Future Internet

27 pages, 5145 KiB

Open AccessArticle

Validation of Value-Driven Token Economy: Focus on Blockchain Content Platform

by Young Sook Kim, Seng-Phil Hong and Marko Majer

Future Internet 2024, 16(5), 178; https://doi.org/10.3390/fi16050178 - 20 May 2024

Viewed by 1224

This study explores the architectural framework of a value-driven token economy on a blockchain content platform and critically evaluates the relationship between blockchain’s decentralization and sustainable economic practices. The existing literature often glorifies the rapid market expansion of cryptocurrencies but overlooks how underlying [...] Read more.

This study explores the architectural framework of a value-driven token economy on a blockchain content platform and critically evaluates the relationship between blockchain’s decentralization and sustainable economic practices. The existing literature often glorifies the rapid market expansion of cryptocurrencies but overlooks how underlying blockchain technology can fundamentally enhance content platforms through a more structured user engagement and equitable reward system. This study proposes a new token economy architecture by adopting the triple-bottom -line (TBL) framework and validates its practicality and effectiveness through an analytic-hierarchy-process (AHP) survey of industry experts. The study shows that the most influential factor in a successful token economy is not profit maximization but fostering a user-centric community where engagement and empowerment are prioritized. This shift can be expected to combine blockchain technology with meaningful economic innovation by challenging traditional profit-driven business models and refocusing on sustainability and user value. Full article

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25 pages, 404 KiB

Open AccessArticle

Teamwork Conflict Management Training and Conflict Resolution Practice via Large Language Models

by Sakhi Aggrawal and Alejandra J. Magana

Future Internet 2024, 16(5), 177; https://doi.org/10.3390/fi16050177 - 19 May 2024

Cited by 2 | Viewed by 4286

Abstract

This study implements a conflict management training approach guided by principles of transformative learning and conflict management practice simulated via an LLM. Transformative learning is more effective when learners are engaged mentally and behaviorally in learning experiences. Correspondingly, the conflict management training approach [...] Read more.

This study implements a conflict management training approach guided by principles of transformative learning and conflict management practice simulated via an LLM. Transformative learning is more effective when learners are engaged mentally and behaviorally in learning experiences. Correspondingly, the conflict management training approach involved a three-step procedure consisting of a learning phase, a practice phase enabled by an LLM, and a reflection phase. Fifty-six students enrolled in a systems development course were exposed to the transformative learning approach to conflict management so they would be better prepared to address any potential conflicts within their teams as they approached a semester-long software development project. The study investigated the following: (1) How did the training and practice affect students’ level of confidence in addressing conflict? (2) Which conflict management styles did students use in the simulated practice? (3) Which strategies did students employ when engaging with the simulated conflict? The findings indicate that: (1) 65% of the students significantly increased in confidence in managing conflict by demonstrating collaborative, compromising, and accommodative approaches; (2) 26% of the students slightly increased in confidence by implementing collaborative and accommodative approaches; and (3) 9% of the students did not increase in confidence, as they were already confident in applying collaborative approaches. The three most frequently used strategies for managing conflict were identifying the root cause of the problem, actively listening, and being specific and objective in explaining their concerns. Full article

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18 pages, 385 KiB

Open AccessArticle

MetaSSI: A Framework for Personal Data Protection, Enhanced Cybersecurity and Privacy in Metaverse Virtual Reality Platforms

by Faisal Fiaz, Syed Muhammad Sajjad, Zafar Iqbal, Muhammad Yousaf and Zia Muhammad

Future Internet 2024, 16(5), 176; https://doi.org/10.3390/fi16050176 - 18 May 2024

Cited by 3 | Viewed by 2249

Abstract

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including [...] Read more.

