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arXiv 2024年

作者： Pei, Yingjie Ni, Wanli Xu, Jin Yue, Xinwei Tao, Xiaofeng Niyato, Dusit School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS;ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity;and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobility and Behavior based Trustable Routing in Mobile Wire...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Praveen Kumar J. Umamageswaran A.V. Kalpana R. Dhanalakshmi Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Routing Throughput Behavioral sciences Delays

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Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing

arXiv

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arXiv 2024年

作者： Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan The Department of Electronic Systems Norwegian University of Science and Technology Trondheim7491 Norway Department of Information and Communications Engineering Aalto University 00076 Finland The Commonwealth Scientific and Industrial Research Organisation PullenvaleQLD4069 Australia The SDU Applied AI and Data Science The Maersk Mc-Kinney Moller Institute Faculty of Engineering University of Southern Denmark Odense5230 Denmark

Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks. PSO-Fed reduces communication overhead by allowing clients to share only a fraction of their model updates with the server. We demonstrate that this partial sharing mechanism has the added advantage of enhancing PSO-Fed’s robustness to model-poisoning attacks. Through theoretical analysis, we show that PSO-Fed maintains convergence even under Byzantine attacks, where malicious clients inject noise into their updates. Furthermore, we derive a formula for PSO-Fed’s mean square error, considering factors like stepsize, attack probability, and the number of malicious clients. Interestingly, we find a non-trivial optimal stepsize that maximizes PSO-Fed’s resistance to these attacks. Extensive numerical experiments confirm our theoretical findings and showcase PSO-Fed’s superior performance against model-poisoning attacks compared to other leading FL algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Remote Patient Monitoring systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： Halimjon Khujamatov Nurshod Akhmedov Amir Lazarev Sobiya Arsheen Dean of the faculty of Computer Engineering Tashkent University of Information Technologies Tashkent Uzbekistan Data transmission networks and systems department Tashkent University of Information Technologies Tashkent Uzbekistan Department of Computer Science and Information Technology Maulana Azad National Urdu University India

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture.

关键词： Phase change materials Adaptation models Patient monitoring Network architecture Ad hoc networks Topology Load modeling

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 dataset

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Computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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A Conditional Perspective on the Logic of Iterated Belief Contraction

arXiv

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arXiv 2022年

作者： Sauerwald, Kai Kern-Isberner, Gabriele Beierle, Christoph FernUniversität in Hagen Faculty of Mathematics and Computer Science Knowledge-Based Systems Hagen58084 Germany TU Dortmund University Department of Computer Science Information Engineering Dortmund44221 Germany

Classical AGM belief change theory provides only limited information on how to deal with iterative belief revision, leading to the question of how appropriate iteration principles look alike. The contributions to the interconnection between iterative belief revision and conditional beliefs lead to fundamental insights on that matter. Besides of clarifying to what amount iterated revision should respect conditional beliefs, these investigations identified several principles for iterated revision that are today widely accepted by the belief change community and beyond. Specifically, due to the seminal work by Darwiche and Pearl, it is standard, that principles for iterated revision and change of conditional beliefs are considered as two sides of the same coin. However, for iterated contraction the situation is different, there are only a small amount of iteration principles present in the literature. Moreover, for none of these iterated principles for contraction, there is a known equivalent formulation in terms of change of conditional beliefs. In this article, we consider iteration principles for contraction, with the goal of identifying properties for contractions that respect conditional beliefs. Therefore, we investigate and evaluate four groups of iteration principles for contraction which consider the dynamics of conditional beliefs. For all these principles, we provide semantic characterization theorems and provide formulations by postulates which highlight how the change of beliefs and of conditional beliefs is constrained, whenever that is possible. The first group is similar to the syntactic Darwiche-Pearl postulates. As a second group, we consider semantic postulates for iteration of contraction by Chopra, Ghose, Meyer and Wong, and by Konieczny and Pino Pérez, respectively, and we provide novel syntactic counterparts. Third, we propose a contraction analogue of the independence condition by Jin and Thielscher. For the fourth group we consider natural and

关键词： Iterative methods

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Deep Learning Based Intrusion Detection System with Integration of Blockchain Technology and Internet of Things

Deep Learning Based Intrusion Detection System with Integrat...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： G. Uganya D. Rajalakshmi K. Ashok Kumar S. Ravi Neeraj Sunheriya Mohammad Kanan Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Information Technology St Joseph's college of Engineering OMR Chennai Department of VLSI Microeelctronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The number of people and objects linked to the Internet of Things (IoT) has expanded dramatically due to its fast development, giving consumers more opportunities to interact with and manage their physical surroundings. IoT is susceptible to a number of security risks, because of its openness, connectedness, and constrained device resources. It is essential to guarantee the dependability and security of data transmitted in IoT networks. In order to solve these security problems, this work provides intrusion detection system that combines IoT with and Blockchain technology. As well as, deep learning method is integrated to improve the security. This work uses the SHA-256 algorithm to secure the data, the Adaptive Optimization algorithm for generating the keys, Rivest, Shamir, and Adleman (RSA) algorithm for encryption. and Deep Neural Network (DNN) for identifying intrusions in Internet of Things. Through identification and mitigation of possible risks, this framework successfully protects IoT networks and ensures the integrity of communications, as demonstrated by a comparison analysis with existing models.

关键词： Deep learning Sensitivity Adaptive systems Accuracy Intrusion detection Artificial neural networks Blockchains Internet of Things Security Optimization

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Advanced Malware Classification Using Convolutional Neural Networks and Image Transformation Techniques

Advanced Malware Classification Using Convolutional Neural N...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Shweta Bhattacharjee Porna Md Zakir Hossain Abdullah Al Naseeh Chowdhury M Govind Raj Roise Uddin Mukit Al Elahi Abdul Basit Mohammad Aman Ullah Khan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Data Science Grand Canyon University USA Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Department of ECE Malla Reddy Engineering College Maisammaguda Telangana India Computer and Information Systems Security Cyber California State University San Bernardino USA Department of CSE Pacific states university Los Angeles California USA Department of CSE Pacific states university 4651 Rosewood St USA Department of CSE School of Data and Sciences BRAC University Dhaka Bangladesh School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Malware seriously compromises cybersecurity and calls for sophisticated and effective categorization methods to find and lessen its effects. This research article offers a complete method based on image processing and deep learning to classify malware. ResNet18, VGG16, VGG19, AlexNet, and our custom-designed MalwareNet are among the several deep learning models we use to categorize malware photos from the nine different classes in the Big 2015 dataset-which shows a clear class imbalance. Using hyperband and bayesian optimization approaches, our strategy maximizes hyperparameters to provide the greatest performance. The hyperparameters for ResNet18, VGG16, VGG19, and AlexNet models comprise learning rate, kernel size, number of epochs, dropout rate, and dense layer size as well as particular parameters for MalwareNet including the number of neurons in each layer and the fully connected layer size. With MalwareNet obtaining especially remarkable results, the examination of our models shows great accuracy, precision, recall, and F1 scores. We examine the training accuracy, F1 score, and log loss measures in great detail; we then show the classification performance over all classes using confusion matrices. With notable increases in malware detection and categorization, the suggested model offers a strong and practical answer for uses in cybersecurity. Supported by diligent hyperparameter adjustment and performance evaluation, this work expands the field by providing a comprehensive comparison of many deep learning architectures and how effectively they classify malware. The results show how well deep learning models might improve malware detection systems and protect digital environments against hostile attacks.

关键词： Deep learning Training Performance evaluation Accuracy Neurons Malware Loss measurement Computer security Kernel Optimization

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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