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Future Internet, Volume 16, Issue 7 (July 2024) – 39 articles

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34 pages, 4682 KiB  
Article
Microservice-Based Vehicular Network for Seamless and Ultra-Reliable Communications of Connected Vehicles
by Mira M. Zarie, Abdelhamied A. Ateya, Mohammed S. Sayed, Mohammed ElAffendi and Mohammad Mahmoud Abdellatif
Future Internet 2024, 16(7), 257; https://doi.org/10.3390/fi16070257 (registering DOI) - 19 Jul 2024
Abstract
The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the [...] Read more.
The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. Full article
(This article belongs to the Special Issue Moving towards 6G Wireless Technologies)
22 pages, 3305 KiB  
Article
Behind the Code: Identifying Zero-Day Exploits in WordPress
by Mohamed Azarudheen Mohamed Mohideen, Muhammad Shahroz Nadeem, James Hardy, Haider Ali, Umair Ullah Tariq, Fariza Sabrina, Muhammad Waqar and Salman Ahmed
Future Internet 2024, 16(7), 256; https://doi.org/10.3390/fi16070256 - 19 Jul 2024
Viewed by 107
Abstract
The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers [...] Read more.
The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers are aware of. In our study, we focused on dynamic application security testing (DAST) to investigate cross-site scripting (XSS) attacks. We closely examined 23 popular WordPress plugins, especially those requiring user or admin interactions, as these are frequent targets for XSS attacks. Our testing uncovered previously unknown zero-day vulnerabilities in three of these plugins. Through controlled environment testing, we accurately identified and thoroughly analyzed these XSS vulnerabilities, revealing their mechanisms, potential impacts, and the conditions under which they could be exploited. One of the most concerning findings was the potential for admin-side attacks, which could lead to multi-site insider threats. Specifically, we found vulnerabilities that allow for the insertion of malicious scripts, creating backdoors that unauthorized users can exploit. We demonstrated the severity of these vulnerabilities by employing a keylogger-based attack vector capable of silently capturing and extracting user data from the compromised plugins. Additionally, we tested a zero-click download strategy, allowing malware to be delivered without any user interaction, further highlighting the risks posed by these vulnerabilities. The National Institute of Standards and Technology (NIST) recognized these vulnerabilities and assigned them CVE numbers: CVE-2023-5119 for the Forminator plugin, CVE-2023-5228 for user registration and contact form issues, and CVE-2023-5955 for another critical plugin flaw. Our study emphasizes the critical importance of proactive security measures, such as rigorous input validation, regular security testing, and timely updates, to mitigate the risks posed by zero-day vulnerabilities. It also highlights the need for developers and administrators to stay vigilant and adopt strong security practices to defend against evolving threats. Full article
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15 pages, 408 KiB  
Article
Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
by Bo Cui, Yachao Chai, Zhen Yang and Keqin Li
Future Internet 2024, 16(7), 255; https://doi.org/10.3390/fi16070255 - 18 Jul 2024
Viewed by 196
Abstract
Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the [...] Read more.
Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model’s ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model. Full article
(This article belongs to the Special Issue Cybersecurity in the IoT)
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32 pages, 15790 KiB  
Review
Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era
by Rui Yu, Sooyeon Lee, Jingyi Xie, Syed Masum Billah and John M. Carroll
Future Internet 2024, 16(7), 254; https://doi.org/10.3390/fi16070254 - 18 Jul 2024
Viewed by 207
Abstract
Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to understand the technical and navigational challenges faced by [...] Read more.
