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QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-Star-RIS

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Yeganeh, Rahman Saadat Omidi, Mohammad Javad Zeinali, Farshad Mili, Mohammad Robat Ghavami, Mohammad Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran Kuwait College of Science and Technology Department of Electronics and Communication Engineering Doha35003 Kuwait Tehran Iran London South Bank University Electrical and Electronic Engineering Department LondonSE1 0AA United Kingdom

In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (SUEs), respectively. Since the SBDs are passive, transmitting information to the SUEs poses significant challenges. To overcome this challenge, we harness the capabilities of massive multiple input multiple output (MIMO) antennas within the base station (BS) to relay the information transmitted by SBDs with greater power. This scheme uses the non-orthogonal multiple access (NOMA) technique for multiple access among all users, and potential interferences are eliminated using successive interference cancellation (SIC). The primary objective is to maximize the throughput between SBDs and SUEs. To achieve this, we formulate an optimization problem involving variables such as active beamforming coefficients at the BS and ASRIS, phase adjustments of ASRIS, and scheduling parameters between CSR and cellular networks. To solve this optimization problem, we used three deep reinforcement learning (DRL) methods: proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and asynchronous advantage actor critic (A3C). These methods were simulated, and the results demonstrate that A3C, TD3, and PPO have the best convergence speeds and achieve the highest increases in network throughput, respectively. Finally, the proposed scheme was evaluated using passive simultaneously transmitting and reflecting RIS (STAR-RIS), which demonstrated poorer performance compared to ASRIS. © 2025 IEEE.

关键词： Deep learning

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Exploiting Machine Learning in Multiscale Modelling of Materials

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Journal of The Institution of Engineers (India): Series D 2023年第2期104卷 867-877页

作者： Anand, G. Ghosh, Swarnava Zhang, Liwei Anupam, Angesh Freeman, Colin L. Ortner, Christoph Eisenbach, Markus Kermode, James R. Department of Metallurgy and Materials Engineering Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India National Centre for Computational Sciences Oak Ridge National Laboratory 1 Bethel Valley Rd Oak RidgeTN37831 United States Department of Mathematics University of British Columbia 1984 Mathematics Rd VancouverBCV6T1Z2 Canada Department of Computer Science Cardiff Metropolitan University Llandaff Campus CardiffCF5 2YB United Kingdom Department of Materials Science and Engineering University of Sheffield Mappin St SheffieldS1 3JD United Kingdom Warwick Centre for Predictive Modelling School of Engineering University of Warwick WarwickCV4 7AL United Kingdom

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed. © 2022, The Institution of Engineers (India).

关键词： Statistical mechanics

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities 3

Authentication using Biometric Data from Mobile Cloud Comput...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Near East University Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near Artificial Intelligence Engineering Dept Near East University Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them. © 2023 IEEE.

关键词： Mobile cloud computing

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Towards All-optical Cavity-based Memory for Non-Gaussian Quantum State engineering

Towards All-optical Cavity-based Memory for Non-Gaussian Qua...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Hanamura, Fumiya Takase, Kan Hirota, Kazuki Nehra, Rajveer Lang, Florian Miki, Shigehito Terai, Hirotaka Yabuno, Masahiro China, Fumihiro Kahiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Asavanant, Warit Endo, Mamoru Yoshikawa, Jun-Ichi Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Information Technology and Electrical Engineering ETH Zurich Zurich8092 Switzerland Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Hyogo Kobe657-0013 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan

We present our experimental results on all-optical memory to store and manipulate quantum states, and propose resource-efficient time-domain-multiplexed state engineering using the memory. This paves a path to scalabl... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical recording

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Editorial for the Special Issue on 6G Requirements,Vision,and Enabling Technologies

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engineering 2022年第1期8卷 1-2页

作者： Ping Zhang Xuemin(Sherman)Shen Jianhua Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

As the commercialization process of the fifth-generation(5G)communication systems accelerates,research on the sixth genera-tion(6G)is being placed on the agenda in academic and industrial communities all over the world.6G systems are expected to fur-ther enhance the performance of 5G systems and continue to pen-etrate into all aspects of society,promoting the technological integration of communications with other disciplines,such as arti-ficial intelligence,materials science,and ***,a spe-cial issue on the development of the future 6G is both timely and *** special issue contains eight papers detailing recent cutting-edge research achievements from the perspectives of 6G requirements,visions,and enabling technologies.

关键词： integration intelligence fifth

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Energy-Aware Sensor Scheduling and Opportunistic Transmission for State Estimation in Industrial IoT Systems

Energy-Aware Sensor Scheduling and Opportunistic Transmissio...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Qiao, Zexin Lyu, Ling Dai, Yanpeng Cheng, Nan Shen, Xuemin Dalian Maritime University School of Information Science and Technology Dalian116026 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350345384

The wide application of Internet of Things (IoT) technology in industrial automation promotes the emergence of industrial IoT systems, where state estimation plays a critical role in accurately tracking the state of control system and enabling informed decisions and appropriate actions. State estimation is generally performed with the sensory data delivered over IoT. Therefore, the transmission performance of sensory data plays a crucial role in the accuracy of state estimation. However, it is challenging to ensure the required transmission reliability for state estimation due to spectrum scarcity, energy limitation, and the harsh industrial radio environment. To address this issue, we propose an energy-aware sensor scheduling and opportunistic transmission scheme to balance the estimation performance and resource consumption. In particular, we define a novel performance metric, estimation error ratio, to evaluate the impact of sensor scheduling on estimation performance. Then, a spectrum and energy resources constrained minimization problem is formulated to reduce the weighted sum of estimation error and energy consumption. In order to effectively solve the non-convex problem, an estimation error ratio-based algorithm is designed to determine the scheduled sensors. Moreover, we propose an iterative method to obtain the near-optimal solution for the opportunistic transmission problem. Finally, simulation results demonstrate that the proposed scheme can effectively reduce energy consumption with the estimation accuracy guarantee. © 2023 IEEE.

