检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chen, Ziyue Luo, Guiyang Shao, Congzhang Yuan, Quan Li, Jinglin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350337020

Multi-agent collaborative perception depends on sharing sensory information to improve perception accuracy and robustness, as well as to extend coverage. However, most collaborative perception methods ignore the limitations of communication networks, such as limited bandwidth and the possibility of wireless conflicts. To fill this gap, this paper proposes BlindSpotEliminator, a conflict-free scheduler over the cellular-V2X networks for supporting practical collaborative point cloud perception to eliminate blind spots. BlindSpotEliminator first identifies the blind spots for each vehicle, then lists the corresponding conflict relationships based on the distribution of the blind spots and communication conflicts, and finally designs an optimized point cloud data transmission strategy to eliminate the blind spots of each vehicle. Extensive experiments show that compared with greedy algorithm and random methods, BlindSpotEliminator achieves better efficiency, i.e., transmitting 20% more point cloud data. © 2023 IEEE.

关键词： Cellular V2X collaborative perception optimization strategy point cloud data transmission

来源：评论

学校读者我要写书评

暂无评论

Design and Development of a Log Management System Based on Cloud Native Architecture 9

Design and Development of a Log Management System Based on C...

引用

9th International Conference on Systems and Informatics, ICSAI 2023

作者： Sun, Yuchen Chen, Yanpiao Zhao, Haotian Peng, Shan Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350383706

With the rapidly development and wide application of cloud computing and service computing, the use of cloud native architecture has become a mainstream trend in software development and deployment. In this booming background, logs have become the key information for each application to record its operation process throughout its life cycle, while existing log management systems are usually designed for specific production environments, which are not able to effectively meet the growing and diversified scenarios. The existing log management systems are usually designed for specific production environments and cannot effectively meet the needs of the growing number of diverse scenarios. Therefore, how to effectively handle log information from multiple sources becomes a key issue. In this paper, we design and develop a log management platform based on cloud-native architecture, which provides log collection, transmission, storage, and system management functions, and at the same time can be oriented to the cloud-native environment and other third-party applications for collection, providing a powerful tool for developers and operation and maintenance personnel. In addition, we designed an anomaly detection system using large language models for log parsing and used this system for anomaly detection in platform logs. Finally, the effectiveness of the system was verified by commonly used log data. The log management platform based on cloud-native architecture passed the test and has been utilized in our real production. © 2023 IEEE.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

Propagation Dynamics Based Resource Deployment Strategy for Edge Networks 97

Propagation Dynamics Based Resource Deployment Strategy for ...

引用

97th IEEE Vehicular technology Conference (VTC-Spring)

作者： Fan, Shaoshuai Gao, Hanlin Tian, Hui Yang, Shiyu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9798350311143

In mobile edge network, dynamically changing content requests can affect network resource deployment. In recent years, epidemic model has been explored to describe the dynamic content popularity. In this article, epidemic model is applied to characterize the content propagation in resource-limited edge networks. The epidemic parameters are analyzed through the data collected by network entities. In order to maximize satisfaction rate of users, we decompose the joint optimization problem and propose a base station caching placement and resource allocation algorithm based on propagation dynamics. Simulation results verify that the proposed strategy offers higher satisfaction rate compared with other benchmark algorithms.

关键词： Propagation Dynamics Edge Caching Resource Allocation

来源：评论

学校读者我要写书评

暂无评论

Sublinear smart semantic search based on knowledge graph over encrypted database

引用

COMPUTERS & SECURITY 2025年 151卷

作者： Li, Jiaxin Peng, Haipeng Li, Lixiang Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Natl Engn Lab Disaster Backup & Recovery Beijing 100876 Peoples R China

In light of the growing significance of data privacy in cloud computing, searchable encryption has emerged as a prominent area of research in recent years. Given that the majority of achievements are only concentrated on keyword-based search, there is a clear need to explore secure semantic extension to enhance the utilization of information. Nevertheless, majority of secure semantic schemes mainly focus on the direct combination of traditional searchable encryption and semantic extension techniques, which results in the high computation and communication overheads for multi-keyword queries. Besides, to enhance the flexibility and extensibility of secure semantic search, some researchers have already proposed encryption methods for structured data. However, the effectiveness and practicality of these methods above are often limited by their rigid structure description and redundant index building. To handle these problems, we present a secure sublinear semantic search scheme based on the knowledge graph, which can achieve sublinear knowledge graph (KG) search and smart sentence queries concurrently. The knowledge graph encryption index is constructed with the same structure as the inverted index, thereby enabling sublinear efficiency. Furthermore, the system also offers functions for both node and edge semantic extension in the knowledge graph. We demonstrate that our proposed scheme is adaptively secure and present a series of experiments to analyze its performance. The experimental results show that our scheme exhibits significant advantages in all computation, communication and expression situations.

关键词： Semantic search Searchable encryption Sublinear Knowledge graph Inverted index

来源：评论

学校读者我要写书评

暂无评论

Intelligent Energy-Efficiency Trajectory Planning of Heterogeneous Air Base Stations for 6G Emergency Communication

Intelligent Energy-Efficiency Trajectory Planning of Heterog...