Remote sighted assistance (RSA) has emerged as a conversational technology aiding people with visual impairments (VI) through real-time video chat communication with sighted agents. We conducted a literature review and interviewed 12 RSA users to understand the technical and navigational challenges faced by both agents and users. The technical challenges were categorized into four groups: agents’ difficulties in orienting and localizing users, acquiring and interpreting users’ surroundings and obstacles, delivering information specific to user situations, and coping with poor network connections. We also presented 15 real-world navigational challenges, including 8 outdoor and 7 indoor scenarios. Given the spatial and visual nature of these challenges, we identified relevant computer vision problems that could potentially provide solutions. We then formulated 10 emerging problems that neither human agents nor computer vision can fully address alone. For each emerging problem, we discussed solutions grounded in human–AI collaboration. Additionally, with the advent of large language models (LLMs), we outlined how RSA can integrate with LLMs within a human–AI collaborative framework, envisioning the future of visual prosthetics. Full article
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29 pages, 8035 KiB  
Article
A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
by Prabu Kaliyaperumal, Sudhakar Periyasamy, Manikandan Thirumalaisamy, Balamurugan Balusamy and Francesco Benedetto
Future Internet 2024, 16(7), 253; https://doi.org/10.3390/fi16070253 - 18 Jul 2024
Viewed by 239
Abstract
The proliferation of IoT services has spurred a surge in network attacks, heightening cybersecurity concerns. Essential to network defense, intrusion detection and prevention systems (IDPSs) identify malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, infiltration, and [...] Read more.
The proliferation of IoT services has spurred a surge in network attacks, heightening cybersecurity concerns. Essential to network defense, intrusion detection and prevention systems (IDPSs) identify malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, infiltration, and Heartbleed. This study focuses on leveraging unsupervised learning for training detection models to counter these threats effectively. The proposed method utilizes basic autoencoders (bAEs) for dimensionality reduction and encompasses a three-stage detection model: one-class support vector machine (OCSVM) and deep autoencoder (dAE) attack detection, complemented by density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. Accurately delineated clusters aid in mapping attack tactics. The MITRE ATT&CK framework establishes a “Cyber Threat Repository”, cataloging attacks and tactics, enabling immediate response based on priority. Leveraging preprocessed and unlabeled normal network traffic data, this approach enables the identification of novel attacks while mitigating the impact of imbalanced training data on model performance. The autoencoder method utilizes reconstruction error, OCSVM employs a kernel function to establish a hyperplane for anomaly detection, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determining cluster count, ensuring scalability, and minimizing false positives and false negatives. Evaluated on standard datasets such as CIC-IDS2017 and CSECIC-IDS2018, the proposed model outperforms existing state of art methods. Our approach achieves accuracies exceeding 98% for the two datasets, thus confirming its efficacy and effectiveness for application in efficient intrusion detection systems. Full article
(This article belongs to the Special Issue Cybersecurity in the IoT)
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44 pages, 2594 KiB  
Article
NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
by Oleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Ruslan Shevchuk and Mikolaj Karpinski
Future Internet 2024, 16(7), 252; https://doi.org/10.3390/fi16070252 (registering DOI) - 17 Jul 2024
Viewed by 297
Abstract
In the rapidly evolving field of digital asset management, centralized and decentralized global registries have become essential tools for organizing, tracking, and distributing digital assets. However, existing systems often face challenges regarding security, censorship resistance, interoperability, customizability, and scalability. This research paper aims [...] Read more.
In the rapidly evolving field of digital asset management, centralized and decentralized global registries have become essential tools for organizing, tracking, and distributing digital assets. However, existing systems often face challenges regarding security, censorship resistance, interoperability, customizability, and scalability. This research paper aims to address these gaps by proposing a novel decentralized global registry system based on blockchain technology and non-fungible tokens (NFTs). The research paper makes several key contributions to the field of digital asset management. First, it provides a detailed system design for the proposed decentralized global registry, outlining its architectural components, functional modules, and integration with blockchain and NFT technologies. Second, it offers a thorough comparative analysis of the advantages and limitations of the proposed system in relation to existing centralized and decentralized registries. Finally, the paper presents potential use cases and practical applications of the proposed system in various industries, demonstrating its versatility and adaptability to different contexts and requirements. In conclusion, this research paper contributes significantly to the ongoing efforts to improve digital asset management by presenting a novel, decentralized global registry system based on blockchain technology and NFTs. The proposed system addresses the key limitations of existing solutions and offers a promising direction for future research and development in this critical field. Full article
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17 pages, 432 KiB  
Article
SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information
by Shaopei Gao, Qiang Liu, Junjie Zeng and Li Li
Future Internet 2024, 16(7), 251; https://doi.org/10.3390/fi16070251 - 17 Jul 2024
Viewed by 193
Abstract
To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, [...] Read more.