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including safe access and data privacy, which are necessary with the advent of Metaverse technology. Traditional, centralized, and network-centered solutions fail to provide a resilient identity management solution. There are multifaceted security and privacy issues that hinder the secure adoption of this game-changing technology in contemporary cyberspace. Moreover, there is a need to dedicate efforts towards a secure-by-design Metaverse that protects the confidentiality, integrity, and privacy of the personally identifiable information (PII) of users. In this research paper, we propose a logical substitute for established centralized identity management systems in compliance with the complexity of the Metaverse. This research proposes a sustainable Self-Sovereign Identity (SSI), a fully decentralized identity management system to mitigate PII leaks and corresponding cyber threats on all multiverse platforms. The principle of the proposed framework ensures that the users are the only custodians and proprietors of their own identities. In addition, this article provides a comprehensive approach to the implementation of the SSI principles to increase interoperability and trustworthiness in the Metaverse. Finally, the proposed framework is validated using mathematical modeling and proved to be stringent and resilient against modern-day cyber attacks targeting Metaverse platforms. Full article

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

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19 pages, 774 KiB

Open AccessArticle

Chatbots in Airport Customer Service—Exploring Use Cases and Technology Acceptance

by Isabel Auer, Stephan Schlögl and Gundula Glowka

Future Internet 2024, 16(5), 175; https://doi.org/10.3390/fi16050175 - 17 May 2024

Cited by 1 | Viewed by 1442

Abstract

Throughout the last decade, chatbots have gained widespread adoption across various industries, including healthcare, education, business, e-commerce, and entertainment. These types of artificial, usually cloud-based, agents have also been used in airport customer service, although there has been limited research concerning travelers’ perspectives [...] Read more.

Throughout the last decade, chatbots have gained widespread adoption across various industries, including healthcare, education, business, e-commerce, and entertainment. These types of artificial, usually cloud-based, agents have also been used in airport customer service, although there has been limited research concerning travelers’ perspectives on this rather techno-centric approach to handling inquiries. Consequently, the goal of the presented study was to tackle this research gap and explore potential use cases for chatbots at airports, as well as investigate travelers’ acceptance of said technology. We employed an extended version of the Technology Acceptance Model considering Perceived Usefulness, Perceived Ease of Use, Trust, and Perceived Enjoyment as predictors of Behavioral Intention, with Affinity for Technology as a potential moderator. A total of

n = 191

travelers completed our survey. The results show that Perceived Usefulness, Trust, Perceived Ease of Use, and Perceived Enjoyment positively correlate with the Behavioral Intention to use a chatbot for airport customer service inquiries, with Perceived Usefulness showing the highest impact. Travelers’ Affinity for Technology, on the other hand, does not seem to have any significant effect. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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21 pages, 5094 KiB

Open AccessArticle

TQU-SLAM Benchmark Dataset for Comparative Study to Build Visual Odometry Based on Extracted Features from Feature Descriptors and Deep Learning

by Thi-Hao Nguyen, Van-Hung Le, Huu-Son Do, Trung-Hieu Te and Van-Nam Phan

Future Internet 2024, 16(5), 174; https://doi.org/10.3390/fi16050174 - 17 May 2024

Viewed by 1121

Abstract

The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual [...] Read more.

The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual and conditional conditions will create a more accurate visual SLAM and VO construction model. In this paper, we introduce the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs. It was collected from the corridors of three interconnected buildings comprising a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset were prepared manually, including 6-DOF camera poses, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world. We also tested the TQU-SLAM benchmark dataset using the PySLAM framework with traditional features such as SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL such as VGG, DPVO, and TartanVO. The camera pose estimation results are evaluated, and we show that the ORB2 features have the best results (

E r r_{d}

= 5.74 mm), while the ratio of the number of frames with detected keypoints of the SHI_TOMASI feature is the best (

r_{d} = 98.97 %

). At the same time, we also present and analyze the challenges of the TQU-SLAM benchmark dataset for building visual SLAM and VO systems. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Computer Vision)

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29 pages, 1178 KiB

Open AccessReview

Machine Learning Strategies for Reconfigurable Intelligent Surface-Assisted Communication Systems—A Review

by Roilhi F. Ibarra-Hernández, Francisco R. Castillo-Soria, Carlos A. Gutiérrez, Abel García-Barrientos, Luis Alberto Vásquez-Toledo and J. Alberto Del-Puerto-Flores

Future Internet 2024, 16(5), 173; https://doi.org/10.3390/fi16050173 - 17 May 2024

Cited by 1 | Viewed by 2167

Abstract

Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic [...] Read more.

Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies. Full article

(This article belongs to the Special Issue 6G Wireless Communication Systems: Applications, Opportunities and Challenges, Volume III)

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Graphical abstract

20 pages, 658 KiB

Open AccessArticle

Using Optimization Techniques in Grammatical Evolution

by Ioannis G. Tsoulos, Alexandros Tzallas and Evangelos Karvounis

Future Internet 2024, 16(5), 172; https://doi.org/10.3390/fi16050172 - 16 May 2024

Cited by 1 | Viewed by 940

Abstract

The Grammatical Evolution technique has been successfully applied to a wide range of problems in various scientific fields. However, in many cases, techniques that make use of Grammatical Evolution become trapped in local minima of the objective problem and fail to reach the [...] Read more.

The Grammatical Evolution technique has been successfully applied to a wide range of problems in various scientific fields. However, in many cases, techniques that make use of Grammatical Evolution become trapped in local minima of the objective problem and fail to reach the optimal solution. One simple method to tackle such situations is the usage of hybrid techniques, where local minimization algorithms are used in conjunction with the main algorithm. However, Grammatical Evolution is an integer optimization problem and, as a consequence, techniques should be formulated that are applicable to it as well. In the current work, a modified version of the Simulated Annealing algorithm is used as a local optimization procedure in Grammatical Evolution. This approach was tested on the Constructed Neural Networks and a remarkable improvement of the experimental results was shown, both in classification data and in data fitting cases. Full article

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15 pages, 588 KiB

Open AccessArticle

SmartDED: A Blockchain- and Smart Contract-Based Digital Electronic Detonator Safety Supervision System

by Na Liu and Wei-Tek Tsai

Future Internet 2024, 16(5), 171; https://doi.org/10.3390/fi16050171 - 16 May 2024

Viewed by 869

Abstract

Digital electronic detonators, as a civil explosive, are of prime importance for people’s life and property safety in the process of production and operation. Therefore, the Ministry of Industry and Information Technology and the Ministry of Public Security of the People’s Republic of [...] Read more.

Digital electronic detonators, as a civil explosive, are of prime importance for people’s life and property safety in the process of production and operation. Therefore, the Ministry of Industry and Information Technology and the Ministry of Public Security of the People’s Republic of China have extremely high requirements for their essential safety. Existing schemes are vulnerable to tampering and single points of failure, which makes tracing unqualified digital electronic detonators difficult and identifying the responsibility for digital electronic detonator accidents hard. This paper presents a digital electronic detonator safety supervision system based on a consortium blockchain. To achieve dynamic supply chain supervision, we propose a novel digital electronic detonator supervision model together with three codes in one. We also propose a blockchain-based system that employs smart contracts to achieve efficient traceability and ensure security. We implemented the proposed model using a consortium blockchain platform and provide the cost. The evaluation results validate that the proposed system is efficient. Full article

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23 pages, 18705 KiB

Open AccessArticle

Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface

by Alireza Fath, Nicholas Hanna, Yi Liu, Scott Tanch, Tian Xia and Dryver Huston

Future Internet 2024, 16(5), 170; https://doi.org/10.3390/fi16050170 - 15 May 2024

Cited by 2 | Viewed by 1362

Abstract

Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability [...] Read more.

Sensing and cognition by homeowners and technicians for home maintenance are prime examples of human–building interaction. Damage, decay, and pest infestation present signals that humans interpret and then act upon to remedy and mitigate. The maintenance cognition process has direct effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern technologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recommend courses of remedial action. The demonstrated technologies include robotic sensing in confined areas, LiDAR scans of structural shape and deformation, moisture and gas sensing, water leak detection, network embedded ML, and augmented reality interfaces with multi-user teaming capabilities. The sensor information passes through a private local dynamic network to processors with neural network pattern recognition capabilities to abstract the information, which then feeds to humans through augmented reality and conventional smart device interfaces. This networked sensor system serves as a testbed and demonstrator for home maintenance technologies, for what can be termed Home Maintenance 4.0. Full article

(This article belongs to the Special Issue Advances in Extended Reality for Smart Cities)

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16 pages, 1055 KiB

Open AccessArticle

Blockchain and Smart Contracts for Digital Copyright Protection

by Franco Frattolillo

Future Internet 2024, 16(5), 169; https://doi.org/10.3390/fi16050169 - 14 May 2024

Cited by 1 | Viewed by 1441

Abstract

In a global context characterized by a pressing need to find a solution to the problem of digital copyright protection, buyer-seller watermarking protocols based on asymmetric fingerprinting and adopting a “buyer-friendly” approach have proven effective in addressing such a problem. They can ensure [...] Read more.