关键词： Energy utilization

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hua, Xiaoqi Chen, Jian Zhang, Peiyun Fu, Zhangjie Zhu, Haibin Lu, Kezhong Ren, Jigang School of Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education The School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China

ISBN: (纸本)9781665410205

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio. © 2024 IEEE.

关键词： Blockchain

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A Double Protecting Approach for Data Privacy 10

A Double Protecting Approach for Data Privacy

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Almutairi, Mishaal M. Apostolopoulou, Dimitra Stupples, David Sen, Adnan Ahmed Abi Yamin, Mohammad Halikias, George School of Mathematics Computer Science and Engineering Department of Electrical and Electronic Engineering LondonEC1V 0HB United Kingdom Deanship of Information Technology Islamic University Madinah Saudi Arabia School of Computing The Australian National University Canberra Australia

ISBN: (纸本)9789380544472

Hackers have become more vigilant and malicious. Thus, cases of direct threats and blackmail, which lead to hacking and theft of users' data, have increased over the years. As such, it is necessary to build effective privacy protection methods and techniques that can guarantee users' data privacy even in the presence of a malicious attacker or an unreliable service provider. In this view, this study proposes a double approach for protecting users' data in a crowd. The study uses a systematic literature review method to identify technologies that have been advanced to ensure data privacy. These technologies include encryption, data mining techniques, obfuscation, dummies, and TTP. Unfortunately, all these methods have significant weaknesses that hinder them from effectively achieving data security and protection. As such, the current study proposes a new approach to privacy that can enhance the effectiveness of existing methods without impacting their performance. This approach also supports complex systems such as those used in crowd management and a wide variety of applications within these systems. Ideally, the double-approach method proposed in this study is a collection of various technologies that rely on two primary conditions: doubling the work of traditional data protection methods and using fog computing to enhance the efficiency and overall performance of these methods. This way, the new approach will effectively serve different complex systems and applications used in crowd management. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Personal computing

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Improving the learning performance by exploiting multimedia in eXtreme apprenticeship

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Multimedia Tools and Applications 2024年 1-25页

作者： Persia, Fabio D’Auria, Daniela Ge, Mouzhi Pilato, Giovanni Department of Information Engineering Computer Science and Mathematics University of L’Aquila Via Vetoio L’Aquila67100 Italy Faculty of Computer Science Free University of Bozen-Bolzano Piazza Domenicani 3 Bozen-Bolzano39100 Italy Faculty of European Campus Rottal-Inn Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany Institute for High Performance Computing and Networking National Research Council of Italy Via Ugo La Malfa 153 Palermo90146 Italy

From prior studies on eXtreme Apprenticeship (XA), it can be seen that XA has emerged as an innovative and effective educational approach. The technology in computer science evolves rapidly and XA tackles the gap between University education and industry development. Additionally, distance learning by multimedia is more and more important, sometimes even becoming necessary. As a result, this paper intends to study how to integrate XA and multimedia to improve the learning performance in computer science, with particular reference to the programming courses. Since computer science is mostly driven by the new applications, XA and multimedia are considered to be effective and efficient to reduce the cognitive efforts by practical demonstration. The experimental results, based on the case study of the operating systems course at the Bachelor’s level, show that exploiting multimedia in XA can significantly improve the learning performance in terms of grade distributions, reliability, knowledge transfer and student satisfaction with respect to more traditional approaches based on frontal lectures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distance education

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Security and Privacy Concerns in 'Communication Systems' Blockchain technology and the Internet of Things 3

Security and Privacy Concerns in 'Communication Systems' Blo...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Prakash, R.Om Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Noida Instue Engineering and Technology U.P Greator Noida201306 India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

This article examines the security and privacy concerns associated with the integration of blockchain technology and the Internet of Things (IoT) in communication systems. As the IoT continues to grow and evolve, there is an increasing need to ensure the security and privacy of the vast amounts of data generated and exchanged between connected devices. Blockchain technology offers promising solutions for enhancing security and privacy in communication systems;however, it also presents its own set of challenges and vulnerabilities. This article explores the existing literature on the topic and analyzes the materials and methods used in previous studies. The results and discussions delve into the key security and privacy concerns in communication systems with blockchain and IoT, including data integrity, authentication, and confidentiality, scalability, and consensus mechanisms. Finally, the article concludes with recommendations and future directions for addressing these concerns and advancing the security and privacy of communication systems in the context of blockchain technology and the IoT. © 2023 IEEE.

关键词： Internet of things

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