To mitigate the control overhead of Software-Defined Mobile Ad Hoc Networks (SD-MANETs), this paper proposes a novel approach, termed Software-Defined Greedy Perimeter Stateless Routing (SD-GPSR), which integrates geographical location information. SD-GPSR optimizes routing functions by decentralizing them within the data plane of SD-MANET, utilizing the geographic location information of nodes to enhance routing efficiency. The controller is primarily responsible for providing location services and facilitating partial centralized decision-making. Within the data plane, nodes employ an enhanced distance and angle-based greedy forwarding algorithm, denoted as GPSR_DA, to efficiently forward data. Additionally, to address the issue of routing voids in the data plane, we employ the A* algorithm to compute an optimal routing path that circumvents such voids. Finally, we conducted a comparative analysis with several state-of-the-art approaches. The evaluation experiments demonstrate that SD-GPSR significantly reduces the control overhead of the network. Simultaneously, there is a notable improvement in both end-to-end latency and packet loss rate across the network. Full article
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23 pages, 841 KiB  
Review
Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges
by Amr Adel and Tony Jan
Future Internet 2024, 16(7), 250; https://doi.org/10.3390/fi16070250 - 13 Jul 2024
Viewed by 346
Abstract
In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and [...] Read more.
In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and drone forensics. This research addresses the unique requirements of UAV security, outlines various threat models, and explores diverse solutions to ensure data integrity. Drone forensics, a field dedicated to the investigation of security incidents involving UAVs, has been extensively examined and demonstrates its relevance in identifying attack origins or establishing accident causes. This paper further surveys artifacts, tools, and benchmark datasets that are critical in the domain of drone forensics, providing a comprehensive view of current capabilities. Acknowledging the ongoing challenges in UAV security, particularly given the pace of technological advancement and complex operational environments, this study underscores the need for increased collaboration, updated security protocols, and comprehensive regulatory frameworks. Ultimately, this research contributes to a deeper understanding of UAV cybersecurity and aids in fostering future research into the secure and reliable operation of drones. Full article
(This article belongs to the Special Issue Cybersecurity in the IoT)
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16 pages, 2805 KiB  
Article
The Use of Virtual Reality in the Countries of the Central American Bank for Economic Integration (CABEI)
by Álvaro Antón-Sancho, Pablo Fernández-Arias, Edwan Anderson Ariza and Diego Vergara
Future Internet 2024, 16(7), 249; https://doi.org/10.3390/fi16070249 - 13 Jul 2024
Viewed by 283
Abstract
In recent years, virtual reality (VR) technologies have become one of the teaching tools with the greatest training potential in higher education. Thus, the study of factors that influence the adoption and valuation of VR by the educational agents involved is a fruitful [...] Read more.
In recent years, virtual reality (VR) technologies have become one of the teaching tools with the greatest training potential in higher education. Thus, the study of factors that influence the adoption and valuation of VR by the educational agents involved is a fruitful line of research, because it can provide keys to promote its incorporation. This article compares the assessments of VR as a teaching technology in higher education given by professors from countries that are members of the Central American Bank for Economic Integration (CABEI) with those of professors from countries in the Latin American region that are not members of CABEI. For this purpose, a validated questionnaire on the perception of VR use was administered to a sample of 1246 professors from the entire Latin American region, and their responses were statistically analyzed. As a result, it was found that professors from CABEI countries give better ratings to the usability dimensions of VR and report a lower number of disadvantages in its use than professors from countries outside CABEI. However, the increase in the digital competence of professors in CABEI countries is more than twice as high as the increase in the valuation of VR. It follows that there is still much room for the integration of VR in higher education in CABEI countries. Furthermore, in CABEI countries there is a more pronounced gap between professors from private and public universities with respect to the above-mentioned ratings than in non-CABEI countries. As a consequence, some implications and suggestions derived from the results are reported. Full article
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25 pages, 8965 KiB  
Article
Multi-Agent Dynamic Fog Service Placement Approach
by Nerijus Šatkauskas and Algimantas Venčkauskas
Future Internet 2024, 16(7), 248; https://doi.org/10.3390/fi16070248 - 13 Jul 2024
Viewed by 266
Abstract
Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are [...] Read more.