In a global context characterized by a pressing need to find a solution to the problem of digital copyright protection, buyer-seller watermarking protocols based on asymmetric fingerprinting and adopting a “buyer-friendly” approach have proven effective in addressing such a problem. They can ensure high levels of usability and security. However, they usually resort to trusted third parties (TTPs) to guarantee the protection process, and this is often perceived as a relevant drawback since TTPs may cause conspiracy or collusion problems, besides the fact that they are generally considered as some sort of “big brother”. This paper presents a buyer-seller watermarking protocol that can achieve the right compromise between usability and security without employing a TTP. The protocol is built around previous experiences conducted in the field of protocols based on the buyer-friendly approach. Its peculiarity consists of exploiting smart contracts executed within a blockchain to implement preset and immutable rules that run automatically under specific conditions without control from some kind of central authority. The result is a simple, usable, and secure watermarking protocol able to do without TTPs. Full article

(This article belongs to the Section Cybersecurity)

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31 pages, 5255 KiB

Open AccessArticle

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

by Muhammad Imran, Annalisa Appice and Donato Malerba

Future Internet 2024, 16(5), 168; https://doi.org/10.3390/fi16050168 - 12 May 2024

Cited by 1 | Viewed by 2718

Abstract

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that [...] Read more.

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them. Adversarial attacks are attack samples produced by carefully manipulating the samples at the test time to violate the model integrity by causing detection mistakes. In this paper, we analyse the performance of five realistic target-based adversarial attacks, namely Extend, Full DOS, Shift, FGSM padding + slack and GAMMA, against two machine learning models, namely MalConv and LGBM, learned to recognise Windows Portable Executable (PE) malware files. Specifically, MalConv is a Convolutional Neural Network (CNN) model learned from the raw bytes of Windows PE files. LGBM is a Gradient-Boosted Decision Tree model that is learned from features extracted through the static analysis of Windows PE files. Notably, the attack methods and machine learning models considered in this study are state-of-the-art methods broadly used in the machine learning literature for Windows PE malware detection tasks. In addition, we explore the effect of accounting for adversarial attacks on securing machine learning models through the adversarial training strategy. Therefore, the main contributions of this article are as follows: (1) We extend existing machine learning studies that commonly consider small datasets to explore the evasion ability of state-of-the-art Windows PE attack methods by increasing the size of the evaluation dataset. (2) To the best of our knowledge, we are the first to carry out an exploratory study to explain how the considered adversarial attack methods change Windows PE malware to fool an effective decision model. (3) We explore the performance of the adversarial training strategy as a means to secure effective decision models against adversarial Windows PE malware files generated with the considered attack methods. Hence, the study explains how GAMMA can actually be considered the most effective evasion method for the performed comparative analysis. On the other hand, the study shows that the adversarial training strategy can actually help in recognising adversarial PE malware generated with GAMMA by also explaining how it changes model decisions. Full article

(This article belongs to the Collection Information Systems Security)

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22 pages, 7403 KiB

Open AccessArticle

A Hybrid Semi-Automated Workflow for Systematic and Literature Review Processes with Large Language Model Analysis

by Anjia Ye, Ananda Maiti, Matthew Schmidt and Scott J. Pedersen

Future Internet 2024, 16(5), 167; https://doi.org/10.3390/fi16050167 - 12 May 2024

Viewed by 2342

Abstract

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT [...] Read more.

