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12 pages, 2729 KiB  
Article
Educational Robotics for Industry 4.0 and 5.0 with Wlkata Mirobot in Laboratory Process Modelling
by Miriam Pekarcikova, Peter Trebuna, Marek Kliment, Jana Kronova and Matus Matiscsak
Machines 2025, 13(9), 753; https://doi.org/10.3390/machines13090753 - 22 Aug 2025
Abstract
This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. [...] Read more.
This study explores the integration of educational robotics into the development of digital competencies essential for Industry 4.0 and 5.0. These industrial paradigms are defined by automation, interconnected cyber-physical systems, value chain integration, and digitalisation. In this environment, digital skills become strategically vital. Didactic robotic platforms, such as the Wlkata Mirobot, offer students hands-on opportunities to develop these abilities in a practical and interdisciplinary context. When combined with technologies like digital twins, the Internet of Things, and simulation tools, educational robotics fosters both technical proficiency and adaptability to evolving industrial demands. The presented case study demonstrates the design, construction, and experimental setup of a functional laboratory mini-line using the Wlkata Mirobot. The focus is placed on layout design, robot programming, and simulation-based process optimization to reflect real industrial processes. This study also presents student feedback and performance indicators from repeated trials to illustrate the educational and operational potential of the solution. Full article
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9 pages, 1118 KiB  
Proceeding Paper
Internet-Enabled Collaborative Fixture Design
by Subramanian Vasanth, Earnest Hebron Jones, Hareendran Praveen and Francis Michael Thomas Rex
Eng. Proc. 2025, 93(1), 25; https://doi.org/10.3390/engproc2025093025 - 20 Aug 2025
Viewed by 147
Abstract
The design of fixtures is a complex and instinctive process. A proficient fixture design system customized for particular applications reduces manufacturing costs and lead times. Various computer-aided systems are available to assist in the many manufacturing stages in today’s industry. A fixture design [...] Read more.
The design of fixtures is a complex and instinctive process. A proficient fixture design system customized for particular applications reduces manufacturing costs and lead times. Various computer-aided systems are available to assist in the many manufacturing stages in today’s industry. A fixture design system should facilitate the seamless movement of information among several domains to enhance product design and production processes. The fixture design system should be easily transferable and compatible with many operating platforms. This study discusses creating an Internet-enabled interactive fixture design system that enables seamless communication among different disciplines in product development. Utilizing the Internet and Virtual Reality Modelling Language (VRML) allows for the exchange of information and expertise among computer-aided manufacturing systems. The CAD model of fixturing pieces is first turned into VRML coding. The VRML code for the model can be modified to vary the size and dimensions of the CAD model, facilitating alterations such as scaling fixture components, repositioning mounting points, and resizing clamping parts to align with specific design needs. The VRML model of the fixturing system was created with Java and built on an FTTP server architecture. It guarantees that the system performs consistently across different platforms. This work has also established a mechanism for comprehensive fixture design independent of a locating scheme. Establishing a library for storing previous fixture designs can prevent the need to recreate the current model. Full article
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16 pages, 1188 KiB  
Article
Can Urban Internet Development Attract Labor Force? Evidence from Chinese Cities
by Xiaoxia Zhai and Yongmin Luo
Sustainability 2025, 17(1), 260; https://doi.org/10.3390/su17010260 - 2 Jan 2025
Cited by 3 | Viewed by 1080
Abstract
Labor force mobility plays a crucial role in achieving balanced regional development in China. This study investigates whether urban internet development can effectively attract labor force inflow using data from the China Labor-force Dynamic Survey (CLDS) and constructing a comprehensive urban internet development [...] Read more.
