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
HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network
Future Internet 2024, 16(6), 185; https://doi.org/10.3390/fi16060185 - 23 May 2024
Abstract
Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of
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Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of driving assistance systems and impairment of vehicle control functionalities, seriously disrupting the normal operation of the vehicle. In this paper, we propose a novel DDoS attack detection method for in-vehicle Ethernet Scalable service-Oriented Middleware over IP (SOME/IP), which integrates the Hawkes process with Long Short-Term Memory networks (LSTMs) to capture the dynamic behavioral features of the attacker. Specifically, we employ the Hawkes process to capture features of the DDoS attack, with its parameters reflecting the dynamism and self-exciting properties of the attack events. Subsequently, we propose a novel deep learning network structure, an HP-LSTM block, inspired by the Hawkes process, while employing a residual attention block to enhance the model’s detection efficiency and accuracy. Additionally, due to the scarcity of publicly available datasets for SOME/IP, we employed a mature SOME/IP generator to create a dataset for evaluating the validity of the proposed detection model. Finally, extensive experiments were conducted to demonstrate the effectiveness of the proposed DDoS attack detection method.
Full article
(This article belongs to the Special Issue Security for Vehicular Ad Hoc Networks)
Open AccessArticle
Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
by
Fouzi Harrou, Benamar Bouyeddou, Abdelkader Dairi and Ying Sun
Future Internet 2024, 16(6), 184; https://doi.org/10.3390/fi16060184 - 22 May 2024
Abstract
The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in power outages and disruptions.
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The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in power outages and disruptions. This paper presents a semi-supervised hybrid deep learning model that combines a Gated Recurrent Unit (GRU)-based Stacked Autoencoder (AE-GRU) with anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptical Envelope. Using GRU units in both the encoder and decoder sides of the stacked autoencoder enables the effective capture of temporal patterns and dependencies, facilitating dimensionality reduction, feature extraction, and accurate reconstruction for enhanced anomaly detection in smart grids. The proposed approach utilizes unlabeled data to monitor network traffic and identify suspicious data flow. Specifically, the AE-GRU is performed for data reduction and extracting relevant features, and then the anomaly algorithms are applied to reveal potential cyberattacks. The proposed framework is evaluated using the widely adopted IEC 60870-5-104 traffic dataset. The experimental results demonstrate that the proposed approach outperforms standalone algorithms, with the AE-GRU-based LOF method achieving the highest detection rate. Thus, the proposed approach can potentially enhance the cybersecurity in smart grids by accurately detecting and preventing cyberattacks.
Full article
(This article belongs to the Special Issue Cybersecurity in the IoT)
Open AccessArticle
Cross-Layer Optimization for Enhanced IoT Connectivity: A Novel Routing Protocol for Opportunistic Networks
by
Ayman Khalil and Besma Zeddini
Future Internet 2024, 16(6), 183; https://doi.org/10.3390/fi16060183 - 22 May 2024
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Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic
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Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes’ meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as the last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.
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Open AccessSystematic Review
Urban Green Spaces and Mental Well-Being: A Systematic Review of Studies Comparing Virtual Reality versus Real Nature
by
Liyuan Liang, Like Gobeawan, Siu-Kit Lau, Ervine Shengwei Lin and Kai Keng Ang
Future Internet 2024, 16(6), 182; https://doi.org/10.3390/fi16060182 - 21 May 2024
Abstract
Increasingly, urban planners are adopting virtual reality (VR) in designing urban green spaces (UGS) to visualize landscape designs in immersive 3D. However, the psychological effect of green spaces from the experience in VR may differ from the actual experience in the real world.
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Increasingly, urban planners are adopting virtual reality (VR) in designing urban green spaces (UGS) to visualize landscape designs in immersive 3D. However, the psychological effect of green spaces from the experience in VR may differ from the actual experience in the real world. In this paper, we systematically reviewed studies in the literature that conducted experiments to investigate the psychological benefits of nature in both VR and the real world to study nature in VR anchored to nature in the real world. We separated these studies based on the type of VR setup used, specifically, 360-degree video or 3D virtual environment, and established a framework of commonly used standard questionnaires used to measure the perceived mental states. The most common questionnaires include Positive and Negative Affect Schedule (PANAS), Perceived Restorativeness Scale (PRS), and Restoration Outcome Scale (ROS). Although the results from studies that used 360-degree video were less clear, results from studies that used 3D virtual environments provided evidence that virtual nature is comparable to real-world nature and thus showed promise that UGS designs in VR can transfer into real-world designs to yield similar physiological effects.
Full article
(This article belongs to the Special Issue Advances in Extended Reality for Smart Cities)
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Open AccessArticle
MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
by
Yuchen Wang, Zishan Huang, Zhongcheng Wei and Jijun Zhao
Future Internet 2024, 16(6), 181; https://doi.org/10.3390/fi16060181 - 21 May 2024
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With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application
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With the increasing popularity of the Internet of Things (IoT), the proliferation of computation-intensive and timing-dependent applications has brought serious load pressure on terrestrial networks. In order to solve the problem of computing resource conflict and long response delay caused by concurrent application service applications from multiple users, this paper proposes an improved edge computing timing-dependent, task-offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that aims to shorten the offloading delay and improve the resource utilization rate by means of resource prediction and collaboration among multiple agents to shorten the offloading delay and improve the resource utilization. First, to coordinate the global computing resource, the gated recurrent unit is utilized, which predicts the next computing resource requirements of the timing-dependent tasks according to historical information. Second, the predicted information, the historical offloading decisions and the current state are used as inputs, and the training process of the reinforcement learning algorithm is improved to propose a task-offloading algorithm based on MADDPG. The simulation results show that the algorithm reduces the response latency by 6.7% and improves the resource utilization by 30.6% compared with the suboptimal benchmark algorithm, and it reduces nearly 500 training rounds during the learning process, which effectively improves the timeliness of the offloading strategy.