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT have the potential to reduce the human workload of the SR process while maintaining accuracy. We propose a new hybrid methodology that combines the strengths of LLMs and humans using the ability of LLMs to summarize large bodies of text autonomously and extract key information. This is then used by a researcher to make inclusion/exclusion decisions quickly. This process replaces the typical manually performed title/abstract screening, full-text screening, and data extraction steps in an SR while keeping a human in the loop for quality control. We developed a semi-automated LLM-assisted (Gemini-Pro) workflow with a novel innovative prompt development strategy. This involves extracting three categories of information including identifier, verifier, and data field (IVD) from the formatted documents. We present a case study where our hybrid approach reduced errors compared with a human-only SR. The hybrid workflow improved the accuracy of the case study by identifying 6/390 (1.53%) articles that were misclassified by the human-only process. It also matched the human-only decisions completely regarding the rest of the 384 articles. Given the rapid advances in LLM technology, these results will undoubtedly improve over time. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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18 pages, 2302 KiB

Open AccessArticle

Blockchain-Enabled Secure and Interoperable Authentication Scheme for Metaverse Environments

by Sonali Patwe and Sunil B. Mane

Future Internet 2024, 16(5), 166; https://doi.org/10.3390/fi16050166 - 11 May 2024

Viewed by 1394

Abstract

The metaverse, which amalgamates physical and virtual realms for diverse social activities, has been the focus of extensive application development by organizations, research institutes, and companies. However, these applications are often isolated, employing distinct authentication methods across platforms. Achieving interoperable authentication is crucial [...] Read more.

The metaverse, which amalgamates physical and virtual realms for diverse social activities, has been the focus of extensive application development by organizations, research institutes, and companies. However, these applications are often isolated, employing distinct authentication methods across platforms. Achieving interoperable authentication is crucial for when avatars traverse different metaverses to mitigate security concerns like impersonation, mutual authentication, replay, and server spoofing. To address these issues, we propose a blockchain-enabled secure and interoperable authentication scheme. This mechanism uniquely identifies users in the physical world as well as avatars, facilitating seamless navigation across verses. Our proposal is substantiated through informal security analyses, employing automated verification of internet security protocols and applications (AVISPA), the real-or-random (ROR) model, and Burrows–Abadi–Needham (BAN) logic and showcasing effectiveness against a broad spectrum of security threats. Comparative assessments against similar schemes demonstrate our solution’s superiority in terms of communication costs, computation costs, and security features. Consequently, our blockchain-enabled, interoperable, and secure authentication scheme stands as a robust solution for ensuring security in metaverse environments. Full article

(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)

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20 pages, 814 KiB

Open AccessArticle

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

by Yuzhu Zhang and Hao Xu

Future Internet 2024, 16(5), 165; https://doi.org/10.3390/fi16050165 - 11 May 2024

Cited by 1 | Viewed by 1355

Abstract

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited [...] Read more.

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks. Full article

(This article belongs to the Special Issue Social Internet of Things (SIoT))

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18 pages, 960 KiB

Open AccessArticle

pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework

by Yu Zhang, Xiaowei Peng and Hequn Xian

Future Internet 2024, 16(5), 164; https://doi.org/10.3390/fi16050164 - 11 May 2024

Viewed by 1003

Abstract

As network technology advances, there is an increasing need for a trusted new-generation information management system. Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data [...] Read more.

As network technology advances, there is an increasing need for a trusted new-generation information management system. Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data privacy protection, server-side security concerns persist. Traditional methods have employed a blockchain system in FL frameworks to maintain a tamper-proof global model database. In this context, we propose a novel personalized federated learning (pFL) with blockchain-assisted semi-centralized framework, pFedBASC. This approach, tailored for the Internet of Things (IoT) scenarios, constructs a semi-centralized IoT structure and utilizes trusted network connections to support FL. We concentrate on designing the aggregation process and FL algorithm, as well as the block structure. To address data heterogeneity and communication costs, we propose a pFL method called FedHype. In this method, each client is assigned a compact hypernetwork (HN) alongside a normal target network (TN) whose parameters are generated by the HN. Clients pull together other clients’ HNs for local aggregation to personalize their TNs, reducing communication costs. Furthermore, FedHype can be integrated with other existing algorithms, enhancing its functionality. Experimental results reveal that pFedBASC effectively tackles data heterogeneity issues while maintaining positive accuracy, communication efficiency, and robustness. Full article

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

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13 pages, 1589 KiB

Open AccessArticle

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

by Zhe Ma, Xuhesheng Chen, Tiejiang Sun, Xukang Wang, Ying Cheng Wu and Mengjie Zhou

Future Internet 2024, 16(5), 163; https://doi.org/10.3390/fi16050163 - 10 May 2024

Viewed by 1619

Abstract

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based [...] Read more.