Labor force mobility plays a crucial role in achieving balanced regional development in China. This study investigates whether urban internet development can effectively attract labor force inflow using data from the China Labor-force Dynamic Survey (CLDS) and constructing a comprehensive urban internet development index through factor analysis. Employing a conditional logit model and addressing potential endogeneity through instrumental variables, we find that (1) urban internet development significantly attracts labor force inflow, with a one-unit increase in the urban internet development index significantly raising the log odds of individual city choice by 0.2, and this effect remains robust across multiple specifications and estimation methods; (2) the attraction effect shows significant heterogeneity—it is stronger among highly educated, younger, foreign language proficient, and unmarried individuals, and more pronounced in regions with lower housing costs, lower income levels, and inland locations. These findings suggest that less-developed cities should prioritize internet infrastructure development to accumulate high-quality labor resources and achieve high-quality economic development, while also enhancing support for bottom-tier workers through public-benefit online platforms. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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21 pages, 803 KiB  
Article
One-Dimensional Deep Residual Network with Aggregated Transformations for Internet of Things (IoT)-Enabled Human Activity Recognition in an Uncontrolled Environment
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Technologies 2024, 12(12), 242; https://doi.org/10.3390/technologies12120242 - 24 Nov 2024
Cited by 2 | Viewed by 2229
Abstract
Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current [...] Read more.
Human activity recognition (HAR) in real-world settings has gained significance due to the growth of Internet of Things (IoT) devices such as smartphones and smartwatches. Nonetheless, limitations such as fluctuating environmental conditions and intricate behavioral patterns have impacted the accuracy of the current procedures. This research introduces an innovative methodology employing a modified deep residual network, called 1D-ResNeXt, for IoT-enabled HAR in uncontrolled environments. We developed a comprehensive network that utilizes feature fusion and a multi-kernel block approach. The residual connections and the split–transform–merge technique mitigate the accuracy degradation and reduce the parameter number. We assessed our suggested model on three available datasets, mHealth, MotionSense, and Wild-SHARD, utilizing accuracy metrics, cross-entropy loss, and F1 score. The findings indicated substantial enhancements in proficiency in recognition, attaining 99.97% on mHealth, 98.77% on MotionSense, and 97.59% on Wild-SHARD, surpassing contemporary methodologies. Significantly, our model attained these outcomes with considerably fewer parameters (24,130–26,118) than other models, several of which exceeded 700,000 parameters. The 1D-ResNeXt model demonstrated outstanding effectiveness under various ambient circumstances, tackling a significant obstacle in practical HAR applications. The findings indicate that our modified deep residual network presents a viable approach for improving the dependability and usability of IoT-based HAR systems in dynamic, uncontrolled situations while preserving the computational effectiveness essential for IoT devices. The results significantly impact multiple sectors, including healthcare surveillance, intelligent residences, and customized assistive devices. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications)
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10 pages, 1174 KiB  
Article
Building Sustainable Societies: The Role of Technology in Empowering Momtrepreneurs of Children with Special Needs
by Zehra Altinay, Emirali Evcimen, Gokmen Dagli and Ainur Kenebayeva
Societies 2024, 14(11), 223; https://doi.org/10.3390/soc14110223 - 31 Oct 2024
Viewed by 1138
Abstract
This study investigates the technology usage patterns among mothers of children with special needs, focusing on their proficiency, frequency, and purposes of computer and internet use. The aim of this study is to reveal the level of technology use of mothers with disabled [...] Read more.
This study investigates the technology usage patterns among mothers of children with special needs, focusing on their proficiency, frequency, and purposes of computer and internet use. The aim of this study is to reveal the level of technology use of mothers with disabled children and the difficulties they experience in this regard. In addition, this study is expected to reveal the level of knowledge that mothers have about both the use of technological devices and their applications and to guide projects and development programs to be carried out for mothers. Utilizing a qualitative research methodology, data were collected through semi-structured interviews with 16 mothers whose children attend Famagusta Special Education and Job Training School. The findings reveal that while a significant portion of the participants are beginners or lack confidence in using computers, 87.5 percent of the respondents utilize the internet regularly, primarily for accessing information related to their children’s disabilities and for entertainment purposes. Despite some mothers having received formal training in computer usage, the majority indicated a need for further education to enhance their technological skills. This study underscores the necessity of tailored programs to support these mothers in effectively leveraging technology for their personal and familial needs, contributing to broader discussions on gender equality and empowerment within the framework of the Sustainable Development Goals. Full article
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20 pages, 3499 KiB  
Article
Feasibility of Employing mHealth in Delivering Preventive Nutrition Interventions Targeting the First 1000 Days of Life: Experiences from a Community-Based Cluster Randomised Trial in Rural Bangladesh
by Tarana E Ferdous, Md. Jahiduj Jaman, Abu Bakkar Siddique, Nadia Sultana, Takrib Hossain, Shams El Arifeen and Sk Masum Billah
Nutrients 2024, 16(20), 3429; https://doi.org/10.3390/nu16203429 - 10 Oct 2024
Cited by 1 | Viewed by 2241
Abstract
Background/Objectives: An Android platform-based customised app and web-linked system was developed to aid in implementing selected nutrition interventions by community health workers (CHWs) in a community-based cluster randomised trial (c-RCT) in rural Bangladesh. Methods: Here, we describe the architecture of the intervention delivery [...] Read more.
