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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

Journal of Communications and information Networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights

arXiv

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arXiv 2024年

作者： Heidari, Moein Azad, Reza Kolahi, Sina Ghorbani Arimond, René Niggemeier, Leon Sulaiman, Alaa Bozorgpour, Afshin Aghdam, Ehsan Khodapanah Kazerouni, Amirhossein Hacihaliloglu, Ilker Merhof, Dorit School of Biomedical Engineering University of British Columbia BC Canada Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Information Science and Technology Universiti Kebangsaan Bangi Malaysia Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Electrical Engineering Shahid Beheshti University Tehran Iran Department of Computer Science University of Toronto Toronto Canada Department of Radiology Department of Medicine University of British Columbia BC Canada Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks. Building upon this paradigm, Vision Transformer (ViT) networks exploit attention mechanisms for improved efficiency. This review navigates the landscape of redesigned attention mechanisms within ViTs, aiming to enhance their performance. This paper provides a comprehensive exploration of techniques and insights for designing attention mechanisms, systematically reviewing recent literature in the field of CV. This survey begins with an introduction to the theoretical foundations and fundamental concepts underlying attention mechanisms. We then present a systematic taxonomy of various attention mechanisms within ViTs, employing redesigned approaches. Amulti-perspective categorization is proposed based on their application, objectives, and the type of attention applied. The analysis includes an exploration of the novelty, strengths, weaknesses, and an in-depth evaluation of the different proposed strategies. This culminates in the development of taxonomies that highlight key properties and contributions. Finally, we gather the reviewed studies along with their available open-source implementations at our *** aim to regularly update it with the most recent relevant papers. © 2024, CC BY-NC-SA.

关键词： Computer vision

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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

arXiv

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arXiv 2025年

作者： Wang, Shuai Xu, Yanqing You, Chaoqun Shao, Mingjie Quek, Tony Q.S. National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Research Institute of Big Data School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China School of Computer Science and Technology Fudan University 200433 China Beijing100149 China Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Deformation And Penetration Hybrid Detection-Net For Parcels Inspection In Industrial Supply Chain

Deformation And Penetration Hybrid Detection-Net For Parcels...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhi Chen Cuifeng Du Xiujie Huang Zelong Lin Yuyu Zhou Quanlong Guan Zhefu Li Shuanghuan Lv Xiaofeng Wu Xiaotian Zhuang College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Cetc Potevio Science&Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Guangzhou Polytechnic of Sports Guangzhou China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

The express delivery industry has become integral to modern social life, but supply chain parcels, especially those made of corrugated cardboard, are at risk of damage during transportation. Although corrugated cardboard boxes offer some impact resistance, they can still experience deformation and penetration damage. To address this issue, we propose a hybrid model called Parcels-DNet. Parcels-DNet adopts a lightweight feature extraction backbone network, enabling deployment in resource-constrained scenarios like mobile or embedded devices. Additionally, our experimental results demonstrate that Parcels-DNet effectively captures the features of parcel deformation and penetration damage. This improves the safety and efficiency of express supply chain parcel transportation, offering greater convenience and economic benefits for the logistics industry.

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning

Efficient Drinking Water Quality Analysis using Machine Lear...

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International Conference on ent Computing and Control systems (ICICCS)

作者： M Uma Maheswari R. Sudharsanan M Arthy Anne Jenefer L Oormila V Samuthira Pandi Department of Electrical and Electronics Engineering RMD Engineering College Tamil Nadu India Department of Information Technology SRM Institute of Science and Technology Ramapuram Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Computer Science Engineering Agni College of Technology Tamil Nadu India Department of Electronics ad Communication Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu India

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing; hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT’s precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential.

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Machine Learning Approach for Mitigating Security Threats in IoT Environment

Machine Learning Approach for Mitigating Security Threats in...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Harnit Saini Saroj Bala S.Jhansi Ida K. Suresh Kumar Shankerpally Swetha Divyabarathi P Department of CSE Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department of CSE Kongu Engineering College Erode Department of Information Technology CVR College of Engineering Telangana Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated.

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Cyber security-based Multikey Management System in Cloud Environment

Cyber security-based Multikey Management System in Cloud Env...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： M. Jeyaselvi C Jayakumar M. Sathya S Jafar Ali Ibrahim N.S. Kalyan Chakravarthy SRM Institute of Science & Technology Kattankulathur Department of Computer Science and Engineering Sri Venkateshwara College of Engineering Sriperumbudur Dept of Information science and Engineering AMC Engineering College Bengaluru Karnataka Dept of Internet of Things Vellore Institute of Technology Vellore TN Director - Data Science and Business Systems QIS College of Engineering and Technology Ongole AP

ISBN: (纸本)9781665491075

The revolutionary expansion in processing and storing mechanisms through the internet has given rise to affordable and powerful computing features. These qualities have been enhanced as a result of this growth. Computing in the cloud is an emerging technology that provides users with the ability to access data storage facilities as well as application access facilities in an online setting. This approach presents an almost infinite number of chances and difficulties. In light of this, ensuring the safety of one’s data and preventing the accumulation of duplicate or otherwise identical information in the cloud are both highly critical concerns that must be handled. As a result, a method known as deduplication was created to cut down on the amount of data in the identical storage system. In this investigation, a unique approach for removing repetitive or duplicate data from cloud servers, which will also help reduce the amount of internet access and storage space needed, has been presented. The results of the experiments show that the suggested system not only offers a higher level of protection for the data that is stored in stored in the cloud but also addresses the primary issues of rent systems.

关键词： Resistance Cloud computing data privacy data security Memory data compression Safety

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Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats

arXiv

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arXiv 2024年

作者： Pavlich, Ryan Ebadi, Nima Tarbell, Richard Linares, Billy Tan, Adrian Humphreys, Rachael Das, Jayanta Kumar Ghandiparsi, Rambod Haley, Hannah George, Jerris Slavin, Rocky Choo, Kim-Kwang Raymond Dietrich, Glenn Rios, Anthony Data Analytics The University of Texas San Antonio United States Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Computer Science The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States

Recognizing the promise of natural language interfaces to databases, prior studies have emphasized the development of text-to-SQL systems. While substantial progress has been made in this field, existing research has concentrated on generating SQL statements from text queries. The broader challenge, however, lies in inferring new information about the returned data. Our research makes two major contributions to address this gap. First, we introduce a novel Internet-of-Things (IoT) text-to-SQL dataset comprising 10,985 text-SQL pairs and 239,398 rows of network traffic activity. The dataset contains additional query types limited in prior text-to-SQL datasets, notably temporal-related queries. Our dataset is sourced from a smart building's IoT ecosystem exploring sensor read and network traffic data. Second, our dataset allows two-stage processing, where the returned data (network traffic) from a generated SQL can be categorized as malicious or not. Our results show that joint training to query and infer information about the data can improve overall text-to-SQL performance, nearly matching substantially larger models. We also show that current large language models (e.g., GPT3.5) struggle to infer new information about returned data, thus our dataset provides a novel test bed for integrating complex domain-specific reasoning into LLMs. © 2024, CC BY.

关键词： Internet of things

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