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks. Full article

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19 pages, 474 KiB

Open AccessArticle

BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading

by Caixiang Fan, Hamzeh Khazaei and Petr Musilek

Future Internet 2024, 16(5), 162; https://doi.org/10.3390/fi16050162 - 7 May 2024

Cited by 1 | Viewed by 1259

Abstract

Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is [...] Read more.

Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market. Full article

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15 pages, 2223 KiB

Open AccessArticle

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

by Jieli Chen, Kah Phooi Seng, Li Minn Ang, Jeremy Smith and Hanyue Xu

Future Internet 2024, 16(5), 161; https://doi.org/10.3390/fi16050161 - 7 May 2024

Cited by 1 | Viewed by 1329

Abstract

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in [...] Read more.

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks. Full article

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18 pages, 620 KiB

Open AccessArticle

Optimizing Requirements Prioritization for IoT Applications Using Extended Analytical Hierarchical Process and an Advanced Grouping Framework

by Sarah Kaleem, Muhammad Asim, Mohammed El-Affendi and Muhammad Babar

Future Internet 2024, 16(5), 160; https://doi.org/10.3390/fi16050160 - 6 May 2024

Viewed by 1339

Abstract

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with [...] Read more.

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled Internet of Things (IoT))

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24 pages, 6630 KiB

Open AccessArticle

Enhanced Multi-Task Traffic Forecasting in Beyond 5G Networks: Leveraging Transformer Technology and Multi-Source Data Fusion

by Ibrahim Althamary, Rubbens Boisguene and Chih-Wei Huang

Future Internet 2024, 16(5), 159; https://doi.org/10.3390/fi16050159 - 5 May 2024

Viewed by 1610

Abstract

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. [...] Read more.

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era. Full article

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16 pages, 4908 KiB

Open AccessArticle

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

by Lassaad Zaway, Nader Ben Amor, Jalel Ktari, Mohamed Jallouli, Larbi Chrifi Alaoui and Laurent Delahoche

Future Internet 2024, 16(5), 158; https://doi.org/10.3390/fi16050158 - 3 May 2024

Viewed by 1381

Abstract

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution [...] Read more.

Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology. Full article

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

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17 pages, 429 KiB

Open AccessArticle

Realization of Authenticated One-Pass Key Establishment on RISC-V Micro-Controller for IoT Applications

by Tuan-Kiet Dang, Khai-Duy Nguyen, Binh Kieu-Do-Nguyen, Trong-Thuc Hoang and Cong-Kha Pham

Future Internet 2024, 16(5), 157; https://doi.org/10.3390/fi16050157 - 3 May 2024

Cited by 2 | Viewed by 1201

Abstract

Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to [...] Read more.

Internet-of-things networks consist of multiple sensor devices spread over a wide area. In order to protect the data from unauthorized access and tampering, it is essential to ensure secure communication between the sensor devices and the central server. This security measure aims to guarantee authenticity, confidentiality, and data integrity. Unlike traditional computing systems, sensor node devices are often limited regarding memory and computing power. Lightweight communication protocols, such as LoRaWAN, were introduced to overcome these limitations. However, despite the lightweight feature, the protocol is vulnerable to different types of attacks. This proposal presents a highly secure key establishment protocol that combines two cryptography schemes: Elliptic Curve Qu–Vanstone and signcryption key encapsulation. The protocol provides a method to establish a secure channel that inherits the security properties of the two schemes. Also, it allows for fast rekeying with only one exchange message, significantly reducing the handshake complexity in low-bandwidth communication. In addition, the selected schemes complement each other and share the same mathematical operations in elliptic curve cryptography. Moreover, with the rise of a community-friendly platform like RISC-V, we implemented the protocol on a RISC-V system to evaluate its overheads regarding the cycle count and execution time. Full article

(This article belongs to the Special Issue Cybersecurity in the IoT)

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27 pages, 2334 KiB

Open AccessArticle

A Fair Crowd-Sourced Automotive Data Monetization Approach Using Substrate Hybrid Consensus Blockchain

by Cyril Naves Samuel, François Verdier, Severine Glock and Patricia Guitton-Ouhamou

Future Internet 2024, 16(5), 156; https://doi.org/10.3390/fi16050156 - 30 Apr 2024

Viewed by 1511

Abstract

This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the [...] Read more.

