluated the implementation with existing applications over both a testbed network in a controlled environment, as well as with global IPv6 network connectivity. Our results show that eNIDs work with existing applications and over existing IPv6 networks. Our analyses shows that using eNIDs creates a disruption to the correlation of flows and so effectively perturbs linkability. As our approach is a network layer (layer 3) mechanism, it is usable by any transport layer (layer 4) protocol, improving privacy for all applications and all users. Full article

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

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28 pages, 11862 KiB

Open AccessArticle

An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements

by Tresna Maulana Fahrudin, Nobuo Funabiki, Komang Candra Brata, Inzali Naing, Soe Thandar Aung, Amri Muhaimin and Dwi Arman Prasetya

Future Internet 2025, 17(5), 195; https://doi.org/10.3390/fi17050195 - 28 Apr 2025

Viewed by 32

Abstract

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their [...] Read more.

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their contents. To solve this drawback, we have proposed a reference paper collection system using a web scraping technology and natural language models. However, our previous system often finds a limited number of relevant reference papers after taking long time, since it relies on one paper search website and runs on a single thread at a multi-core CPU. In this paper, we present an improved reference paper collection system with three enhancements to solve them: (1) integrating the APIs from multiple paper search web sites, namely, the bulk search endpoint in the Semantic Scholar API, the article search endpoint in the DOAJ API, and the search and fetch endpoint in the PubMed API to retrieve article metadata, (2) running the program on multiple threads for multi-core CPU, and (3) implementing Dynamic URL Redirection, Regex-based URL Parsing, and HTML Scraping with URL Extraction for fast checking of PDF file accessibility, along with sentence embedding to assess relevance based on semantic similarity. For evaluations, we compare the number of obtained reference papers and the response time between the proposal, our previous work, and common literature search tools in five reference paper queries. The results show that the proposal increases the number of relevant reference papers by 64.38% and reduces the time by 59.78% on average compared to our previous work, while outperforming common literature search tools in reference papers. Thus, the effectiveness of the proposed system has been demonstrated in our experiments. Full article

(This article belongs to the Special Issue ICT and AI in Intelligent E-systems)

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20 pages, 1267 KiB

Open AccessArticle

BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network

by Wanwei Huang, Huicong Yu, Yingying Li, Xi He and Rui Chen

Future Internet 2025, 17(5), 194; https://doi.org/10.3390/fi17050194 - 27 Apr 2025

Viewed by 154

Abstract

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup [...] Read more.

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. After iterative training of the BPDM-GCN algorithm, the comprehensive link weights within the network topology are generated. Then, according to the comprehensive link weight and taking the shortest path as the optimization objective, a backup path implementation method based on the incremental shortest path tree is designed to reduce the phasor data transmission delay in the backup path. In conclusion, the experimental results show that the backup path formulated by this algorithm exhibits reduced data transmission delay, minimal path extension, and a high success rate in recovering failed links. Compared to the superior NRLF-RL algorithm, the BPDM-GCN algorithm achieves a reduction of approximately 14.29% in the average failure link recovery delay and an increase of approximately 5.24% in the failure link recovery success rate. Full article

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17 pages, 3936 KiB

Open AccessArticle

Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security

by Guobin Xu, Oluwole Adetifa, Jianzhou Mao, Eric Sakk and Shuangbao Wang

Future Internet 2025, 17(5), 193; https://doi.org/10.3390/fi17050193 - 26 Apr 2025

Viewed by 62

Abstract

Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional [...] Read more.

Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEaaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security. 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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30 pages, 5336 KiB

Open AccessArticle

Railway Cloud Resource Management as a Service

by Ivaylo Atanasov, Dragomira Dimitrova, Evelina Pencheva and Ventsislav Trifonov

Future Internet 2025, 17(5), 192; https://doi.org/10.3390/fi17050192 - 24 Apr 2025

Viewed by 177

Abstract

Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry [...] Read more.

Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry has the potential to enhance operational efficiency, data management, and overall system performance. Cloud management is essential for complex systems, and the automation of management services can speed up the provisioning, deployment, and maintenance of cloud infrastructure and applications by enabling visibility across the environment. It can provide consistent and unified management over resource allocation, streamline security processes, and automate the monitoring of key performance indicators. Key railway cloud management challenges include the lack of open interfaces and standardization, which are related to the vendor lock-in problem. In this paper, we propose an approach to design the railway cloud resource management as a service. Based on typical use cases, the requirements to fault and performance management of the railway cloud resources are identified. The main functionality is designed as RESTful services. The approach feasibility is proved by formal verification of the cloud resource management models supported by cloud management application and services. The proposed approach is open, in contrast to any proprietary solutions and feature scalability and interoperability. Full article

(This article belongs to the Special Issue Cloud and Edge Computing for the Next-Generation Networks)

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38 pages, 4044 KiB

Open AccessArticle

Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings

by Rosario G. Garroppo, Pietro Giuseppe Giardina, Giada Landi and Marco Ruta

Future Internet 2025, 17(5), 191; https://doi.org/10.3390/fi17050191 - 23 Apr 2025

Viewed by 159

Abstract

Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. [...] Read more.

Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. Furthermore, the design of the proposed method considers three key principles: sustainability, adaptability, and trustworthiness. The proposed data pre-processing and engineering system significantly reduces the amount of data to be processed by the CNN, helping to limit the processing load and associated energy consumption towards more sustainable Artificial Intelligence (AI) techniques. Furthermore, the data engineering process, which includes sampling, feature extraction, and transformation of data into images, is designed considering its adaptability to integrate new sensor data and to fit seamlessly into a zero-touch system, following the principles of Machine Learning Operations (MLOps). The designed CNNs allow for the investigation of AI reasoning, implementing eXplainable AI (XAI) techniques such as the correlation map analyzed in this paper. Using the ToN-IoT dataset, the results show that the proposed FL-IDS achieves performance comparable to that of its centralized counterpart. To address the specific vulnerabilities of FL, a secure and robust aggregation method is introduced, making the system resistant to poisoning attacks from up to

20 %

of the participating clients. 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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17 pages, 1488 KiB

Open AccessArticle

A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs

by Avnish Malde, Vishnunarayan Girishan Prabhu, Dishant Banga, Michael Hsieh, Chaithanya Renduchintala and Ronald Pirrallo

Future Internet 2025, 17(5), 190; https://doi.org/10.3390/fi17050190 - 22 Apr 2025

Viewed by 222

Abstract

According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income [...] Read more.

According to the World Health Organization, maternal mortality rates remain a critical public health issue, with 94% of maternal deaths occurring in low- and middle-income countries (LMICs), where the rates reached 430 per 100,000 live births in 2020 compared to 13 in high-income countries. Despite this difference, only a few studies have investigated whether sparse data and features such as vital signs can effectively predict maternal health risks. This study addresses this gap by evaluating the predictive capability of vital sign data using machine learning models trained on a dataset of 1014 pregnant women from rural Bangladesh. This study developed multiple machine learning models using a dataset containing age, blood pressure, temperature, heart rate, and blood glucose of 1014 pregnant women from rural Bangladesh. The models’ performance were evaluated using regular, random and stratified sampling techniques. Additionally, we developed a stacking ensemble machine learning model combining multiple methods to evaluate predictive accuracy. A key contribution of this study is developing a stacking ensemble model combined with stratified sampling, an approach not previously considered in maternal health risk prediction. The ensemble model using stratified sampling achieved the highest accuracy (87.2%), outperforming CatBoost (84.7%), XGBoost (84.2%), random forest (81.3%) and decision trees (80.3%) without stratified sampling. Observations from our study demonstrate the feasibility of using sparse data and features for maternal health risk prediction using algorithms. By focusing on data from resource-constrained settings, we show that machine learning offers a convenient and accessible solution to improve prenatal care and reduce maternal deaths in LMICs. Full article

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

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23 pages, 5845 KiB

Open AccessArticle

Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR

by Mingjun Wang, Xuezhi Zhang, Feng Jing and Mei Gao

Future Internet 2025, 17(5), 189; https://doi.org/10.3390/fi17050189 - 22 Apr 2025

Viewed by 154

Abstract

The rapid development of wireless network technology and the continuous evolution of network service demands have raised higher requirements for congestion control algorithms. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm based on the Transmission Control [...] Read more.

The rapid development of wireless network technology and the continuous evolution of network service demands have raised higher requirements for congestion control algorithms. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm based on the Transmission Control Protocol (TCP) protocol. While BBR offers lower latency and higher throughput compared to traditional congestion control algorithms, it still faces challenges. These include the periodic triggering of the ProbeRTT phase, which impairs data transmission efficiency, data over-injection caused by the congestion window (CWND) value-setting policy, and the difficulty of coordinating resource allocation across multiple concurrent flows. These limitations make BBR less effective in multi-stream competition scenarios in high-speed wireless networks. This paper analyzes the design limitations of the BBR algorithm from a theoretical perspective and proposes the Adaptive-BBR (Ad-BBR) algorithm. The Ad-BBR algorithm incorporates real-time RTT and link queue-state information, introduces a new RTprop determination mechanism, and implements a finer-grained, RTT-based adaptive transmission rate adjustment mechanism to reduce data over-injection and improve RTT fairness. Additionally, the ProbeRTT phase-triggering mechanism is updated to ensure more stable and smoother data transmission. In the NS3, 5G, and Wi-Fi simulation experiments, Ad-BBR outperformed all comparison algorithms by effectively mitigating data over-injection and minimizing unnecessary entries into the ProbeRTT phase. Compared to the BBRv1 algorithm, Ad-BBR achieved a 17% increase in throughput and a 30% improvement in RTT fairness, along with a 13% reduction in the retransmission rate and an approximate 20% decrease in latency. Full article

