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
Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection
Future Internet 2024, 16(5), 168; https://doi.org/10.3390/fi16050168 (registering DOI) - 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
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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.
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(This article belongs to the Collection Information Systems Security)
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 (registering DOI) - 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
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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.
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(This article belongs to the Section Big Data and Augmented Intelligence)
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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 (registering DOI) - 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
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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.
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(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)
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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 (registering DOI) - 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
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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.
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(This article belongs to the Special Issue Social Internet of Things (SIoT))
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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 (registering DOI) - 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
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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)
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 (registering DOI) - 10 May 2024
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
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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.
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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
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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
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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.
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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
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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
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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.
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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
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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.
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(This article belongs to the Special Issue Artificial Intelligence-Enabled Internet of Things (IoT))
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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
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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.
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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.
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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
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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.
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(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction)
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Open AccessArticle
Realization of Authenticated One-Pass Key Establishment on RISC-V Micro-Controller for IoT Applications
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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
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
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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.
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(This article belongs to the Special Issue Cybersecurity in the IoT)
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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
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
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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.
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(This article belongs to the Special Issue Security in the Internet of Things (IoT))
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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
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
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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.
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(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in USA 2024–2025)
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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
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
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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.
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(This article belongs to the Special Issue Inter-Vehicle Communication Protocols and Their Applications)
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Open AccessArticle
A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
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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
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
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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.
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(This article belongs to the Section Smart System Infrastructure and Applications)
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A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
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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
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
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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.
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(This article belongs to the Special Issue IoT, Edge, and Cloud Computing in Smart Cities)
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Open AccessArticle
Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
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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
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
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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.
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(This article belongs to the Special Issue AI and Security in 5G Cooperative Cognitive Radio Networks)
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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
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
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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.
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(This article belongs to the Special Issue Novel Advances in Collaborative Environments for Virtual, Augmented, Mixed and Extended Reality)
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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
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
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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.
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(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Decentralized Edge Environments)
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