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arXiv 2022年

作者： Yang, Tianyi Li, Baitong Shen, Jiacheng Su, Yuxin Yang, Yongqiang Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-Sen Univeristy Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Shenzhen China

Interactions between cloud services result in service dependencies. Evaluating and managing the cascading impacts caused by service dependencies is critical to the reliability of cloud systems. This paper summarizes the dependency types in cloud systems and demonstrates the design of the Dependency Management System (DMS), a platform for managing the service dependencies in the production cloud system. DMS features the full-lifecycle support for service reliability (i.e., initial service deployment, service upgrade, proactive architectural optimization, and reactive failure mitigation) and refined characterization of the intensity of dependencies. © 2022, CC BY.

关键词： Software reliability

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Mixture of Experts-Enabled Parallel Scheduling and Processing for Vehicular Generative AI Services

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Xie, Gaochang Xiong, Zehui Xie, Renchao Deng, Xiumei Guo, Song Guizani, Mohsen Han, Zhu Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology Beijing100876 China Purple Mountain Laboratories Nanjing211111 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Mohammad Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Foundation models (FMs) have revolutionized generative AI (GAI) lifecycle with their pre-trained intelligence capabilities. While the recent success of web-based models like GPT-4 has spurred interest in extending FMs to edge scenarios like the Internet of Vehicles (IoV), challenges such as data privacy, network congestion, and limited edge resource awareness hinder the direct application of cloud-based FMs. To address these challenges, this paper reconfigures the mixture of experts (MoE) architecture to deploy Transformer-based FM experts across vehicles for distributed GAI inference. Specifically, we propose an MoE-empowered vehicular FM system with two key innovations: a physical gating network that dynamically adapts to wireless environments, and a vehicle-to-vehicle (V2V) communication-based expert parallelism mechanism to enhance efficiency and resource utilization. Then, we formulate the communication, computation, and memory models for analyzing inference latency. Furthermore, to optimize the computational resource allocation for enhancing inference performance, we establish a Stackelberg game and propose the Gradient Ascent and Evolutionary Optimization-based Competitive Pricing and Allocation (GECPA) algorithm, which balances resource allocation and usage costs by combining the rapid convergence of gradient ascent with the broader exploration capability of evolutionary optimization. Simulation results demonstrate the superior parallel processing efficiency and reduced latency of the proposed MoE-based FM inference scheme. Compared with the best-performing benchmark algorithm, GECPA improves the average utility of infrastructure vehicles by up to 2.01 times, reduces inference latency by 16.58%, and increases the successful execution rate of GAI tasks by 10.66%, thereby achieving a better balance between efficiency and incentive compatibility in dynamic IoV environments. © 2015 IEEE.

关键词： Resource allocation

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Network Time Connectivity for Wireless Networks

Network Time Connectivity for Wireless Networks

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2020 International Conference on Computing, networking and Communications, ICNC 2020

作者： Zhou, Baofeng Vuran, Mehmet C. Cyber-Physical Networking Laboratory Department of Computer Science and Engineering University of Nebraska-Lincoln LincolnNE68588 United States

ISBN: (纸本)9781728149059

In this paper, a novel concept of time connectivity is devised to evaluate the quality of network-level synchronization. The time connectivity is defined analogously to communication connectivity. To facilitate time connectivity analysis for wireless networks, a probabilistic synchronization model is developed for pre-synchronization and post-synchronization scenarios to evaluate synchronization performance. By utilizing recent results on stochastic geometry and percolation theory, time connectivity is transformed into the two-dimensional space for network-level analysis. Notably, the network time connectivity is investigated in two network schemes: partial-coverage and out-of-coverage. Extensive simulations are conducted to evaluate the time connectivity framework. To the best of our knowledge, this is the first paper that incorporates stochastic geometry and percolation theory to study the performance of network-level synchronization. © 2020 IEEE.

关键词： Synchronization

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection

arXiv

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arXiv 2024年

作者： Chien, Chun-Tse Ju, Rui-Yang Chou, Kuang-Yi Xieerke, Enkaer Chiang, Jen-Shiun Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan School of Nursing National Taipei University of Nursing and Health Sciences Taipei City112303 Taiwan College of Energy and Mechanical Engineering Shanghai University of Electric Power Shanghai201306 China

Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for it based on the analysis of the radiologist. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection as computer-assisted diagnosis (CAD). In 2023, Ultralytics presented the latest version of the YOLO models, which has been employed for detecting fractures across various parts of the body. Attention mechanism is one of the hottest methods to improve the model performance. This research work proposes YOLOv8-AM, which incorporates the attention mechanism into the original YOLOv8 architecture. Specifically, we respectively employ four attention modules, Convolutional Block Attention Module (CBAM), Global Attention Mechanism (GAM), Efficient Channel Attention (ECA), and Shuffle Attention (SA), to design the improved models and train them on GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean Average Precision at IoU 50 (mAP 50) of the YOLOv8-AM model based on ResBlock + CBAM (ResCBAM) increased from 63.6% to 65.8%, which achieves the state-of-the-art (SOTA) performance. Conversely, YOLOv8-AM model incorporating GAM obtains the mAP 50 value of 64.2%, which is not a satisfactory enhancement. Therefore, we combine ResBlock and GAM, introducing ResGAM to design another new YOLOv8-AM model, whose mAP 50 value is increased to 65.0%. The implementation code for this study is available on GitHub at https://***/RuiyangJu/Fracture_Detection_Improved_YOLOv8. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical image processing

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Cloud-Powered Patient Interaction: Humanoid Robots in Hospital Reception using NLP

Cloud-Powered Patient Interaction: Humanoid Robots in Hospit...

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International Conference on Communications and Signal Processing

作者： N. Naveenkumar S. Priyadarshini R. Swathy Nilamadhab Mishra P. Thirumaraiselvan S. Renukadevi Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of English Vinayaka Mission’s Kirupananda Variyar Arts & Science College Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India Department of Mathematics Bharathi Women’s College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This research presents a novel method for improving patient engagement in hospital reception areas by using cloud computing and natural language processing (NLP) approaches to power humanoid robots. In order to improve the patient experience overall, the greeting procedure is being streamlined and personalized via the deployment of humanoid robots. To facilitate an effective connection between the hospital’s computer systems and the humanoid robots, cloud computing enables the processing and analysis of data in real-time. The ability to comprehend and react to patient questions, provide pertinent information, and aid with navigation inside the hospital grounds are all made possible by the robots’ natural language processing skills. Robots may access and update patient data using cloud resources, proving accurate and up-to-date information transmission. And since the system is cloud-based, it can learn and adapt on the go, so the robots may improve their communication abilities in response to human input. Results in decreasing wait times, improving information accuracy, and increasing overall patient happiness have been encouraging since the deployment of this cloud-powered patient contact system. To improve healthcare administration and patient care by integrating advanced technology into healthcare environments. Adding new features and improving the system in response to input from users (both patients and doctors) are potential directions for future growth.

关键词： Cloud computing Hospitals Navigation Humanoid robots Information processing Signal processing Natural language processing

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

关键词：

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Federated Contrastive Learning for Personalized Semantic Communication

arXiv

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arXiv 2024年

作者： Wang, Yining Ni, Wanli Yi, Wenqiang Xu, Xiaodong Zhang, Ping Nallanathan, Arumugam The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Peng Cheng Laboratory Shenzhen518066 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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Multi-Functional RIS Integrated Sensing and Communications for 6G Networks

arXiv

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arXiv 2024年

作者： Han, Dongsheng Wang, Peng Ni, Wanli Wang, Wen Zheng, Ailing Niyato, Dusit Al-Dhahir, Naofal The School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Baoding071003 China The Department of Electronic Engineering Tsinghua University Beijing100084 China The Pervasive Communications Center Purple Mountain Laboratories Nanjing211111 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The Department of Electrical and Computer Engineering The University of Texas Dallas United States

In this paper, we propose a novel multi-functional reconfigurable intelligent surface (MF-RIS) that supports signal reflection, refraction, amplification, and target sensing simultaneously. Our MF-RIS aims to enhance integrated communication and sensing (ISAC) systems, particularly in multi-user and multi-target scenarios. Equipped with reflection and refraction components (i.e., amplifiers and phase shifters), MF-RIS is able to adjust the amplitude and phase shift of both communication and sensing signals on demand. Additionally, with the assistance of sensing elements, MF-RIS is capable of capturing the echo signals from multiple targets, thereby mitigating the signal attenuation typically associated with multi-hop links. We propose a MF-RIS-enabled multi-user and multi-target ISAC system, and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of sensing targets. This problem involves jointly optimizing the transmit beamforming and MF-RIS configurations, subject to constraints on the communication rate, total power budget, and MF-RIS coefficients. We decompose the formulated non-convex problem into three sub-problems, and then solve them via an efficient iterative algorithm. Simulation results demonstrate that: 1) The performance of MF-RIS varies under different operating protocols, and energy splitting (ES) exhibits the best performance in the considered MF-RIS-enabled multi-user multi-target ISAC system;2) Under the same total power budget, the proposed MF-RIS with ES protocol attains 52.2%, 73.5%, and 60.86% sensing SINR gains over active RIS, passive RIS, and simultaneously transmitting and reflecting RIS (STAR-RIS), respectively;3) The number of sensing elements will no longer improve sensing performance after exceeding a certain number. © 2024, CC BY.

关键词： Budget control

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