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Open AccessReview
Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
by
Nuno Marques, Rodrigo Rocha Silva and Jorge Bernardino
Future Internet 2024, 16(6), 180; https://doi.org/10.3390/fi16060180 - 21 May 2024
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Large language models (LLMs) have had a significant impact on several domains, including software engineering. However, a comprehensive understanding of LLMs’ use, impact, and potential limitations in software engineering is still emerging and remains in its early stages. This paper analyzes the role
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Large language models (LLMs) have had a significant impact on several domains, including software engineering. However, a comprehensive understanding of LLMs’ use, impact, and potential limitations in software engineering is still emerging and remains in its early stages. This paper analyzes the role of large language models (LLMs), such as ChatGPT-3.5, in software requirements engineering, a critical area in software engineering experiencing rapid advances due to artificial intelligence (AI). By analyzing several studies, we systematically evaluate the integration of ChatGPT into software requirements engineering, focusing on its benefits, challenges, and ethical considerations. This evaluation is based on a comparative analysis that highlights ChatGPT’s efficiency in eliciting requirements, accuracy in capturing user needs, potential to improve communication among stakeholders, and impact on the responsibilities of requirements engineers. The selected studies were analyzed for their insights into the effectiveness of ChatGPT, the importance of human feedback, prompt engineering techniques, technological limitations, and future research directions in using LLMs in software requirements engineering. This comprehensive analysis aims to provide a differentiated perspective on how ChatGPT can reshape software requirements engineering practices and provides strategic recommendations for leveraging ChatGPT to effectively improve the software requirements engineering process.
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Open AccessArticle
Object and Event Detection Pipeline for Rink Hockey Games
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Jorge Miguel Lopes, Luis Paulo Mota, Samuel Marques Mota, José Manuel Torres, Rui Silva Moreira, Christophe Soares, Ivo Pereira, Feliz Ribeiro Gouveia and Pedro Sobral
Future Internet 2024, 16(6), 179; https://doi.org/10.3390/fi16060179 - 21 May 2024
Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also
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All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm’s performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.
Full article
(This article belongs to the Special Issue Advances Techniques in Computer Vision and Multimedia II)
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Open AccessArticle
Validation of Value-Driven Token Economy: Focus on Blockchain Content Platform
by
Young Sook Kim, Seng-Phil Hong and Marko Majer
Future Internet 2024, 16(5), 178; https://doi.org/10.3390/fi16050178 - 20 May 2024
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This study explores the architectural framework of a value-driven token economy on a blockchain content platform and critically evaluates the relationship between blockchain’s decentralization and sustainable economic practices. The existing literature often glorifies the rapid market expansion of cryptocurrencies but overlooks how underlying
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This study explores the architectural framework of a value-driven token economy on a blockchain content platform and critically evaluates the relationship between blockchain’s decentralization and sustainable economic practices. The existing literature often glorifies the rapid market expansion of cryptocurrencies but overlooks how underlying blockchain technology can fundamentally enhance content platforms through a more structured user engagement and equitable reward system. This study proposes a new token economy architecture by adopting the triple-bottom -line (TBL) framework and validates its practicality and effectiveness through an analytic-hierarchy-process (AHP) survey of industry experts. The study shows that the most influential factor in a successful token economy is not profit maximization but fostering a user-centric community where engagement and empowerment are prioritized. This shift can be expected to combine blockchain technology with meaningful economic innovation by challenging traditional profit-driven business models and refocusing on sustainability and user value.
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Open AccessArticle
Teamwork Conflict Management Training and Conflict Resolution Practice via Large Language Models
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Sakhi Aggrawal and Alejandra J. Magana
Future Internet 2024, 16(5), 177; https://doi.org/10.3390/fi16050177 - 19 May 2024
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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
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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.
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Open AccessArticle
MetaSSI: A Framework for Personal Data Protection, Enhanced Cybersecurity and Privacy in Metaverse Virtual Reality Platforms
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Faisal Fiaz, Syed Muhammad Sajjad, Zafar Iqbal, Muhammad Yousaf and Zia Muhammad
Future Internet 2024, 16(5), 176; https://doi.org/10.3390/fi16050176 - 18 May 2024
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
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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.
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(This article belongs to the Special Issue Advances and Perspectives in Human-Computer Interaction)
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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
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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)
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Open AccessArticle
TQU-SLAM Benchmark Dataset for Comparative Study to Build Visual Odometry Based on Extracted Features from Feature Descriptors and Deep Learning
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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
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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.
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(This article belongs to the Special Issue Machine Learning Techniques for Computer Vision)
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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
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Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system’s performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.
Full article
(This article belongs to the Special Issue 6G Wireless Communication Systems: Applications, Opportunities and Challenges, Volume III)
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Open AccessArticle
Using Optimization Techniques in Grammatical Evolution
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Ioannis G. Tsoulos, Alexandros Tzallas and Evangelos Karvounis
Future Internet 2024, 16(5), 172; https://doi.org/10.3390/fi16050172 - 16 May 2024
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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
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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.
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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
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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
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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.
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Open AccessArticle
Indoor Infrastructure Maintenance Framework Using Networked Sensors, Robots, and Augmented Reality Human Interface
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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
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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.
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(This article belongs to the Special Issue Advances in Extended Reality for Smart Cities)
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Open AccessArticle
Blockchain and Smart Contracts for Digital Copyright Protection
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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
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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.
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(This article belongs to the Section Cybersecurity)
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Open AccessArticle
Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection
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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
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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)
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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)
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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 - 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)
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