Fog computing as a paradigm was offered more than a decade ago to solve Cloud Computing issues. Long transmission distances, higher data flow, data loss, latency, and energy consumption lead to providing services at the edge of the network. But, fog devices are known for being mobile and heterogenous. Their resources can be limited, and their availability can be constantly changing. A service placement optimization is needed to meet the QoS requirements. We propose a service placement orchestration, which functions as a multi-agent system. Fog computing services are represented by agents that can both work independently and cooperate. Service placement is being completed by a two-stage optimization method. Our service placement orchestrator is distributed, services are discovered dynamically, resources can be monitored, and communication messages among fog nodes can be signed and encrypted as a solution to the weakness of multi-agent systems due to the lack of monitoring tools and security. Full article
(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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17 pages, 2937 KiB  
Article
Emotion Recognition from Videos Using Multimodal Large Language Models
by Lorenzo Vaiani, Luca Cagliero and Paolo Garza
Future Internet 2024, 16(7), 247; https://doi.org/10.3390/fi16070247 - 13 Jul 2024
Viewed by 284
Abstract
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data [...] Read more.
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence in Smart Societies)
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24 pages, 16679 KiB  
Article
Achieving Accountability and Data Integrity in Message Queuing Telemetry Transport Using Blockchain and Interplanetary File System
by Sara Lazzaro and Francesco Buccafurri
Future Internet 2024, 16(7), 246; https://doi.org/10.3390/fi16070246 - 13 Jul 2024
Viewed by 254
Abstract
Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates [...] Read more.
Ensuring accountability and integrity in MQTT communications is important for enabling several IoT applications. This paper presents a novel approach that combines blockchain technology and the interplanetary file system (IPFS) to achieve non-repudiation and data integrity in the MQTT protocol. Our solution operates in discrete temporal rounds, during which the broker constructs a Merkle hash tree (MHT) from the messages received. Then the broker publishes the root on the blockchain and the MHT itself on IPFS. This mechanism guarantees that both publishers and subscribers can verify the integrity of the message exchanged. Furthermore, the interactions with the blockchain made by the publishers and the broker ensure they cannot deny having sent the exchanged messages. We provide a detailed security analysis, showing that under standard assumptions, the proposed solution achieves both data integrity and accountability. Additionally, we provided an experimental campaign to study the scalability and the throughput of the system. Our results show that our solution scales well with the number of clients. Furthermore, from our results, it emerges that the throughput reduction depends on the integrity check operations. However, since the frequency of these checks can be freely chosen, we can set it so that the throughput reduction is negligible. Finally, we provided a detailed analysis of the costs of our solution showing that, overall, the execution costs are relatively low, especially given the critical security and accountability benefits it guarantees. Furthermore, our analysis shows that the higher the number of subscribers in the system, the lower the costs per client in our solution. Again, this confirms that our solution does not present any scalability issues. Full article
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18 pages, 715 KiB  
Article
Optimizing Drone Energy Use for Emergency Communications in Disasters via Deep Reinforcement Learning
by Wen Qiu, Xun Shao, Hiroshi Masui and William Liu
Future Internet 2024, 16(7), 245; https://doi.org/10.3390/fi16070245 - 11 Jul 2024
Viewed by 307
Abstract
For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration [...] Read more.
For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones—acting as agents—can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 1045% higher than that of the other three methods, and it can save up to 3% more energy. Full article
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36 pages, 2495 KiB  
Article
Blockchain Financial Statements: Innovating Financial Reporting, Accounting, and Liquidity Management
by Natalia Dashkevich, Steve Counsell and Giuseppe Destefanis
Future Internet 2024, 16(7), 244; https://doi.org/10.3390/fi16070244 - 9 Jul 2024
Viewed by 835
Abstract
The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting [...] Read more.
The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management. Full article
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21 pages, 4618 KiB  
Article
Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
by Imen Ahmed, Mariem Turki, Mouna Baklouti, Bouthaina Dammak and Amnah Alshahrani
Future Internet 2024, 16(7), 243; https://doi.org/10.3390/fi16070243 - 9 Jul 2024
Viewed by 387
Abstract
This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism [...] Read more.