This work presents a private consortium blockchain-based automotive data monetization architecture implementation using the Substrate blockchain framework. Architecture is decentralized where crowd-sourced data from vehicles are collectively auctioned ensuring data privacy and security. Smart Contracts and OffChain worker interactions built along with the blockchain make it interoperable with external systems to send or receive data. The work is deployed in a Kubernetes cloud platform and evaluated on different parameters like throughput, hybrid consensus algorithms AuRa and BABE, along with GRANDPA performance in terms of forks and scalability for increasing node participants. The hybrid consensus algorithms are studied in depth to understand the difference and performance in the separation of block creation by AuRa and BABE followed by chain finalization through the GRANDPA protocol. Full article

(This article belongs to the Special Issue Security in the Internet of Things (IoT))

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13 pages, 486 KiB

Open AccessArticle

Novel Approach towards a Fully Deep Learning-Based IoT Receiver Architecture: From Estimation to Decoding

by Matthew Boeding, Michael Hempel and Hamid Sharif

Future Internet 2024, 16(5), 155; https://doi.org/10.3390/fi16050155 - 30 Apr 2024

Cited by 2 | Viewed by 1468

Abstract

As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially [...] Read more.

As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially with the advances of 5G/6G technology. Nevertheless, the transmission of wireless communications demands substantial power and computational resources, presenting a significant challenge to these devices and their operations. In this work, we propose the use of deep learning to improve the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) wireless receivers. By improving the BER performance of these receivers, devices can transmit with less power, thereby improving IoT devices’ battery life. The architecture presented in this paper utilizes a depthwise Convolutional Neural Network (CNN) for channel estimation and demodulation, whereas a Graph Neural Network (GNN) is utilized for Low-Density Parity Check (LDPC) decoding, tested against a proposed (1998, 1512) LDPC code. Our results show higher performance than traditional receivers in both isolated tests for the CNN and GNN, and a combined end-to-end test with lower computational complexity than other proposed deep learning models. For BER improvement, our proposed approach showed a 1 dB improvement for eliminating BER in QPSK models. Additionally, it improved 16-QAM Rician BER by five decades, 16-QAM LOS model BER by four decades, 64-QAM Rician BER by 2.5 decades, and 64-QAM LOS model BER by three decades. Full article

(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in USA 2024–2025)

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27 pages, 3323 KiB

Open AccessArticle

A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy

by John David Sprunger, Alvin Lim and David M. Bevly

Future Internet 2024, 16(5), 154; https://doi.org/10.3390/fi16050154 - 29 Apr 2024

Viewed by 1330

Abstract

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by [...] Read more.

Convoys composed of autonomous vehicles could improve the transportation and freight industries in several ways. One of the avenues of improvement is in fuel efficiency, where the vehicles maintain a close following distance to each other in order to reduce air resistance by way of the draft effect. While close following distances improve fuel efficiency, they also reduce both the margin of safety and the system’s tolerance to disturbances in relative position. The system’s tolerance to disturbances is known as string stability, where the error magnitude either grows or decays as it propagates rearward through the convoy. One of the major factors in a system’s string stability is its delay in sending state updates to other vehicles, the most pertinent being a hard braking maneuver. Both external sensors and vehicle-to-vehicle communication standards have relatively long delays between peer vehicle state changes and the information being actionable by the ego vehicle. The system presented here, called the Convoy Vehicular Ad Hoc Network (Convoy VANET), was designed to reliably propagate emergency event messages with low delay while maintaining reasonable channel efficiency. It accomplishes this using a combination of several techniques, notably relative position-based retransmission delays. Our results using Network Simulator 3 (ns3) show the system propagating messages down a 20-vehicle convoy in less than 100 ms even with more than a 35% message loss between vehicles that are not immediately adjacent. These simulation results show the potential for this kind of system in situations where emergency information must be disseminated quickly in low-reliability wireless environments. Full article

(This article belongs to the Special Issue Inter-Vehicle Communication Protocols and Their Applications)

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23 pages, 1350 KiB

Open AccessArticle

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

by Daniel Nuñez-Agurto, Walter Fuertes, Luis Marrone, Eduardo Benavides-Astudillo, Christian Coronel-Guerrero and Franklin Perez

Future Internet 2024, 16(5), 153; https://doi.org/10.3390/fi16050153 - 29 Apr 2024

Cited by 2 | Viewed by 1819

Abstract

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized [...] Read more.

