Future Internet

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

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

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

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

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

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

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

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

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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24 pages, 1273 KiB

Open AccessArticle

Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry

by Maria Crespo-Aguado, Raul Lozano, Fernando Hernandez-Gobertti, Nuria Molner and David Gomez-Barquero

Future Internet 2024, 16(11), 431; https://doi.org/10.3390/fi16110431 - 20 Nov 2024

Abstract

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT [...] Read more.

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load. Full article

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

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22 pages, 945 KiB

Open AccessReview

Resilience in the Internet of Medical Things: A Review and Case Study

by Vikas Tomer, Sachin Sharma and Mark Davis

Future Internet 2024, 16(11), 430; https://doi.org/10.3390/fi16110430 - 20 Nov 2024

Abstract

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare [...] Read more.

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies. Full article

(This article belongs to the Special Issue The Future Internet of Medical Things II)

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19 pages, 3800 KiB

Open AccessArticle

Fully Open-Source Meeting Minutes Generation Tool

by Amma Liesvarastranta Haz, Yohanes Yohanie Fridelin Panduman, Nobuo Funabiki, Evianita Dewi Fajrianti and Sritrusta Sukaridhoto

Future Internet 2024, 16(11), 429; https://doi.org/10.3390/fi16110429 - 20 Nov 2024

Abstract

With the increasing use of online meetings, there is a growing need for efficient tools that can automatically generate meeting minutes from recorded sessions. Current solutions often rely on proprietary systems, limiting adaptability and flexibility. This paper investigates whether various open-source models and [...] Read more.

With the increasing use of online meetings, there is a growing need for efficient tools that can automatically generate meeting minutes from recorded sessions. Current solutions often rely on proprietary systems, limiting adaptability and flexibility. This paper investigates whether various open-source models and methods such as audio-to-text conversion, summarization, keyword extraction, and optical character recognition (OCR) can be integrated to create a meeting minutes generation tool for recorded video presentations. For this purpose, a series of evaluations are conducted to identify suitable models. Then, the models are integrated into a system that is modular yet accurate. The utilization of an open-source approach ensures that the tool remains accessible and adaptable to the latest innovations, thereby ensuring continuous improvement over time. Furthermore, this approach also benefits organizations and individuals by providing a cost-effective and flexible alternative. This work contributes to creating a modular and easily extensible open-source framework that integrates several advanced technologies and future new models into a cohesive system. The system was evaluated on ten videos created under controlled conditions, which may not fully represent typical online presentation recordings. It showed strong performance in audio-to-text conversion with a low word-error rate. Summarization and keyword extraction were functional but showed room for improvement in terms of precision and relevance, as gathered from the users’ feedback. These results confirm the system’s effectiveness and efficiency in generating usable meeting minutes from recorded presentation videos, with room for improvement in future works. Full article

(This article belongs to the Special Issue Deep Learning and Natural Language Processing II)

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26 pages, 1394 KiB

Open AccessArticle

Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach

by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso and Javier Diaz-Fuentes

Future Internet 2024, 16(11), 428; https://doi.org/10.3390/fi16110428 - 20 Nov 2024

Abstract

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing [...] Read more.

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. However, the current literature yields a lack of methods for efficient fault prediction and fast reconfiguration. To achieve this goal, this paper builds on DEN2DE, an adaptable routing and reconfiguration solution potentially applicable to SGs, and investigates its potential extension with AI-based fault prediction using real-world datasets and randomly generated topologies based on the IEEE 123 Node Test Feeder. The study applies models based on Machine Learning (ML) and Deep Learning (DL) techniques, specifically evaluating Random Forest (RF) and Support Vector Machine (SVM) as ML methods, and Artificial Neural Network (ANN) as a DL method, evaluating each for accuracy, precision, and recall. Results indicate that the RF model with Recursive Feature Elimination (RFECV) achieves 94.28% precision and 81.05% recall, surpassing SVM (precision 89.32%, recall 6.95%) and ANN (precision 72.17%, recall 13.49%) in fault detection accuracy and reliability. Full article

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

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16 pages, 725 KiB

Open AccessArticle

Virtualization vs. Containerization, a Comparative Approach for Application Deployment in the Computing Continuum Focused on the Edge

by Hamish Sturley, Augustin Fournier, Andoni Salcedo-Navarro, Miguel Garcia-Pineda and Jaume Segura-Garcia

Future Internet 2024, 16(11), 427; https://doi.org/10.3390/fi16110427 - 19 Nov 2024

Abstract

With the emergence of containerization 10 years ago, we saw a compact, convenient and portable way of running apps directly concurrently with virtualization. The major difference is in the architecture. Containers share the same kernel as the guest and then do not virtualize [...] Read more.