This work introduces a blockchain-based secure medical prescription-management system seamlessly integrated with a dynamic Internet of Things (IoT) framework. Notably, this integration constitutes a pivotal challenge in the arena of resource-constrained IoT devices: energy consumption. The choice of a suitable blockchain consensus mechanism emerges as the linchpin in surmounting this hurdle. Thus, this paper conducts a comprehensive comparison of energy consumption between two distinct consensus mechanisms: Proof of Work (PoW) and Quorum-based Byzantine fault tolerance (QBFT). Furthermore, an assessment of the most energy-efficient algorithm is performed across multiple networks and various parameters. This approach ensures the acquisition of reliable and statistically significant data, enabling meaningful conclusions to be drawn about the system’s performance in real-world scenarios. The experimental results show that, compared to the PoW, the QBFT consensus mechanism reduced the energy consumption by an average of 5%. This finding underscores the significant advantage of QBFT in addressing the energy consumption challenges posed by resource-constrained IoT devices. In addition to its inherent benefits of privacy and block time efficiency, the Quorum blockchain emerges as a more sustainable choice for IoT applications due to its lower power consumption. Full article
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17 pages, 1606 KiB  
Article
Evaluating Convolutional Neural Networks and Vision Transformers for Baby Cry Sound Analysis
by Samir A. Younis, Dalia Sobhy and Noha S. Tawfik
Future Internet 2024, 16(7), 242; https://doi.org/10.3390/fi16070242 - 7 Jul 2024
Viewed by 370
Abstract
Crying is a newborn’s main way of communicating. Despite their apparent similarity, newborn cries are physically generated and have distinct characteristics. Experienced medical professionals, nurses, and parents are able to recognize these variations based on their prior interactions. Nonetheless, interpreting a baby’s cries [...] Read more.
Crying is a newborn’s main way of communicating. Despite their apparent similarity, newborn cries are physically generated and have distinct characteristics. Experienced medical professionals, nurses, and parents are able to recognize these variations based on their prior interactions. Nonetheless, interpreting a baby’s cries can be challenging for carers, first-time parents, and inexperienced paediatricians. This paper uses advanced deep learning techniques to propose a novel approach for baby cry classification. This study aims to accurately classify different cry types associated with everyday infant needs, including hunger, discomfort, pain, tiredness, and the need for burping. The proposed model achieves an accuracy of 98.33%, surpassing the performance of existing studies in the field. IoT-enabled sensors are utilized to capture cry signals in real time, ensuring continuous and reliable monitoring of the infant’s acoustic environment. This integration of IoT technology with deep learning enhances the system’s responsiveness and accuracy. Our study highlights the significance of accurate cry classification in understanding and meeting the needs of infants and its potential impact on improving infant care practices. The methodology, including the dataset, preprocessing techniques, and architecture of the deep learning model, is described. The results demonstrate the performance of the proposed model, and the discussion analyzes the factors contributing to its high accuracy. Full article
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15 pages, 2271 KiB  
Article
Explainable Artificial Intelligence Methods to Enhance Transparency and Trust in Digital Deliberation Settings
by Ilias Siachos and Nikos Karacapilidis
Future Internet 2024, 16(7), 241; https://doi.org/10.3390/fi16070241 - 6 Jul 2024
Viewed by 562
Abstract
Digital deliberation has been steadily growing in recent years, enabling citizens from different geographical locations and diverse opinions and expertise to participate in policy-making processes. Software platforms aiming to support digital deliberation usually suffer from information overload, due to the large amount of [...] Read more.
Digital deliberation has been steadily growing in recent years, enabling citizens from different geographical locations and diverse opinions and expertise to participate in policy-making processes. Software platforms aiming to support digital deliberation usually suffer from information overload, due to the large amount of feedback that is often provided. While Machine Learning and Natural Language Processing techniques can alleviate this drawback, their complex structure discourages users from trusting their results. This paper proposes two Explainable Artificial Intelligence models to enhance transparency and trust in the modus operandi of the above techniques, which concern the processes of clustering and summarization of citizens’ feedback that has been uploaded on a digital deliberation platform. Full article
(This article belongs to the Special Issue ICT and AI in Intelligent E-systems)
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20 pages, 1567 KiB  
Article
Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities
by Darya Ostrikova, Elizaveta Golos, Vitalii Beschastnyi, Egor Machnev, Yuliya Gaidamaka and Konstantin Samouylov
Future Internet 2024, 16(7), 240; https://doi.org/10.3390/fi16070240 - 6 Jul 2024
Viewed by 977
Abstract
The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for [...] Read more.