Background/Objectives: An Android platform-based customised app and web-linked system was developed to aid in implementing selected nutrition interventions by community health workers (CHWs) in a community-based cluster randomised trial (c-RCT) in rural Bangladesh. Methods: Here, we describe the architecture of the intervention delivery system, and explore feasibility of employing mHealth as CHWs’ job aid, employing a mixed-method study design covering 17 visits per mother-child dyad. We analysed CHWs’ real-time visit information from monitoring and documentation data, and CHWs’ qualitative interviews to explore the advantages and barriers of using mHealth as a job aid. Results: Intervention coverage was high across the arms (>90%), except around the narrow perinatal period (51%) due to mothers’ cultural practice of moving to their parents’ homes and/or hospitals for childbirth. CHWs mentioned technical and functional advantages of the job aid including device portability, easy navigability of content, pictorial demonstration that improved communication, easy information entry, and automated daily scheduling of tasks. Technical challenges included charging tablets, especially in power cut-prone areas, deteriorated battery capacity over continuous device usage, unstable internet network in unsupportive weather conditions, and device safety. Nevertheless, onsite supervision and monitoring by expert supervisors remained important to ensure intervention quality. Conclusions: With appropriate training and supervision, CHWs utilised the tablet-based app proficiently, attaining high coverage of long-term visits. mHealth was thus useful for designing, planning, scheduling, and delivering nutrition interventions through CHWs, and for monitoring and supervision by supervisors. Therefore, this application and job aid can be adopted or replicated into the currently developing national health systems platform for improving coverage and quality of preventive maternal and child nutrition services. In addition, continuous supportive supervision by skilled supervisors must be accompanied to ensure CHWs’ task quality. Finally, future studies should rigorously assess undesirable health and environmental effects of mHealth before and after mainstreaming, effective interventions addressing device-induced health hazards should be designed and scaled up, and effective e-waste management must be ensured. Full article
(This article belongs to the Section Nutrition and Public Health)
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12 pages, 237 KiB  
Article
Behavioral and Psychosocial Dynamics of Engagement: The Digital Divide in Artificial Intelligence [AI]-Driven Sports Podcasts
by Yair Galily, Tal Laor and Tal Samuel Azran
Behav. Sci. 2024, 14(10), 911; https://doi.org/10.3390/bs14100911 - 8 Oct 2024
Viewed by 1705
Abstract
The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on [...] Read more.
The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as the first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. The existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources. Full article
(This article belongs to the Special Issue Behavioral and Psychosocial Dynamics of Sports and Exercise)
33 pages, 6102 KiB  
Review
Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review
by Sudhan Kasiviswanathan, Sakthivel Gnanasekaran, Mohanraj Thangamuthu and Jegadeeshwaran Rakkiyannan
J. Sens. Actuator Netw. 2024, 13(5), 53; https://doi.org/10.3390/jsan13050053 - 4 Sep 2024
Cited by 16 | Viewed by 4364
Abstract
Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their [...] Read more.
