检索结果-内蒙古大学图书馆

Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

IEEE Transactions on Cognitive Communications and networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

引用

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore 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

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

E-commerce Scheme Based on Proxy t-out-of-n Oblivious Signature

引用

International Journal of Network Security 2024年第5期26卷 851-860页

作者： Chen, Jingyu Gong, Linming Ma, Xiangxiang Wang, Daoshun The Shaanxi Key Laboratory of Clothing Intelligence School of Computer Science Xi’an Polytechinic University Xi’an710048 China State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services School of Computer Science Xi’an Polytechnic University Shaanxi Xi’an710048 China Department of Computer Science and Technology Tsinghua University Beijing100084 China No. 58 Shan-gu Road Lintong Xi’an710600 China

With the development of the internet and the demand for online shopping, intelligent online shopping systems have received widespread attention in recent years. However, existing online shopping systems’ security and privacy issues have not been well addressed. This article proposes a shopping scheme based on proxy t-out-of-n oblivious signatures, which uses t-out-of-n oblivious signature technology to ensure that users select multiple messages from a predetermined set of messages, ensuring the legality and privacy of the selection while using proxy signature technology to provide the attribute of signature unforgeability. We have analyzed the correctness of the proposed scheme and demonstrated its security in terms of integrity, unforgeability, confidentiality of selected messages, selection restriction, non-repetitive, verifiability, distinguishability, non-repudiation, and prevention of abuse. Our scheme has lower computational and communication costs than other oblivious signature schemes. In addition, the privacy-preserving multi-choice shopping scheme we propose enables users to shop more securely and conveniently. © (2024), (International Journal of Network Security). All rights reserved.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

引用

Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

来源：评论

学校读者我要写书评

暂无评论

Spatial Feature Topology-Based Heterogeneous Knowledge Transfer Framework for Long-Term Fingerprint Positioning

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Si, Haonan Guo, Xiansheng Boateng, Gordon Owusu Yang, Yin Ansari, Nirwan University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China New Jersey Institute of Technology Advanced Networking Lab. Department of Electrical and Computer Engineering NewarkNJ07102 United States

Transfer learning (TL) is effective for addressing distribution discrepancies in fingerprint positioning. However, existing TL frameworks cannot react well to the heterogeneous feature dimensions of fingerprints caused by the topology variations of base stations (BSs) in evolving long-term environments. To address this issue, we propose a spatial feature topology-based heterogeneous knowledge transfer framework tailored for long-term fingerprint positioning (SFTP). Firstly, we partition the heterogeneous features into common and domain-specific (including source and target-specific) features from static and dynamic environmental components perspectives, respectively. Notably, we observe that the features captured from BSs with significant spatial distances differ across all samples, while those from BSs with close spatial distances show higher similarity. Based on these observations, we approximate the cross-domain mapping for each domain-specific feature by integrating the mappings of similar common features, which are easy to achieve using deep neural networks (DNNs). Subsequently, the heterogeneous feature spaces are effectively transformed into homogeneous counterparts, and a deep adaptation network (DAN) is utilized to further predict the positions for testing samples. Hence, SFTP is capable of capturing evolutionary environmental information for long-term positioning. Finally, real-world experimental results demonstrate the superiority and robustness of the proposed framework. © 1967-2012 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

引用

Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

来源：评论

学校读者我要写书评

暂无评论

OSM2Net: A Robust Road Network Extraction Framework from Noisy Indoor Parking OpenStreetMap

引用

IEEE Internet of Things Journal 2025年

作者： Cao, Yu Guo, Xiansheng Boateng, Gordon Owusu Ansari, Nirwan Si, Haonan Qian, Bocheng Liu, Xinhao Xia, Huang Liu, Yinong University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China New Jersey Institute of Technology Advanced Networking Lab Department of Electrical and Computer Engineering NewarkNJ07102 United States

Intelligent Transportation Systems (ITS) rely on high-precision road networks, which are particularly scarce in indoor parking. Existing methods depend on expensive hardware (e.g., LiDAR) or manual mapping, both of which are costly and inefficient. The rise of the Internet of Things (IoT) has enabled large-scale data collection and connectivity, offering new opportunities for automated road network extraction. OpenStreetMap (OSM), as a crowdsourced IoT-driven platform, provides multi-layer geospatial data, including the Road Network Layer (RNL), Lane Boundary Layer (LBL), and Turn Sign Layer (TSL). However, OSM data often suffers from incompleteness and noisy connectivity, affecting the continuity and accuracy of road networks. This paper introduces OSM2Net, a novel framework designed to extract road networks from individual layers and leverage multi-layer data to construct directed road networks. Specifically, OSM2Net rasterizes noisy OSM data into bitmaps for image processing and multi-layer fusion. By leveraging the topology relationship between lane boundaries and road networks, a Lane-Road Map Generator (LRMG) creates a simulated dataset for training. Then, utilizing the simulated dataset, a Lane2Net model is designed to extract road networks from sparse lane boundary images. The framework then vectorizes bitmaps into a lightweight, undirected road network and refines it into a directed network by extracting and matching turn sign information. Experimental results show that Lane2Net achieves Intersection over Union (IoU) of 93% and 92% using simulated and real-world datasets, respectively. Extensive experiments on real-world datasets confirm that OSM2Net delivers robust completeness and high-quality road network extraction. © 2014 IEEE.

关键词： advanced public transportation systems

来源：评论

学校读者我要写书评

暂无评论

Accelerate Inference of CNN Models on CPU via Column Combining Based on Simulated Annealing 11

Accelerate Inference of CNN Models on CPU via Column Combini...

引用

11th International Symposium on Computing and networking, CANDAR 2023

作者： Lin, Chien-Hung Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan Institute of Information Science Academia Sinica Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan National Taiwan University The Graduate School of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350306705

Convolutional Neural Networks (CNNs) have been successful in various computer vision tasks. However, the size of state-of-The-Art CNN models tends to be tremendous, which results in very long inference times and high memory usage. Model compression technology such as unstructured pruning can prune a significant proportion of parameters without affecting accuracy, but efficient utilization of sparsity remains a challenge. Column combining compress unstructurally-pruned CNN models by combining multiple sparse columns in a convolutional filter matrix into a single dense column. In addition, pruning all but the largest magnitude weight in each row of the combined column further compresses the matrix effectively. However, previous work did not address the details of partitioning sparse columns to minimize the negative impact of additional pruning on the performance of the model. In this work, we first prove that the column partition problem is an NP-Complete problem. Next, we propose a column combining scheme based on simulated annealing and global unstructured pruning to minimize the adverse effects of additional pruning on model performance. We implement the acceleration of column-combined CNN models using the TVM AI compiler without special hardware support. The proposed scheme achieves more efficient model compression, leading to a 0.65% improvement in accuracy and a 1.24× faster inference time on VGG19 under 88% sparsity with the TinyImageNet dataset. © 2023 IEEE.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Quantum Remote State Preparation Based on Quantum Network Coding

引用

computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

来源：评论

学校读者我要写书评

暂无评论

Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory

Dynamic Encoding and Decoding of Information for Split Learn...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Omar Alhussein Moshi Wei Arashmid Akhavain Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Advanced Networking Team Huawei Ottawa Research & Development Centre Ottawa Canada Department of Computer Science York University Toronto Canada

Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The encoder shares so-called latent representation, rather than raw data, for model training. In mobile-edge computing, network functions (such as traffic forecasting) can be trained via split learning where an encoder resides in a user equipment (UE) and a decoder resides in the edge network. Based on the data processing inequality and the information bottleneck (IB) theory, we present a new framework and training mechanism to enable a dynamic balancing of the transmission resource consumption with the informativeness of the shared latent representations, which directly impacts the predictive performance. The proposed training mechanism offers an encoder-decoder neural network architecture featuring multiple modes of complexity-relevance tradeoffs, enabling tunable performance. The adaptability can accommodate varying real-time network conditions and application requirements, potentially reducing operational expenditure and enhancing network agility. As a proof of concept, we apply the training mechanism to a millimeter-wave (mmWave)-enabled throughput prediction problem. We also offer new insights and highlight some challenges related to recurrent neural networks from the perspective of the IB theory. Interestingly, we find a compression phenomenon across the temporal domain of the sequential model, in addition to the compression phase that occurs with the number of training epochs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：