The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems. Full article
(This article belongs to the Section Internet of Things)
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35 pages, 7792 KiB  
Article
TWIN-ADAPT: Continuous Learning for Digital Twin-Enabled Online Anomaly Classification in IoT-Driven Smart Labs
by Ragini Gupta, Beitong Tian, Yaohui Wang and Klara Nahrstedt
Future Internet 2024, 16(7), 239; https://doi.org/10.3390/fi16070239 - 4 Jul 2024
Viewed by 439
Abstract
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead [...] Read more.
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings. Full article
(This article belongs to the Special Issue Digital Twins in Intelligent Manufacturing)
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16 pages, 4157 KiB  
Article
Enhancing Autonomous Driving Navigation Using Soft Actor-Critic
by Badr Ben Elallid, Nabil Benamar, Miloud Bagaa and Yassine Hadjadj-Aoul
Future Internet 2024, 16(7), 238; https://doi.org/10.3390/fi16070238 - 4 Jul 2024
Viewed by 341
Abstract
Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on [...] Read more.
Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on Deep Reinforcement Learning (DRL) show potential for tackling urban driving situations, they suffer from slow convergence, especially in complex scenarios with high mobility. In this paper, we present a new approach based on the Soft Actor-Critic (SAC) algorithm to control the autonomous vehicle to enter roundabouts smoothly and safely and ensure it reaches its destination without delay. For this, we introduce a destination vector concatenated with extracted features using Convolutional Neural Networks (CNN). To evaluate the performance of our model, we conducted extensive experiments in the CARLA simulator and compared it with the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) models. Qualitative results reveal that our model converges rapidly and achieves a high success rate in scenarios with high traffic compared to the DQN and PPO models. Full article
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16 pages, 2459 KiB  
Article
Software-Bus-Toolchain (SBT): Introducing a Versatile Method for Quickly Implementing (I)IoT-Scenarios
by Simon D. Duque Anton
Future Internet 2024, 16(7), 237; https://doi.org/10.3390/fi16070237 - 3 Jul 2024
Viewed by 417
Abstract
The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is [...] Read more.
The Internet of Things (IoT) has become ubiquitous. IoT devices are applied in a multitude of applications, e.g., in smart home scenarios, building automation, smart energy and smart cities, healthcare, and industrial environments. Fast and efficient implementation and roll-out of IoT devices is a critical factor for successs and acceptance of IoT devices. At the same time, the variety of hardware platforms that can be used for IoT applications, as well as the number of IoT orchestration platforms is increasing. Finding the right combination of tooling and hardware is not trivial, but essential for building applications that provide value. In this work, a Software-Bus-Toolchain (SBT) is introduced that encapsulates firmware design, data point definition, and communication protocol usage. Furthermore, an IoT control platform is provided to control and evaluate the IoT modules. Thus, using the SBT, solely the business logic has to be designed, while the hardware-design is automated to a high degree. Usage of the Zephyr framework allows the interchange of hardware modules, while interfaces provide easy adaption of data points and communication capabilities. The implementation of interfaces to the IoT-platform as well as to the communication layer provides a universal usage of logic and data elements. The SBT is evaluated in two application scenarios, where its flexible nature is shown. Full article
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27 pages, 3239 KiB  
Systematic Review
Does Anyone Care about the Opinion of People on Participating in a “Social” Metaverse? A Review and a Draft Proposal for a Surveying Tool
by Stefano Mottura
Future Internet 2024, 16(7), 236; https://doi.org/10.3390/fi16070236 - 2 Jul 2024
Viewed by 444
Abstract
In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is [...] Read more.