Tool condition monitoring (TCM) systems have evolved into an essential requirement for contemporary manufacturing sectors of Industry 4.0. These systems employ sensors and diverse monitoring techniques to swiftly identify and diagnose tool wear, defects, and malfunctions of computer numerical control (CNC) machines. Their pivotal role lies in augmenting tool lifespan, minimizing machine downtime, and elevating productivity, thereby contributing to industry growth. However, the efficacy of CNC machine TCM hinges upon multiple factors, encompassing system type, data precision, reliability, and adeptness in data analysis. Globally, extensive research is underway to enhance real-time TCM system efficiency. This review focuses on the significance and attributes of proficient real-time TCM systems of CNC turning centers. It underscores TCM’s paramount role in manufacturing and outlines the challenges linked to TCM data processing and analysis. Moreover, the review elucidates various TCM system variants, including cutting force, acoustic emission, vibration, and temperature monitoring systems. Furthermore, the integration of industrial Internet of things (IIoT) and machine learning (ML) into CNC machine TCM systems are also explored. This article concludes by underscoring the ongoing necessity for research and development in TCM technology to empower modern intelligent industries to operate at peak efficiency. Full article
(This article belongs to the Special Issue AI and IoT Convergence for Sustainable Smart Manufacturing)
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17 pages, 6547 KiB  
Article
Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities
by Zhao Zhao, Weike Song, Huajie Wang, Yifeng Sun and Haifeng Luo
Sensors 2024, 24(14), 4433; https://doi.org/10.3390/s24144433 - 9 Jul 2024
Cited by 3 | Viewed by 2122
Abstract
The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in [...] Read more.
The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors’ expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults. Full article
(This article belongs to the Section Internet of Things)
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19 pages, 884 KiB  
Article
A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
by Wa’ad H. Aljuaid and Sultan S. Alshamrani
Appl. Sci. 2024, 14(13), 5381; https://doi.org/10.3390/app14135381 - 21 Jun 2024
Cited by 18 | Viewed by 6335
Abstract
Cloud computing services have become indispensable to people’s lives. Many of their activities are performed through cloud services, from small companies to large enterprises and individuals to government agencies. It has enabled clients to use companies’ services on demand at the lowest cost [...] Read more.
Cloud computing services have become indispensable to people’s lives. Many of their activities are performed through cloud services, from small companies to large enterprises and individuals to government agencies. It has enabled clients to use companies’ services on demand at the lowest cost anywhere, anytime, over the Internet. Despite these advantages, cloud networks are vulnerable to many types of attacks. However, as the adoption of cloud services accelerates, the risks associated with these services have also increased. For this reason, solutions have been implemented to improve cloud security, such as monitoring networks, the backbone of the cloud infrastructure, and detecting and classifying cyberattacks. Therefore, an intrusion detection system (IDS) is one of the essential defenses for detecting attacks in the cloud computing network. Current IDSs encounter some challenges in handling and simultaneously analyzing the large scale of traffic found in the cloud environment, and this affects the accuracy of cyberattack detection. Therefore, this research proposes a deep learning-based model by leveraging advanced convolutional neural networks (CNNs)-based model architecture to detect cyberattacks in the cloud environment efficiently. The proposed CNN-based model for intrusion detection consists of multiple significant stages: dataset collection, preprocessing, the SMOTE balance data strategy, feature selection, model training, testing, and performance evaluation. Experiments have demonstrated that the proposed model is highly effective in protecting cloud networks against various potential attacks. With over 98.67% accuracy, precision, and recall, the model has proven its ability to detect and classify network intrusions. Detailed analyses show that the model is proficient in securing cloud security measures and mitigating the risks associated with evolving security threats. Full article
(This article belongs to the Special Issue Network Intrusion Detection and Attack Identification)
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20 pages, 2640 KiB  
Article
Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism
by Wael M. S. Yafooz
Information 2024, 15(6), 316; https://doi.org/10.3390/info15060316 - 28 May 2024
Cited by 10 | Viewed by 3566
Abstract
Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of [...] Read more.
Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation. Full article
(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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28 pages, 1475 KiB  
Systematic Review
A Comparison of Parenting Strategies in a Digital Environment: A Systematic Literature Review
by Leonarda Banić and Tihomir Orehovački
Multimodal Technol. Interact. 2024, 8(4), 32; https://doi.org/10.3390/mti8040032 - 12 Apr 2024
Cited by 18 | Viewed by 14147
Abstract
In the modern digital landscape, parental involvement in shaping children’s internet usage has gained unprecedented importance. This research delves into the evolving trends of parental mediation concerning children’s internet activities. As the digital realm increasingly influences young lives, the role of parents in [...] Read more.