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment. Full article

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

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15 pages, 518 KiB

Open AccessArticle

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

by Mansoor Jamal, Zaib Ullah, Muddasar Naeem, Musarat Abbas and Antonio Coronato

Future Internet 2024, 16(5), 152; https://doi.org/10.3390/fi16050152 - 28 Apr 2024

Cited by 1 | Viewed by 1533

Abstract

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple [...] Read more.

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer. Full article

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

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16 pages, 1437 KiB

Open AccessArticle

Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network

by Aye Nyein Aung, Che-Wei Liao and Jeih-Weih Hung

Future Internet 2024, 16(5), 151; https://doi.org/10.3390/fi16050151 - 28 Apr 2024

Cited by 1 | Viewed by 1153

Abstract

Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either [...] Read more.

Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separation algorithms are often built on a deep neural network (DNN) structure, which seeks to learn the complex relationship between the speech mixture and any specific speech source of interest. DNN-based speech separation algorithms outperform conventional statistics-based methods, although they typically need a lot of processing and/or a larger model size. This study presents a new end-to-end speech separation network called ESC-MASD-Net (effective speaker separation through convolutional multi-view attention and SuDoRM-RF network), which has relatively fewer model parameters compared with the state-of-the-art speech separation architectures. The network is partly inspired by the SuDoRM-RF++ network, which uses multiple time-resolution features with downsampling and resampling for effective speech separation. ESC-MASD-Net incorporates the multi-view attention and residual conformer modules into SuDoRM-RF++. Additionally, the U-Convolutional block in ESC-MASD-Net is refined with a conformer layer. Experiments conducted on the WHAM! dataset show that ESC-MASD-Net outperforms SuDoRM-RF++ significantly in the SI-SDRi metric. Furthermore, the use of the conformer layer has also improved the performance of ESC-MASD-Net. Full article

(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)

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15 pages, 2087 KiB

Open AccessArticle

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

by Jingzhe Zhang, Tiange Chen, Wenjie Gong, Jiayue Liu and Jiangjie Chen

Future Internet 2024, 16(5), 150; https://doi.org/10.3390/fi16050150 - 27 Apr 2024

Cited by 1 | Viewed by 1246

Abstract

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was [...] Read more.

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques’ input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques’ pros and cons and offers design guidelines and future research directions. Full article

(This article belongs to the Special Issue Novel Advances in Collaborative Environments for Virtual, Augmented, Mixed and Extended Reality)

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22 pages, 8080 KiB

Open AccessArticle

A Blockchain-Based Real-Time Power Balancing Service for Trustless Renewable Energy Grids

by Andrea Calvagna, Giovanni Marotta, Giuseppe Pappalardo and Emiliano Tramontana

Future Internet 2024, 16(5), 149; https://doi.org/10.3390/fi16050149 - 26 Apr 2024

Cited by 1 | Viewed by 1322

Abstract

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing [...] Read more.

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing produced vs consumed energy flows. In this context, being able to provide quick restore actions in response to unpredictable unbalancing events is a must, as fluctuations are the norm for renewable energy sources. To this aim, the high scalability and diversity of sources are crucial requirements for the said balancing to be actually manageable. In this study, we explored the challenges and benefits of adopting a blockchain-based software architecture as a scalable, trustless interaction platform between prosumers’ smart energy meters and the distributor. Our developed prototype accomplishes the energy load balancing service via smart contracts deployed in a real blockchain network with an increasing number of simulated prosumers. We show that the blockchain-based application managed to react in a timely manner to energy unbalances for up to a few hundred prosumers. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Decentralized Edge Environments)

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Future Internet, Volume 16, Issue 5 (May 2024) – 35 articles

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