In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is well known that the company Meta (formerly Facebook) is striving to realize its interpretation of a “social” metaverse. As Meta is a big leader of social media, it is reasonable to guess that, in the future, users will participate in a new social platform, such as that which the company is building by depicting unlimited and engaging opportunities. Regardless of Meta, we ask what the opinion of people is about this possible future scenario. A literature search of previous works about this topic has been done; the few results we found were not properly on topic and showed heterogeneous content. A survey on interpretations of the metaverse of major information and communication technologies (ICT) companies that impact the consumer world was undertaken; the results show that Meta is the most prominent company with the mission of building a ”social” metaverse worldwide. Finally, a draft of a tool for assessing the predilection of people for a “social” metaverse, based on various facets of the future social platform, is proposed. Full article
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18 pages, 721 KiB  
Article
A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
by Enbo Sun, Jiaxuan Han, Yiquan Li and Cheng Huang
Future Internet 2024, 16(7), 235; https://doi.org/10.3390/fi16070235 - 2 Jul 2024
Viewed by 453
Abstract
In recent years, various Remote Code Execution vulnerabilities on the Internet have been exposed frequently; thus, more and more security researchers have begun to pay attention to the detection of Remote Code Execution attacks. In this paper, we focus on three kinds of [...] Read more.
In recent years, various Remote Code Execution vulnerabilities on the Internet have been exposed frequently; thus, more and more security researchers have begun to pay attention to the detection of Remote Code Execution attacks. In this paper, we focus on three kinds of common Remote Code Execution attacks: XML External Entity, Expression Language Injection, and Insecure Deserialization. We propose a packet content-oriented Remote Code Execution attack payload detection model. For the XML External Entity attack, we propose an algorithm to construct the use-definition chain of XML entities, and implement detection based on the integrity of the chain and the behavior of the chain’s tail node. For the Expression Language Injection and Insecure Deserialization attack, we extract 34 features to represent the string operation and the use of sensitive classes/methods in the code, and then train a machine learning model to implement detection. At the same time, we build a dataset to evaluate the effect of the proposed model. The evaluation results show that the model performs well in detecting XML External Entity attacks, achieving a precision of 0.85 and a recall of 0.94. Similarly, the model performs well in detecting Expression Language Injection and Insecure Deserialization attacks, achieving a precision of 0.99 and a recall of 0.88. Full article
(This article belongs to the Section Cybersecurity)
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23 pages, 714 KiB  
Review
Smart Irrigation Systems from Cyber–Physical Perspective: State of Art and Future Directions
by Mian Qian, Cheng Qian, Guobin Xu, Pu Tian and Wei Yu
Future Internet 2024, 16(7), 234; https://doi.org/10.3390/fi16070234 - 29 Jun 2024
Viewed by 452
Abstract
Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The [...] Read more.
Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people’s rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT’s applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field. Full article
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20 pages, 3983 KiB  
Article
Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
by Marcus Pieska, Andreas Kassler, Anna Brunstrom, Veselin Rakocevic and Markus Amend
Future Internet 2024, 16(7), 233; https://doi.org/10.3390/fi16070233 - 28 Jun 2024
Viewed by 405
Abstract
Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless [...] Read more.
Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm. Full article
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14 pages, 875 KiB  
Article
Digital Transformation in the Construction Sector: Blockchain, BIM and SSI for a More Sustainable and Transparent System
by Luisanna Cocco and Roberto Tonelli
Future Internet 2024, 16(7), 232; https://doi.org/10.3390/fi16070232 - 28 Jun 2024
Viewed by 398
Abstract
This article presents a model built for deep digitalization in the construction industry and for making building information modeling achieve a greater level of transparency, verifiability and effectiveness for the benefit of all stakeholders. Thanks to blockchain and the self-sovereign identity paradigm, the [...] Read more.
This article presents a model built for deep digitalization in the construction industry and for making building information modeling achieve a greater level of transparency, verifiability and effectiveness for the benefit of all stakeholders. Thanks to blockchain and the self-sovereign identity paradigm, the model guarantees data integrity and transaction reliability, enabling the generation of more efficient and productive businesses. The model includes a decentralized application for notarization of the information flow in building information modeling processes; the application is implemented and tested on a local blockchain. The proposed model represents a so-called digital twin and is, hence, a huge system that manages all the information flow associated with a building throughout its life cycle, returning to individuals the control of their own data. In this model, all stakeholders operate based on so-called decentralized identifiers and DID documents, which store on-chain the fingerprints of the information flow in a common data environment. Full article
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16 pages, 1941 KiB  
Systematic Review
Exploring the Architectural Composition of Cyber Ranges: A Systematic Review
by Dionysios Stamatopoulos, Menelaos Katsantonis, Panagiotis Fouliras and Ioannis Mavridis
Future Internet 2024, 16(7), 231; https://doi.org/10.3390/fi16070231 - 28 Jun 2024
Viewed by 545
Abstract
In light of the ever-increasing complexity of cyber–physical systems (CPSs) and information technology networking systems (ITNs), cyber ranges (CRs) have emerged as a promising solution by providing theoretical and practical cybersecurity knowledge for participants’ skill improvement toward a safe work environment. This research [...] Read more.
