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A Secure and Trusted Communication Solution for Web 3.0 Based on Edge Intelligence

Internet Technology Letters 2025年第4期8卷

作者： Rathee, Geetanjali Cheriguene, Anissa Kerrache, Chaker Abdelaziz Calafate, Carlos T. Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India Department of English ENS of Laghouat Laghouat Algeria Laboratoire d'Informatique et de Mathématiques Université Amar Telidji de Laghouat Laghouat Algeria Universitat Politècnica de Valencia Valencia Spain

The rise of AI has positioned edge computing as a pivotal domain for deploying machine learning technologies, fostering agile processing, and enhancing network robustness and decision-making capabilities. This paper addresses the underexplored aspects of DDoS and phishing attacks, and precise decision-making at network edge devices within blockchain-based frameworks. The contribution lies in proposing an incentive-based security mechanism to divert intruders from genuine routes. Legitimate devices conducting accurate decision-making are rewarded, enticing their participation in identifying false devices. A honeypot intrusion detection system attracts false devices, and real-time trust computation monitors communication devices. This approach is analyzed under security threats and network delays, demonstrating its efficacy compared to existing methods in safeguarding edge computing environments. © 2025 John Wiley & Sons Ltd.

关键词： Network intrusion

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arXiv

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

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Predicting Continuity of Online Conversations on Reddit

SSRN

引用

SSRN 2022年

作者： Kim, Jinhyeon Han, Jinyoung Choi, Daejin Department of Electronic Engineering Incheon National University Incheon Korea Republic of Department of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Computer Science and Engineering Incheon National University Incheon Korea Republic of

Online conversation has been facilitating human society, which has encouraged people not only to share news, products, or daily events, but also to develop ideas. A popular type of online conversation is threaded conversation, where a person initiates a conversation with a new topic by uploading a post, then others reply to the post or the comments made by other participants in a recursive manner. Even though the growing importance of understanding and predicting the development process of threaded conversations has attracted research community to investigate continuity of online threaded conversations from a perspective of individual posts or comments, little work has focused on predicting the continuity or persistence of online conversations. In this paper, we propose a deep learning model to predict continuity of threaded conversations - whether there will be a newly-arrived comment or not. Cooperating with popular pre-trained text embedding models and graph neural network models, the proposed model captures text, structural, and temporal characteristics of the threaded conversation by an observable time for the final prediction. We evaluate the proposed model with two different types of the threaded conversations, self-introductory Q&A and discussion, which demonstrates that the proposed model can accurately predict the continuity of the conversations regardless of types of threaded conversations. We believe that the proposed methodology and the results can provide the potential insights to advertisers, opinion leaders, or platform designers who want to understand and predict the evolutionary process of threaded conversations. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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An Improved MangoNet Architecture Using Harris Hawks Optimization for Fruit Classification with Uncertainty Estimation

Journal of Uncertain Systems

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Journal of Uncertain Systems 2023年第1期16卷

作者： Rajalaxmi, R.R. Saradha, M. Fathima, S.K. Sathish Kumar, V.E. Sandeep Kumar, M. Prabhu, J. Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode 638060 India Department of Mathematics School of Applied Sciences Reva University Karnataka Bengaluru 560064 India Department of Computer Science and Engineering Sona College of Technology Salem Tamil Nadu 636005 India Department of Industrial Engineering Hanyang University Seoul South Korea School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

Agriculture sector plays a crucial role in the development of the economic system for developing countries. Useful insights extracted from agricultural data would lead to a better economy in the upcoming future. It is hard to manually look for the ripened fruits for harvesting in a farmland of a big area. Automated image-based methods to identify the presence of fruits on a tree and determine the number of fruits present on the tree is a challenging problem. MangoNet architecture performs well in detecting the mangoes invariant. The proposed work improves the efficiency of the model by optimizing the dropout rate of the MangoNet architecture. Harris Hawks Swarm Optimization (HHO) algorithm is developed to tune the dropout rate of the model. The dropout rate is optimized using HHO in the architecture to avoid overfitting problem. The proposed method is compared with particle swarm optimization (PSO) and BAT algorithm (BA) which underestimate uncertainty. The simulation results exhibit improved accuracy over 80% when compared to other existing methods considered in this study. We show theoretically that this gives us the ability to capture uncertainty better than existing methods. © 2023 World Scientific Publishing Company.

关键词： Deep learning dropout rate Harris Hawks optimization hyperparameter MangoNet uncertainty estimation

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Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device

Deep Learning Approach for Complex Activity Recognition usin...

引用

2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021

作者： Hnoohom, Narit Jitpattanakul, Anuchit You, Ilsun Mekruksavanich, Sakorn Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand Soonchunhyang University Department of Information Security Engineering Asan31538 Korea Republic of University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9781665403009

The classification of simple and complex sequences of operations is made easier according to the use of heterogeneous sensors from a wearable device. Sensor-based human activity recognition (HAR) is being used in smartphone platforms for elderly healthcare monitoring, fall detection, and inappropriate behavior prevention, such as smoking habit, unhealthy eating, and lack of exercise. Common machine learning and deep learning techniques have recently been presented to tackle the HAR issue, with a focus on everyday activities, particularly general human activities including moving, sitting, and standing. However, there is an intriguing and challenging HAR research subjects involving more complicated psychological activities in various environments, including smoking, eating, and drinking. The use of heterogeneous sensor data to enhance recognition performance over sensor-based deep learning networks is considered in this work. We demonstrate that using a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA). Furthermore, we describe the impact of five window sizes (5s - 40s) on a publicly accessible benchmark dataset and how increasing window size effects to the classification performance of CHA deep learning networks. © 2021 IEEE.

