Future Internet

20 pages, 4716 KiB

Open AccessArticle

TasksZE: A Task-Based and Challenge-Based Math Serious Game Using Facial Emotion Recognition

by Humberto Marín-Vega, Giner Alor-Hernández, Maritza Bustos-López, Jonathan Hernández-Capistran, Norma Leticia Hernández-Chaparro and Sergio David Ixmatlahua-Diaz

Future Internet 2024, 16(12), 440; https://doi.org/10.3390/fi16120440 - 25 Nov 2024

Viewed by 303

Abstract

Serious games play a significant role in the teaching and learning process by focusing on educational objectives rather than purely on entertainment. By addressing specific educational needs, these games provide targeted learning experiences. The integration of emotion recognition technology into serious games can [...] Read more.

Serious games play a significant role in the teaching and learning process by focusing on educational objectives rather than purely on entertainment. By addressing specific educational needs, these games provide targeted learning experiences. The integration of emotion recognition technology into serious games can further enhance teaching and learning by identifying areas where students may need additional support, The integration of emotion recognition into a serious game facilitates the learning of mathematics by allowing the identification of emotional impact on learning and the creation of a tailored learning experience for the student. This study proposes a challenge-based and task-based math serious game that integrates facial emotion recognition named TasksZE. TasksZE introduces a novel approach by adjusting gameplay based on detected emotions, which includes real-time emotion analysis and the cross-validation of emotions. We conducted a usability evaluation of the game using the System Usability Scale (SUS) as a reference, and the results indicate that the students feel that TasksZE is easy to use, the functions are well integrated, and most people can learn to use it very quickly. The students answered that they would use this system frequently since they felt motivated by game attributes, rewards, and level progression. These elements contributed to a more engaging and effective learning experience. Full article

(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction II)

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17 pages, 2554 KiB

Open AccessArticle

QRLIT: Quantum Reinforcement Learning for Database Index Tuning

by Diogo Barbosa, Le Gruenwald, Laurent D’Orazio and Jorge Bernardino

Future Internet 2024, 16(12), 439; https://doi.org/10.3390/fi16120439 - 22 Nov 2024

Viewed by 387

Abstract

Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index [...] Read more.

Selecting indexes capable of reducing the cost of query processing in database systems is a challenging task, especially in large-scale applications. Quantum computing has been investigated with promising results in areas related to database management, such as query optimization, transaction scheduling, and index tuning. Promising results have also been seen when reinforcement learning is applied for database tuning in classical computing. However, there is no existing research with implementation details and experiment results for index tuning that takes advantage of both quantum computing and reinforcement learning. This paper proposes a new algorithm called QRLIT that uses the power of quantum computing and reinforcement learning for database index tuning. Experiments using the database TPC-H benchmark show that QRLIT exhibits superior performance and a faster convergence compared to its classical counterpart. Full article

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26 pages, 1559 KiB

Open AccessArticle

Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases

by Markus Hornsteiner, Michael Kreussel, Christoph Steindl, Fabian Ebner, Philip Empl and Stefan Schönig

Future Internet 2024, 16(12), 438; https://doi.org/10.3390/fi16120438 - 22 Nov 2024

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Abstract

Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap [...] Read more.

Based on their ability to efficiently and intuitively represent real-world relationships and structures, graph databases are gaining increasing popularity. In this context, this paper proposes an innovative integration of a Large Language Model into NoSQL databases and Knowledge Graphs to bridge the gap in field of Text-to-Cypher queries, focusing on Neo4j. Using the Design Science Research Methodology, we developed a Natural Language Interface which can receive user queries in real time, convert them into Cypher Query Language (CQL), and perform targeted queries, allowing users to choose from different graph databases. In addition, the user interaction is expanded by an additional chat function based on the chat history, as well as an error correction module, which elevates the precision of the generated Cypher statements. Our findings show that the chatbot is able to accurately and efficiently solve the tasks of database selection, chat history referencing, and CQL query generation. The developed system therefore makes an important contribution to enhanced interaction with graph databases, and provides a basis for the integration of further and multiple database technologies and LLMs, due to its modular pipeline architecture. Full article

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

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23 pages, 740 KiB

Open AccessArticle

Towards a Comprehensive Metaverse Forensic Framework Based on Technology Task Fit Model

by Amna AlMutawa, Richard Adeyemi Ikuesan and Huwida Said

Future Internet 2024, 16(12), 437; https://doi.org/10.3390/fi16120437 - 22 Nov 2024

Viewed by 270

Abstract

This article introduces a robust metaverse forensic framework designed to facilitate the investigation of cybercrime within the dynamic and complex digital metaverse. In response to the growing potential for nefarious activities in this technological landscape, the framework is meticulously developed and aligned with [...] Read more.

