Journal Description
Future Internet
Future Internet
is an international, peer-reviewed, open access journal on internet technologies and the information society, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
- Journal Rank: CiteScore - Q1 (Computer Networks and Communications)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 11.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
Teamwork Conflict Management Training and Conflict Resolution Practice via Large Language Models
Future Internet 2024, 16(5), 177; https://doi.org/10.3390/fi16050177 (registering DOI) - 19 May 2024
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
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 (registering DOI) - 18 May 2024
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)
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
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 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)
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
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 ( = 5.74 mm), while the ratio of the number of frames with detected keypoints of the SHI_TOMASI feature is the best ( ). 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)
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Graphical abstract
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
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
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
Abstract
►▼
Show Figures
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
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
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))
►▼
Show Figures
Figure 1
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
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)
►▼
Show Figures
Figure 1
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
Abstract
►▼
Show Figures
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
Figure 1
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
Abstract
►▼
Show Figures
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
Figure 1
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
Abstract
►▼
Show Figures
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
Figure 1
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
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))
►▼
Show Figures
Figure 1
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
Abstract
►▼
Show Figures
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
Figure 1
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
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)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Future Internet Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Drones, Electronics, Future Internet, Information, Mathematics
Future Internet Architecture: Difficulties and Opportunities
Topic Editors: Peiying Zhang, Haotong Cao, Keping YuDeadline: 30 June 2024
Topic in
Algorithms, Future Internet, Information, Mathematics, Symmetry
Research on Data Mining of Electronic Health Records Using Deep Learning Methods
Topic Editors: Dawei Yang, Yu Zhu, Hongyi XinDeadline: 31 August 2024
Topic in
Algorithms, Axioms, Future Internet, Mathematics, Symmetry
Multimodal Sentiment Analysis Based on Deep Learning Methods Such as Convolutional Neural Networks
Topic Editors: Junaid Baber, Ali Shariq Imran, Sher Doudpota, Maheen BakhtyarDeadline: 31 October 2024
Topic in
Entropy, Future Internet, Healthcare, MAKE, Sensors
Communications Challenges in Health and Well-Being
Topic Editors: Dragana Bajic, Konstantinos Katzis, Gordana GardasevicDeadline: 20 November 2024
Conferences
Special Issues
Special Issue in
Future Internet
QoS in Wireless Sensor Network for IoT Applications
Guest Editor: Pascal LorenzDeadline: 25 May 2024
Special Issue in
Future Internet
Semantic and Social Internet of Things
Guest Editors: Konstantinos Kotis, Christos GoumopoulosDeadline: 31 May 2024
Special Issue in
Future Internet
Smart Sensorics and Robotics for IoT- and AI-Empowered Monitoring and Communication
Guest Editors: Zhongliang Zhao, Dmitry KorzunDeadline: 20 June 2024
Special Issue in
Future Internet
Machine Learning for Blockchain and IoT System in Smart Cities
Guest Editors: José A. Afonso, Joao FerreiraDeadline: 30 June 2024
Topical Collections
Topical Collection in
Future Internet
Featured Reviews of Future Internet Research
Collection Editor: Dino Giuli
Topical Collection in
Future Internet
5G/6G Networks for the Internet of Things: Communication Technologies and Challenges
Collection Editor: Sachin Sharma
Topical Collection in
Future Internet
Computer Vision, Deep Learning and Machine Learning with Applications
Collection Editors: Remus Bard, Arpad Gellert
Topical Collection in
Future Internet
Innovative People-Centered Solutions Applied to Industries, Cities and Societies
Collection Editors: Dino Giuli, Filipe Portela