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17 pages, 17464 KiB

Open AccessArticle

Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach

by Anand Raju and Sathishkumar Samiappan

Future Internet 2025, 17(5), 188; https://doi.org/10.3390/fi17050188 - 22 Apr 2025

Viewed by 126

Abstract

This paper represents a new method for the extraction of features from 5G signals using spectrogram and quantum cat swarm optimization (QCSO). The proposed approach uses a discrete wavelet transform (DWT)-based convolutional neural network (W-CNN) to enhance the extracted features and improve the [...] Read more.

This paper represents a new method for the extraction of features from 5G signals using spectrogram and quantum cat swarm optimization (QCSO). The proposed approach uses a discrete wavelet transform (DWT)-based convolutional neural network (W-CNN) to enhance the extracted features and improve the signal classification. The combination of QCSO and W-CNN is designed to enable improved signal recognition and dimension reduction. Our results demonstrate an improvement in the 5G signal feature extraction performance with the use of this novel approach. The QCSO shows improvement in seven out of eight parameters studied when compared to five other state-of-the-art optimization methods. Full article

(This article belongs to the Special Issue 5G/6G and Beyond: The Future of Wireless Communications Systems)

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20 pages, 12983 KiB

Open AccessArticle

Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

by Raiyen Z. Rakin, Mahmudur Rahman, Kanij F. Borsa, Fahmid Al Farid, Shakila Rahman, Jia Uddin and Hezerul Abdul Karim

Future Internet 2025, 17(5), 187; https://doi.org/10.3390/fi17050187 - 22 Apr 2025

Viewed by 406

Abstract

The current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has been proposed which analyzes infrastructure [...] Read more.

The current infrastructure is crucial to metropolitan improvement. Natural factors, aging, and overuse cause these structures to deteriorate, introducing dangers to public well-being. Timely detection of infrastructure failures requires an effective solution. A YOLOv11-based deep learning model has been proposed which analyzes infrastructure and detects faults in civil architecture. The focus of this study is on an image-based approach to infrastructure assessment, which is an alternative to manual visual inspections. Despite not explicitly modeling infrastructure deterioration, the proposed method is designed to automate defect identification based on visual cues. A customized dataset was created with 9116 images collected from various platforms. The dataset was pre-processed, i.e., annotated, and after pre-processing, the proposed model was trained. After training, our proposed model finds defects with greater precision and speed than conventional defect detection techniques. It achieves high performance with precision, recall, F1 score, and mAP in 100 epochs, and is therefore reliable for applications in civil engineering and urban infrastructure monitoring. Finally, the detection results show that the proposed YOLOv11 model works better than other baseline algorithms (YOLOv8, YOLOv9, and YOLOv10) and is more accurate at finding infrastructure problems in real-world scenarios. Full article

(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)

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15 pages, 4047 KiB

Open AccessArticle

Using Machine Learning to Detect Vault (Anti-Forensic) Apps

by Michael N. Johnstone, Wencheng Yang and Mohiuddin Ahmed

Future Internet 2025, 17(5), 186; https://doi.org/10.3390/fi17050186 - 22 Apr 2025

Viewed by 184

Abstract

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static [...] Read more.

Content hiding, or vault applications (apps), are designed with a secondary, often concealed purpose, such as encrypting and storing files. While these apps may serve legitimate functions, they unequivocally present significant challenges for law enforcement. Conventional methods for tackling this issue, whether static or dynamic, prove inadequate when devices—typically smartphones—cannot be modified. Additionally, these methods frequently require prior knowledge of which apps are classified as vault apps. This research decisively demonstrates that a non-invasive method of app analysis, combined with machine learning, can effectively identify vault apps. Our findings reveal that it is entirely possible to detect an Android vault app with 98% accuracy using a random forest classifier. This clearly indicates that our approach can be instrumental for law enforcement in their efforts to address this critical issue. Full article

(This article belongs to the Collection Machine Learning Approaches for User Identity)

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Future Internet, Volume 17, Issue 5 (May 2025) – 13 articles

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