With the emergence of containerization 10 years ago, we saw a compact, convenient and portable way of running apps directly concurrently with virtualization. The major difference is in the architecture. Containers share the same kernel as the guest and then do not virtualize low-layer components like the Central Processing Unit (CPU). On the one hand, they are lighter and more flexible than virtual machines (VMs). On the other hand, VMs can more precisely meet the low-layer needs and are completely autonomous systems. Nowadays, what is the best architecture to use to develop an application? In this paper, we will study the two main virtual methods of deploying this. We will compare both methods on several criteria: compatibility based on user experience and the ease of installation/deployment, scalability based on the automatic elasticity facing the workload and energy efficiency in terms of energy and computer resources. After the tests, we conclude that the containerization option is the most ecologically advantageous option in terms of energy consumption. Full article

(This article belongs to the Special Issue Scalable and Distributed Cloud Continuum Orchestration for Next-Generation IoT Applications: Latest Advances and Prospects)

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19 pages, 2662 KiB

Open AccessArticle

Identifying Persons of Interest in Digital Forensics Using NLP-Based AI

by Jonathan Adkins, Ali Al Bataineh and Majd Khalaf

Future Internet 2024, 16(11), 426; https://doi.org/10.3390/fi16110426 - 18 Nov 2024

Abstract

The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst’s expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This [...] Read more.

The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst’s expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This process of investigative analysis is often done manually. Artificial Intelligence (AI) can provide practical solutions to efficiently mine enormous amounts of data to find useful patterns that can be leveraged to investigate crimes. Natural Language Processing (NLP) is a subdomain of research under AI that deals with problems involving unstructured data, specifically language. The domain of NLP includes several tools to parse text, including topic modeling, pairwise correlation, word vector cosine distance measurement, and sentiment analysis. In this research, we propose a digital forensic investigative technique that uses an ensemble of NLP tools to identify a person of interest list based on a corpus of text. Our proposed method serves as a type of human feature reduction, where a total pool of suspects is filtered down to a short list of candidates who possess a higher correlation with the crime being investigated. Full article

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24 pages, 2020 KiB

Open AccessArticle

Enhanced Long-Range Network Performance of an Oil Pipeline Monitoring System Using a Hybrid Deep Extreme Learning Machine Model

by Abbas Kubba, Hafedh Trabelsi and Faouzi Derbel

Future Internet 2024, 16(11), 425; https://doi.org/10.3390/fi16110425 - 17 Nov 2024

Abstract

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and [...] Read more.

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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21 pages, 1716 KiB

Open AccessArticle

AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis

by Elena-Anca Paraschiv, Lidia Băjenaru, Cristian Petrache, Ovidiu Bica and Dragoș-Nicolae Nicolau

Future Internet 2024, 16(11), 424; https://doi.org/10.3390/fi16110424 - 16 Nov 2024

Abstract

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline [...] Read more.

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients. Full article

(This article belongs to the Special Issue eHealth and mHealth)

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26 pages, 4934 KiB

Open AccessArticle

Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure

by Naba Raj Khatiwoda, Babu Ram Dawadi and Sashidhar Ram Joshi

Future Internet 2024, 16(11), 423; https://doi.org/10.3390/fi16110423 - 14 Nov 2024

Abstract

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper [...] Read more.

With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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20 pages, 552 KiB

Open AccessArticle

SBNNR: Small-Size Bat-Optimized KNN Regression

by Rasool Seyghaly, Jordi Garcia, Xavi Masip-Bruin and Jovana Kuljanin

Future Internet 2024, 16(11), 422; https://doi.org/10.3390/fi16110422 - 14 Nov 2024

Abstract

Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. [...] Read more.

Small datasets are frequent in some scientific fields. Such datasets are usually created due to the difficulty or cost of producing laboratory and experimental data. On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. For this reason, in some cases, low-performance, overfitting models are developed for small-scale data. As a result, it appears necessary to develop methods for dealing with this type of data. In this research, we provide a new and innovative framework for regression problems with a small sample size. The base of our proposed method is the K-nearest neighbors (KNN) algorithm. For feature selection, instance selection, and hyperparameter tuning, we use the bat optimization algorithm (BA). Generative Adversarial Networks (GANs) are employed to generate synthetic data, effectively addressing the challenges associated with data sparsity. Concurrently, Deep Neural Networks (DNNs), as a deep learning approach, are utilized for feature extraction from both synthetic and real datasets. This hybrid framework integrates KNN, DNN, and GAN as foundational components and is optimized in multiple aspects (features, instances, and hyperparameters) using BA. The outcomes exhibit an enhancement of up to 5% in the coefficient of determination (

R^{2}

score) using the proposed method compared to the standard KNN method optimized through grid search. Full article

(This article belongs to the Special Issue Deep Learning Techniques Addressing Data Scarcity)

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13 pages, 420 KiB

Open AccessArticle

Towards a Decentralized Collaborative Framework for Scalable Edge AI

by Ahmed M. Abdelmoniem , Mona Jaber , Ali Anwar , Yuchao Zhang and Mingliang Gao

Future Internet 2024, 16(11), 421; https://doi.org/10.3390/fi16110421 - 14 Nov 2024

Abstract

Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led [...] Read more.

Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led to the growth of Edge Intelligence, which has become an integral part of the development cycle of most modern applications. However, scalability issues hinder their wide-scale adoption. We aim to focus on these challenges and propose a scalable decentralized edge intelligence framework. Therefore, we analyze and empirically evaluate the challenges of existing methods, and design an architecture that overcomes these challenges. The proposed approach is client-driven and model-centric, allowing models to be shared between entities in a scalable fashion. We conduct experiments over various benchmarks to show that the proposed approach presents an efficient alternative to the existing baseline method, and it can be a viable solution to scale edge intelligence. Full article

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

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