This article introduces a robust metaverse forensic framework designed to facilitate the investigation of cybercrime within the dynamic and complex digital metaverse. In response to the growing potential for nefarious activities in this technological landscape, the framework is meticulously developed and aligned with international standardization, ensuring a comprehensive, reliable, and flexible approach to forensic investigations. Comprising seven distinct phases, including a crucial incident pre-response phase, the framework offers a detailed step-by-step guide that can be readily applied to any virtualized platform. Unlike previous studies that have primarily adapted the existing digital forensic methodologies, this proposed framework fills a critical research gap by providing a proactive and granular investigative process. The approach goes beyond mere adaptation, ensuring a comprehensive strategy that addresses the unique challenges posed by the metaverse environment. The seven phases cover a spectrum of forensic investigation, offering a thorough interpretation with careful consideration of real-life metaverse forensic scenarios. To validate its effectiveness, the proposed framework undergoes a rigorous evaluation against the appropriate ISO/IEC standards. Additionally, metaverse expert reviews, based on the task–technology fit theory, contribute valuable insights. The overall assessment confirms the framework’s adherence to forensic standards, making it a reliable guide for investigators navigating the complexities of cybercrime in the metaverse. This comprehensive metaverse forensic framework provides investigators with a detailed and adaptable tool to address a wide range of cybercrime incidents within the evolving virtualized landscape. Furthermore, its stepwise guidance ensures a thorough and reliable investigation process, offering significant contributions to proactive security measures in the face of emerging challenges in the metaverse. Full article

(This article belongs to the Topic Future Internet Architecture: Difficulties and Opportunities)

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24 pages, 21174 KiB

Open AccessArticle

An Ensemble Deep CNN Approach for Power Quality Disturbance Classification: A Technological Route Towards Smart Cities Using Image-Based Transfer

by Mirza Ateeq Ahmed Baig, Naeem Iqbal Ratyal, Adil Amin, Umar Jamil, Sheroze Liaquat, Haris M. Khalid and Muhammad Fahad Zia

Future Internet 2024, 16(12), 436; https://doi.org/10.3390/fi16120436 - 22 Nov 2024

Viewed by 594

Abstract

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid [...] Read more.

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. Full article

(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)

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23 pages, 9394 KiB

Open AccessArticle

Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications

by Vesna Antoska Knights, Olivera Petrovska and Jasenka Gajdoš Kljusurić

Future Internet 2024, 16(12), 435; https://doi.org/10.3390/fi16120435 - 21 Nov 2024

Viewed by 521

Abstract

This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and [...] Read more.

This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear dynamic environments and the importance of machine learning. The proposed hybrid control system is designed for a 20 degrees of freedom (DOFs) robotic platform, combining traditional nonlinear control methods with machine learning models to predict and optimize robotic movements. The machine learning models, including neural networks, are trained using historical data and real-time sensor inputs to dynamically adjust the control parameters. Through simulations, the system demonstrated improved accuracy in trajectory tracking and adaptability, particularly in nonlinear and time-varying environments. The results show that combining traditional control strategies with machine learning significantly enhances the robot’s performance in real-world scenarios. This work offers a foundation for future research into intelligent control systems, with broader implications for industrial applications where precision and adaptability are critical. Full article

(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)

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17 pages, 5086 KiB

Open AccessArticle

A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks

by Irene Vilà, Jordi Pérez-Romero and Oriol Sallent

Future Internet 2024, 16(12), 434; https://doi.org/10.3390/fi16120434 - 21 Nov 2024

Viewed by 271

Abstract

The use of Reinforcement Learning (RL) techniques has been widely addressed in the literature to cope with capacity sharing in 5G Radio Access Network (RAN) slicing. These algorithms consider a training process to learn an optimal capacity sharing decision-making policy, which is later [...] Read more.