关键词： Deep learning

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A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion

arXiv

引用

arXiv 2022年

作者： Shankar, Ravi Hsieh, Hsi-Wei Charon, Nicolas Venkataraman, Archana The Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States The Department of Applied Math and Statistics Johns Hopkins University BaltimoreMD21218 United States

This paper introduces a new framework for nonparallel emotion conversion in speech. Our framework is based on two key contributions. First, we propose a stochastic version of the popular Cycle-GAN model. Our modified loss function introduces a Kullback–Leibler (KL) divergence term that aligns the source and target data distributions learned by the generators, thus overcoming the limitations of sample-wise generation. By using a variational approximation to this stochastic loss function, we show that our KL divergence term can be implemented via a paired density discriminator. We term this new architecture a variational Cycle-GAN (VCGAN). Second, we model the prosodic features of target emotion as a smooth and learnable deformation of the source prosodic features. This approach provides implicit regularization that offers key advantages in terms of better range alignment to unseen and out-of-distribution speakers. We conduct rigorous experiments and comparative studies to demonstrate that our proposed framework is fairly robust with high performance against several state-of-the-art baselines. © 2022, CC BY.

关键词： Stochastic systems

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gaith Rjoub Saidul Islam Jamal Bentahar Mohammed Amin Almaiah Rana Alrawashdeh Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi UAE King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decisionmaking efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： Intelligent automation Navigation Decision making Ecosystems Smart homes Reinforcement learning Transformers

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Simulation and Visualization of the electrical Activity of the Heart with focal ventricular tachycardia in a 3D Model

Simulation and Visualization of the electrical Activity of t...

引用

作者： Quester, Christian Heinke, Matthias Horth, Johannes Biomedical Engineering Department of University of Applied Sciences Offenburg Badstrasse 24 OffenburgD-77652 Germany Peter Osypka Institute of Medical Engineering Departement of Electrical Engineering Medical Engineering and Computer Science Badstrasse 24 OffenburgD-77652 Germany

Patients with focal ventricular tachycardia are at risk of hemodynamic failure and if no treatment is provided the mortality rate can exceed 30%. Therefore, medical professionals must be adequately trained in the management of these conditions. To achieve the best treatment, the origin of the abnormality should be known, as well as the course of the disease. This study provides an opportunity to visualize various focal ventricular tachycardias using the Offenburg heart rhythm model. Modeling and simulation of focal ventricular tachycardias in the Offenburg heart rhythm model was performed using CST (computer Simulation Technology) software from Dessault Systèms. A bundle of nerve tissue in different regions in the left and right ventricle was defined as the focus in the already existing heart rhythm model. This ultimately served as the origin of the focal excitation sites. For the simulations, the heart rhythm model was divided into a mesh consisting of 5354516 tetrahedra, which is required to calculate the electric field lines. The simulations in the Offenburg heart rhythm model were able to successfully represent the progression of focal ventricular tachycardia in the heart using measured electrical field lines. The simulation results were realized as an animated sequence of images running in real time at a frame rate of 20 frames per second. By changing the frame rate, these simulations can additionally be produced at different speeds. The Offenburg heart rhythm model allows visualization of focal ventricular arrhythmias using computer simulations. By selecting the frame rate, the speed of the simulation results can be adjusted accordingly to visualize the electric field lines of focal ventricular tachycardias in more detail. The static and dynamic simulation results could be used in the future for teaching and research, including the training of medical professionals. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Heart

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Automatic detection of defects in pre-stressed multi strand wires using hall effect sensor

Automatic detection of defects in pre-stressed multi strand ...

引用

2021 International Conference on Advances in Thermal engineering and Applications, ICATEA 2021

作者： Kumar, V Dinesh Mohamed, Jinnah Sheik Nallasivan, G. Ramalingam, P Sethu Karpagaraj, M. Rajesh Kanna, S.K. School of Automotive and Mechanical Engineering Kalasalingam University Krishnankoil Tamil Nadu India Department of Mechanical Engineering University of Technology and Applied Sciences Shinas Oman Department of Computer Science Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mechanical Engineering Rajalakshmi Institute of Technology Kuthambakkam Tamil Nadu Chennai India Department of Mechanical Engineering Thamirabharani Engineering College Tamil Nadu Tirunelveli India

Pre-stressed wire ropes are the steel-reinforced cables used in bridges to carry loads and to prevent the bridges from displacements. Steel strands are of different orientations and with arbitrary number of wires within it. This strands twisted one over the other to form the steel ropes, which plays a major role for supportive purpose and any defects in the strand wires leads to failure of the rope and the structures which it supports. So it became essential to inspect periodically, to ensure the safety operation. But, the challenges are that the ropes are at the nonaccessible and non-demandable locations, thereby the common inspection methods are limited. So in this research, inspection of wire ropes had done by using a robot with Hall Effect sensor. This developed robot has to be allowed to pass over the cable and the detector detects the defects, if any based on the flux variations at the broken steel strands. The experimentation results proved that the developed robot is at par in detection and it can be used to detect the defects for various applications. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Nondestructive examination

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Hybrid Optimization Framework for Energy Efficiency Maximization in NOMA-Aided Internet of Vehicles with beyond Diagonal RIS

引用

IEEE Internet of Things Journal 2025年

作者： Yi, Xiaoxi Wang, Han Zhang, Chi Song, Huiling Dutta, Ashit Kumar Yichun University College of Physical Science and Technology Jiangxi Yichun336000 China City University of Macau Faculty of Data Science Macao999078 China Yichun University College of Physical Science and Engineering Yichun336000 China Harbin University of Commerce College of Basic Science Heilongjiang Harbin150006 China AlMaarefa University Ad Diriyah College of Applied Sciences Department of Computer Science and Information Systems Riyadh13713 Saudi Arabia

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association-a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves

关键词： Reinforcement learning

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