引用

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

作者： Tian, Jingyue Yu, Peng Li, Wenjing Feng, Lei Zhou, Fanqin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350345407

With the development of 6G, emergency communication services upgrade and the need for edge intelligence is increasing. However, today's 6G emergency communication scenarios are changeable and complex, and existing methods cannot achieve large-scale and QoS guarantees. So we proposed a heterogeneous coverage compensation mechanism with static complete deployment and dynamic enhancement deployment. To enhance the capacity of hotpots determined by the clustering algorithm, we jointly optimize the UAV trajectory and users' connectivity to maximize the UAV's energy efficiency(EE). The optimized problem is NP-hard and is solved by deep reinforcement learning(DRL) algorithm. Simulation results show our method can significantly improve EE, which is much higher than Q-learning, PSO, and GA. © 2023 IEEE.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

A Federated Learning Approach for Net Load Forecasting in Microgrids. 24

A Federated Learning Approach for Net Load Forecasting in Mi...

引用

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Qin, Zihan Shao, Sujie Guo, Shaoyong Qiu, Xuesong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9788995004395

Microgrid is a small-scale power grid composed of various distributed energy resources, and net load forecasting (NLF) plays significant role in its energy generation planning. Due to the dispersed distribution of nodes within the microgrid and disparate user data, completing the net load forecasting task becomes challenging. In order to efficiently forecast the net load while ensuring data security, this paper proposes an approach for NLF based on federated learning (FL). Firstly, a FL framework for NLF in microgrids is constructed to complete the NLF task with long short-term memory neural network (LSTM) model and node selection (NS) in aggregation. Secondly, considering factors such as the data precision and quantity, an accuracy optimization problem model is proposed. Then, the problem is formulated as a Markov decision process (MDP), and a NS algorithm based on Asynchronous Advantage Actor-Critic (A3C) is designed for selecting a node set to optimize model aggregation. The simulation demonstrate that the proposed NS method based on A3C improves the performance of FL, resulting in better convergence of LSTM network and ensuring the scalability of the cluster in microgrid. Copyright 2023 KICS.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3-D Scene Semantic Transmission

引用

IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 898-902页

作者： Yue, Weijie Si, Zhongwei Wu, Bolin Wang, Sixian Qin, Xiaoqi Niu, Kai Dai, Jincheng Zhang, Ping Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature's different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions.

关键词： Three-dimensional displays Neural radiance field Symbols Feature extraction Decoding Entropy Optimization Image reconstruction Encoding Transforms Neural radiance field (NeRF) 3D scene transmission semantic features nonlinear transform coding

来源：评论

学校读者我要写书评

暂无评论

DT-PBFT: A Double-Layer Group Consensus Algorithm of Credibility for IoT Blockchain 2

DT-PBFT: A Double-Layer Group Consensus Algorithm of Credibi...

引用

2nd International Conference on Big Data, Information and Computer Network, BDICN 2023

作者： Chen, Yaxin Jia, Yongpu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350321364

Blockchain technology is becoming more and more popular, but performance problems have always troubled it, especially the scale of distributed networks is limited. Like Practical Byzantine consensus algorithm (PBFT), the main bottlenecks are concentrated in the scope of consensus too small, and the communication is too high. In order to implement PBFT in large-scale systems such as large-scale IoT ecosystems and blockchains, this paper proposes a reputation-based two-layer improved PBFT consensus computing model DT-PBFT. In order to better combine with the large -scale Internet of Things ecosystem, we give each node regional location, communication capacity, computing power and other related attributes to each involvement, so that Client nodes can input node attributes as parameters into the reputation calculation algorithm, to select the master node of each layer in the double-layer consensus model. Determining the master node through reputation improves the credibility of the consensus algorithm. At the same time, the DTPBFT cancels the Pre-Prepare stage, reducing communication complexity. Finally, this paper transforms the existing PBFT algorithm on the Fabric platform to implement the DT-PBFT consensus algorithm and compares its performance with traditional PBFT, proving that DT-PBFT has the best balance and excellent comprehensive performance in large-scale IoT application scenarios. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Multiple QoS Metrics-Aware Virtual Network Embedding Algorithm

引用

COMPUTER JOURNAL 2023年第3期67卷 1171-1186页

作者： Lu, Meilian Li, Meng Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.

关键词： virtual network embedding quality of service differentiated service reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

DECC: Delay-Aware Edge-Cloud Collaboration for Accelerating DNN Inference

引用

IEEE Transactions on Emerging Topics in Computing 2024年第02期13卷 1-13页

作者： Zhuang, Zirui Chen, Jianan Xu, Wenchao Qi, Qi Guo, Song Wang, Jingyu Lu, Lu Yang, Hongwei Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong Institute of China Mobile Beijing China

Deep neural network (DNN)-enabled edge intelligence has been widely adopted to support a variety of smart applications because of its ability to preserve privacy and conserve communication efficiency. The dilemma is that DNN models can be too large to be deployed on computationally constrained edge devices, and the volume of raw data can be too large to be efficiently transmitted to a centralized server. Thus, it is of utter importance that edge devices and cloud servers collaborate with each other to achieve fast and dependable model inference. Current collaborative solutions separate the DNN into two parts, which are placed and executed at the edge and in the cloud, respectively. However, these separated parts are executed consecutively, and all subsequent layers have to wait for the output of the previous layer even if they are not directly connected, causing significant inference latency. We propose a delay-aware edge-cloud collaboration (DECC) algorithm to reorganize the execution of DNN layers. By dividing DNN into several independent branches and selecting the optimal partition points, we apply a pipeline approach to parallelize the execution of these branches to minimize the inference delay. Extensive experiments show that the DECC outperforms existing methods by significantly reducing inference latency and improving throughput. IEEE

关键词： Pipelines

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：