The use of Reinforcement Learning (RL) techniques has been widely addressed in the literature to cope with capacity sharing in 5G Radio Access Network (RAN) slicing. These algorithms consider a training process to learn an optimal capacity sharing decision-making policy, which is later applied to the RAN environment during the inference stage. When relevant changes occur in the RAN, such as the deployment of new cells in the network, RL-based capacity sharing solutions require a re-training process to update the optimal decision-making policy, which may require long training times. To accelerate this process, this paper proposes a novel Transfer Learning (TL) approach for RL-based capacity sharing solutions in multi-cell scenarios that is implementable following the Open-RAN (O-RAN) architecture and exploits the availability of computing resources at the edge for conducting the training/inference processes. The proposed approach allows transferring the weights of the previously learned policy to learn the new policy to be used after the addition of new cells. The performance assessment of the TL solution highlights its capability to reduce the training process duration of the policies when adding new cells. Considering that the roll-out of 5G networks will continue for several years, TL can contribute to enhancing the practicality and feasibility of applying RL-based solutions for capacity sharing. Full article

(This article belongs to the Special Issue Convergence of Edge Computing and Next Generation Networking)

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43 pages, 4383 KiB

Open AccessReview

Integrating UAVs and RISs in Future Wireless Networks: A Review and Tutorial on IoTs and Vehicular Communications

by Mohsen Eskandari and Andrey V. Savkin

Future Internet 2024, 16(12), 433; https://doi.org/10.3390/fi16120433 - 21 Nov 2024

Viewed by 533

Abstract

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to [...] Read more.

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance Internet of Vehicles (IoV) systems within beyond-fifth-generation (B5G) and sixth-generation (6G) networks. We explore the combination of quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks and their applications in urban environments. This review covers essential areas such as channel modeling and position-aware beamforming in dynamic networks, including UAVs and IoVs. Moreover, we investigate UAV navigation and control, emphasizing the development of obstacle-free trajectory designs in dense urban areas while meeting kinodynamic and motion constraints. The emerging potential of RIS-equipped UAVs (RISeUAVs) is highlighted, along with their role in supporting IoVs and in mobile edge computing. Optimization techniques, including convex programming methods and machine learning, are explored to tackle complex challenges, with an emphasis on studying computational complexity and feasibility for real-time operations. Additionally, this review highlights the integrated localization and communication strategies to enhance UAV and autonomous ground vehicle operations. This tutorial-style overview offers insights into the technical challenges and innovative solutions of the next-generation wireless networks in smart cities, with a focus on vehicular communications. Finally, future research directions are outlined. Full article

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

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16 pages, 3101 KiB

Open AccessArticle

Using Multimodal Foundation Models for Detecting Fake Images on the Internet with Explanations

by Vishnu S. Pendyala and Ashwin Chintalapati

Future Internet 2024, 16(12), 432; https://doi.org/10.3390/fi16120432 - 21 Nov 2024

Viewed by 439

Abstract

Generative AI and multimodal foundation models have fueled a proliferation of fake content on the Internet. This paper investigates if foundation models help detect and thereby contain the spread of fake images. The task of detecting fake images is a formidable challenge owing [...] Read more.

Generative AI and multimodal foundation models have fueled a proliferation of fake content on the Internet. This paper investigates if foundation models help detect and thereby contain the spread of fake images. The task of detecting fake images is a formidable challenge owing to its visual nature and intricate analysis. This paper details experiments using four multimodal foundation models, Llava, CLIP, Moondream2, and Gemini 1.5 Flash, to detect fake images. Explainable AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and removal-based explanations are used to gain insights into the detection process. The dataset used comprised real images and fake images generated by a generative artificial intelligence tool called MidJourney. Results show that the models can achieve up to a 69% accuracy rate in detecting fake images in an intuitively explainable way, as confirmed by multiple techniques and metrics. Full article

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Future Internet, Volume 16, Issue 12 (December 2024) – 9 articles

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