In the modern digital landscape, parental involvement in shaping children’s internet usage has gained unprecedented importance. This research delves into the evolving trends of parental mediation concerning children’s internet activities. As the digital realm increasingly influences young lives, the role of parents in guiding and safeguarding their children’s online experiences becomes crucial. The study addresses key research questions to explore the strategies parents adopt, the content they restrict, the rules they establish, the potential exposure to inappropriate content, and the impact of parents’ computer literacy on their children’s internet safety. Additionally, the research includes a thematic question that broadens the analysis by incorporating insights from studies not directly answering the primary questions but contributing valuable context and understanding to the digital parenting arena. Building on this, the findings from a systematic literature review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, highlight a shift towards more proactive parental involvement. Incorporating 49 studies from 11 databases, these findings reveal the current trends and methodologies in parental mediation. Active mediation strategies, which involve positive interactions and discussions about online content, are gaining recognition alongside the prevalent restrictive mediation approaches. Parents are proactively forbidding specific internet content, emphasizing safety and privacy concerns. Moreover, the emergence of parents’ computer literacy as a significant factor influencing their children’s online safety underlines the importance of digital proficiency. By shedding light on the contemporary landscape of parental mediation, this study contributes to a deeper understanding of how parents navigate their children’s internet experiences and the challenges they face in ensuring responsible and secure online engagement. The implications of these findings offer valuable insights for both practitioners and researchers, emphasizing the need for active parental involvement and the importance of enhancing parents’ digital proficiency. Despite limitations due to the language and methodological heterogeneity among the included studies, this research paves the way for future investigations into digital parenting practices. Full article
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21 pages, 3761 KiB  
Article
Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
by Muhammad Nafees Ulfat Khan, Weiping Cao, Zhiling Tang, Ata Ullah and Wanghua Pan
Future Internet 2024, 16(2), 66; https://doi.org/10.3390/fi16020066 - 19 Feb 2024
Cited by 3 | Viewed by 2375
Abstract
The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed [...] Read more.
The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost. Full article
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20 pages, 19399 KiB  
Article
Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
by Haohan Shi, Xiyu Shi and Safak Dogan
Future Internet 2024, 16(2), 63; https://doi.org/10.3390/fi16020063 - 17 Feb 2024
Cited by 3 | Viewed by 2386
Abstract
Audio inpainting plays an important role in addressing incomplete, damaged, or missing audio signals, contributing to improved quality of service and overall user experience in multimedia communications over the Internet and mobile networks. This paper presents an innovative solution for speech inpainting using [...] Read more.
Audio inpainting plays an important role in addressing incomplete, damaged, or missing audio signals, contributing to improved quality of service and overall user experience in multimedia communications over the Internet and mobile networks. This paper presents an innovative solution for speech inpainting using Long Short-Term Memory (LSTM) networks, i.e., a restoring task where the missing parts of speech signals are recovered from the previous information in the time domain. The lost or corrupted speech signals are also referred to as gaps. We regard the speech inpainting task as a time-series prediction problem in this research work. To address this problem, we designed multi-layer LSTM networks and trained them on different speech datasets. Our study aims to investigate the inpainting performance of the proposed models on different datasets and with varying LSTM layers and explore the effect of multi-layer LSTM networks on the prediction of speech samples in terms of perceived audio quality. The inpainted speech quality is evaluated through the Mean Opinion Score (MOS) and a frequency analysis of the spectrogram. Our proposed multi-layer LSTM models are able to restore up to 1 s of gaps with high perceptual audio quality using the features captured from the time domain only. Specifically, for gap lengths under 500 ms, the MOS can reach up to 3~4, and for gap lengths ranging between 500 ms and 1 s, the MOS can reach up to 2~3. In the time domain, the proposed models can proficiently restore the envelope and trend of lost speech signals. In the frequency domain, the proposed models can restore spectrogram blocks with higher similarity to the original signals at frequencies less than 2.0 kHz and comparatively lower similarity at frequencies in the range of 2.0 kHz~8.0 kHz. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing II)
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31 pages, 1418 KiB  
Article
A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness
by Mahmoud Elkhodr, Samiya Khan and Ergun Gide
Future Internet 2024, 16(1), 22; https://doi.org/10.3390/fi16010022 - 7 Jan 2024
Cited by 5 | Viewed by 4073
Abstract
In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes [...] Read more.
In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency. Full article
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