In light of the ever-increasing complexity of cyber–physical systems (CPSs) and information technology networking systems (ITNs), cyber ranges (CRs) have emerged as a promising solution by providing theoretical and practical cybersecurity knowledge for participants’ skill improvement toward a safe work environment. This research adds to the extant respective literature, exploring the architectural composition of CRs. It aims to improve the understanding of their design and how they are deployed, expanding skill levels in constructing better CRs. Our research follows the PRISMA methodology guidelines for transparency, which includes a search flow of articles based on specific criteria and quality valuation of selected articles. To extract valuable research datasets, we identify keyword co-occurrences that selected articles are concentrated on. In the context of literature evidence, we identify key attributes and trends, providing details of CRs concerning their architectural composition and underlying infrastructure, along with today’s challenges and future research directions. A total of 102 research articles’ qualitative analyses reveal a lack of adequate architecture examination when CR elements and services interoperate with other CR elements and services participating, leading to gaps that increase the administration burden. We posit that the results of this study can be leveraged as a baseline for future enhancements toward the development of CRs. Full article
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18 pages, 884 KiB  
Article
Trusted Composition of Internet of Medical Things over Imperfect Networks
by Ehsan Ahmad, Brian Larson and Abdulbasid Banga
Future Internet 2024, 16(7), 230; https://doi.org/10.3390/fi16070230 - 28 Jun 2024
Viewed by 535
Abstract
The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite [...] Read more.
The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated. Full article
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23 pages, 886 KiB  
Article
Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
by Lucas Henriques, Felipe Prata Lima and Cecilia Castro
Future Internet 2024, 16(7), 229; https://doi.org/10.3390/fi16070229 - 28 Jun 2024
Viewed by 1060
Abstract
Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with [...] Read more.
Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. Full article
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19 pages, 2171 KiB  
Article
Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
by Pietro Ruiu, Salvatore Saiu and Enrico Grosso
Future Internet 2024, 16(7), 228; https://doi.org/10.3390/fi16070228 - 28 Jun 2024
Viewed by 730
Abstract
Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious [...] Read more.
Today, more than ever before, technological progress is evolving rapidly, and in the absence of adequate regulatory frameworks, the big players in the digital market (the so-called Big Techs) are exploiting personal data (name, address, telephone numbers) and private data (political opinions, religious beliefs, financial information, or health status) in an uncontrolled manner. A crucial role in this scenario is played by the weakness of international regulatory frameworks due to the slow response time of legislators who are incapable, from a regulatory point of view, of keeping pace with technological evolution and responding to the new requirements coming from the social context, which is increasingly characterized by the pervasive presence of new technologies, such as smartphones and wearable devices. At the European level, the General Data Protection Regulation (GDPR) and the Regulation on Electronic Identification, Authentication and Trust Services (eIDAS) have marked a significant turning point in the regulatory landscape. However, the mechanisms proposed present clear security issues, particularly in light of emerging concepts such as digital identity. Moreover, despite the centrality of biometric issues within the European regulatory framework and the practical introduction of biometric data within electronic national identity (eID) cards, there are still no efforts to use biometric features for the identification and authentication of a person in a digital context. This paper clarifies and precisely defines the potential impact of biometric-based digital identity and hypothesizes its practical use for accessing network-based services and applications commonly used in daily life. Using the Italian eID card as a model, an authentication scheme leveraging biometric data is proposed, ensuring full compliance with GDPR and eIDAS regulations. The findings suggest that such a scheme can significantly improve the security and reliability of electronic identification systems, promoting broader adoption of eIDAS solutions. Full article
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