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A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

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China communications 2024年第5期21卷 177-189页

作者： Fu Shiming Zhang Ping Shi Xuehong school of Artificial Intelligence Chongqing University of EducationChongqing 400065China school of communications and information engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information *** wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various *** encryption and authentication mechanisms cannot prevent attacks launched by internal malicious *** trust-based security mechanism is usually adopted to solve this problem in ***,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor ***,how to efficiently collect behavioral evidences are rarely *** address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud ***,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate ***,the cloud model is introduced to estimate the trust values for sensor ***,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety *** results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

关键词： behavior monitoring cloud fuzzy trust wireless sensor networks

User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

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China communications 2024年第6期21卷 69-86页

作者： Wu Dapeng Yang Lin Cui Yaping He Peng Wang Ruyan school of communications and information engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Advanced Network and Intelligent Connection technology key laboratory of chongqing Education Commission of China Chongqing 400065China chongqing key laboratory of Ubiquitous Sensing and Networking Chongqing 400065China

The emergence of various new services has posed a huge challenge to the existing network *** improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible ***,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past *** this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is *** divide the caching strategy into three phases,that is,the content placement,the content delivery and the content *** the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents *** the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached *** the content update phase,a content update algorithm is proposed according to the popularity of the ***,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

关键词： cooperative caching network delay timevarying popularity user preference

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

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China communications 2024年第4期21卷 104-119页

作者： Huang Xiaoge Yin Hongbo Cao Bin Wang Yongsheng Chen Qianbin Zhang Jie school of communications and information engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China State key laboratory of Networking and Switching technology Beijing University of Posts and TelecommunicationsBeijing 100876China school of Communication and information engineering University of SheffieldUnited Kingdom

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)*** ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus *** to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious ***,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network *** results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

关键词： blockchain energy consumption fog computing network Internet of Things latency

Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue school of communications and information engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China key laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China school of information engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

Gradient sparsification for efficient wireless federated learning with differential privacy

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Science China(information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU school of Electronic and Optical engineering Nanjing University of Science and Technology Zhejiang lab key laboratory of Computer Network and information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation school of information and Communication engineering Hainan University school of communications and information engineering Nanjing University of Posts and Telecommunications school of Electronic information and Electrical engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client’s local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client’s DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

Video caching and scheduling with edge cooperation

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Digital communications and Networks 2024年第2期10卷 450-460页

作者： Zhidu Li Fuxiang Li Tong Tang Hong Zhang Jin Yang school of Communication and information engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Advanced Network and Intelligent Connection technology key laboratory of chongqing Education Commission of China Chongqing400065China key laboratory of Ubiquitous Sensing and Networking in chongqing Chongqing400065China

In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video *** aim is to reduce the average initial buffer delay and improve the quality of user *** the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer *** on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update *** the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak *** the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer *** results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.

关键词： Video service Distributed and collaborative caching Long-term popularity Short-term popularity

Joint Design of ISAC Waveform Under PAPR Constraints

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China communications 2024年第7期21卷 186-211页

作者： Chen Yating Wen Cai Huang Yan Liang Le Li Jie Zhang Hui Hong Wei State key lab of Millimeter Waves Southeast UniversityNanjing 211100China school of information Science and technology Northwest UniversityXi’an 710127China National mobile communications Research laboratory Nanjing 211100China College of Electronic and information engineering Nanjing University of Aeronautics and AstronauticsNanjing 211100China

In this paper,we formulate the precoding problem of integrated sensing and communication(ISAC)waveform as a non-convex quadratically constrained quadratic programming(QCQP),in which the weighted sum of communication multi-user interference(MUI)and the gap between dual-use waveform and ideal radar waveform is minimized with peak-toaverage power ratio(PAPR)*** propose an efficient algorithm based on alternating direction method of multipliers(ADMM),which is able to decouple multiple variables and provide a closed-form solution for each *** addition,to improve the sensing performance in both spatial and temporal domains,we propose a new criteria to design the ideal radar waveform,in which the beam pattern is made similar to the ideal one and the integrated sidelobe level of the ambiguity function in each target direction is minimized in the region of *** limited memory Broyden-Fletcher-Goldfarb-Shanno(LBFGS)algorithm is applied to the design of the ideal radar waveform which works as a reference in the design of the dual-function *** results indicate that the designed dual-function waveform is capable of offering good communication quality of service(QoS)and sensing performance.

关键词： ambiguity function integrated sensing and communication MIMO OFDM PAPR waveform design

Weakly supervised point cloud segmentation via deep morphological semantic information embedding

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CAAI Transactions on Intelligence technology 2024年第3期9卷 695-708页

作者： Wenhao Xue Yang Yang Lei Li Zhongling Huang Xinggang Wang Junwei Han Dingwen Zhang school of Automation Northwestern Polytechnical UniversityXi'anChina Institute of Mechanical and Electrical engineering BeijingChina school of Electronic information and communications Huazhong University of Science and TechnologyWuhanChina

Segmenting the semantic regions of point clouds is a crucial step for intelligent agents to understand 3D *** supervised point cloud segmentation is highly desirable because entirely labelling point clouds is highly time-consuming and *** the low-costing labelling of 3D point clouds,the scene-level label is one of the most effortless label ***,due to the limitation of classifier discriminative capability and the orderless and structurless nature of the point cloud data,existing scene-level method is hard to transfer the semantic information,which usually leads to the under-activated or over-activated *** this end,a local semantic embedding network is introduced to learn local structural patterns and semantic ***,the proposed network contains graph convolution-based dilation and erosion embedding modules to implement‘inside-out’and‘outside-in’semantic information dissemination ***,the proposed weakly supervised learning framework could achieve the mutual propagation of semantic information in the foreground and *** experiments on the widely used ScanNet benchmark demonstrate the superior capacity of the proposed approach when compared to the current alternatives and baseline models.

关键词： artificial intelligence computer vision deep learning

Multi-layer network embedding on scc-based network with motif

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Digital communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China key laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China key laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of information and Communication engineering Harbin Engineering UniversityHarbin 150001China school of information and Communication engineering Hainan UniversityHaikou 570228China State key laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif