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Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

China Communications 2024年第4期21卷 38-52页

作者： Jiang Fan Qin Junwei Liu Lei Tian Hui School of Communications and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy co

关键词： associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC)

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A survey on the network models applied in the industrial network optimization

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Science China(Information Sciences) 2024年第2期67卷 85-103页

作者： Chao DONG Xiaoxiong XIONG Qiulin XUE Zhengzhen ZHANG Kai NIU Ping ZHANG Key Laboratory of universal Wireless Communications Ministry of EducationBeijing University of Posts and Telecommunications Smart City College Beijing Union University

Network architecture design is critical for optimizing industrial networks. Network architectures can be classified into small-scale networks and large-scale networks based on scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, their structure often has regular topology. For large-scale ones, the current body of work mainly focuses on random characteristics of network nodes and edges. Recently, widely used models include random networks, small-world networks,and scale-free networks. In this study, starting from the scale of the network, network modeling methods based on graph theory as well as their industrial applications, are summarized and analyzed. Moreover, a novel network performance metric, called system entropy, is proposed. From the perspective of mathematical properties, an analysis of its non-negativity and concavity is performed. The advantage of system entropy is that it can cover the existing regular networks, random networks, small-world networks, and scale-free networks, and has strong generality. The simulation results reveal that this proposed metric can achieve the comparison of various industrial networks under different models.

关键词： industrial network small-scale network large-scale network graph theory system entropy

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Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

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Computers, Materials & Continua 2024年第5期79卷 2697-2721页

作者： Kamlesh Kumar Soothar Yuanxiang Chen Arif Hussain Magsi Cong Hu Hussain Shah School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an710071China State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)*** emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of *** existingliterature witnessed various partial or inadequate *** the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and *** traditional fault managementsystems,this research has three-fold ***,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC ***,this paper assesses the classificationdelay of each classification ***,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults *** work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various *** addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid *** findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 *** the other hand,EL techniques improved the accuracy in detecting fiberoptic ***,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

关键词： Fiber optics fault detection multiclassification machine learning ensemble learning

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Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 713-727页

作者： Yizhuo CAI Bo LEI Qianying ZHAO Jing PENG Min WEI Yushun ZHANG Xing ZHANG Wireless Signal Processing and Network Laboratory Beijing University of Posts and TelecommunicationsBeijing 100876China Research Institute of China Telecom Co. Ltd.Beijing 102209China beijing Telecom Beijing 100011China

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global ***,factors such as network topology and computing power of devices can affect its training or communication process in complex network *** and network convergence(CNC)of sixth-generation(6G)networks,a new network architecture and paradigm with computing-measurable,perceptible,distributable,dispatchable,and manageable capabilities,can effectively support federated learning training and improve its communication *** guiding the participating devices'training in federated learning based on business requirements,resource load,network conditions,and computing power of devices,CNC can reach this *** this paper,to improve the communication eficiency of federated learning in complex networks,we study the communication eficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating *** simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model *** results show that the methods we proposed can cope well with complex network situations,effectively balance the delay distribution of participating devices for local training,improve the communication eficiency during the transfer of model parameters,and improve the resource utilization in the network.

关键词： Computing and network convergence Communication efficiency Federated learning Two architectures

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Experimental demonstration of a quantum downstream access network in continuous variable quantum key distribution with a local local oscillator

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Photonics Research 2024年第6期12卷 1262-1273页

作者： DENGKE QI XIANGYU WANG ZHENGHUA LI JIAYU MA ZIYANG CHEN YUEMING LU SONG YU State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(MoE) Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronicsand Center for Quantum Information TechnologyPeking UniversityBeijing 100871China

Quantum networks provide opportunities and challenges across a range of intellectual and technical frontiers,including quantum computation,communication,and *** traditional communication networks,quantum networks utilize quantum bits rather than classical bits to store and transmit *** key distribution(QKD)relying on the principles of quantum mechanics is a key component in quantum networks and enables two parties to produce a shared random secret key,thereby ensuring the security of data *** this work,we propose a cost-effective quantum downstream access network structure in which each user can get their corresponding key information through terminal *** on this structure,we demonstrate the first four-end-users quantum downstream access network in continuous variable QKD with a local local *** contrast to point-to-point continuous variable QKD,the network architecture reevaluates the security of each user and accounts for it accordingly,and each user has a lower tolerance for excess noise as the overall network expands with more ***,the feasibility of the experiment is based on the analysis of the theoretical model,noise analysis,and multiple techniques such as the particle filter and adaptive equalization algorithm used to suppress excess *** results show that each user can get a low level of excess noise and can achieve secret key rates of 546 kbps,535 kbps,522.5 kbps,and 512.5 kbps under a transmission distance of 10 km,respectively,with the finite-size block of 1×10~*** not only verifies the good performance but also provides the foundation for the future multi-user quantum downstream access networks.

关键词： quantum oscillator local

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Dynamic Scene Graph Generation of Point Clouds with Structural Representation Learning

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Tsinghua Science and Technology 2024年第1期29卷 232-243页

作者： Chao Qi Jianqin Yin Zhicheng Zhang Jin Tang School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China Standard and Metrology Research Institute China Academy of Railway Sciences Corporation LimitedBeijing 100081China

Scene graphs of point clouds help to understand object-level relationships in the 3D *** graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud *** point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual *** address these problems,we explore a method to convert the point clouds into structured data and generate graphs without given ***,we cluster points with similar augmented features into groups and establish their relationships,resulting in an initial structural representation of the point ***,we propose a Dynamic Graph Generation Network(DGGN)to judge the semantic labels of targets of different *** dynamically splits and merges point groups,resulting in a scene graph with high *** show that our methods outperform other baseline *** output reliable graphs describing the object-level relationships without additional manual labeled data.

关键词： scene graph generation structural representation point cloud

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Towards Data-Driving Multi-View Evaluation Framework for Scratch

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Tsinghua Science and Technology 2024年第2期29卷 517-528页

作者： Xiaolin Chai Yan Sun Yan Gao Department of Computer Science Beijing University of Posts and TelecommunicationBeijing 100876China Intellectual Property Institute Shandong Polytechnic CollegeJining 272067China

As one of the most popular visual programming languages,Scratch has a lot of evaluation around *** evaluation can help programmers understand their projects *** the same time,it can also provide a reference for them to browse other projects in the online *** of the existing evaluations on Scratch are carried from three perspectives:Computational Thinking(CT)ability,visual presentation aesthetics,and code *** them,the assessment of CT and code quality is mainly carried out from the program script,while the evaluation of visual aesthetics is analyzed from the perspective of image sequences generated by project *** single-view evaluation focuses on the performance of a program in a certain aspect and is *** this paper,we propose a multi-view evaluation framework to integrate various evaluations using different *** quantitatively analyze the assessment of different views driven by *** with overall evaluations that represent human opinions,we analyze their differences and *** experiments,we determine the weights of different integration policies,the proposed multi-view evaluation method can generate evaluation results similar to human opinions.

关键词： Scratch Computational Thinking(CT) visual aesthetic static analysis integration policy machine learning

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FL-EASGD:Federated Learning Privacy Security Method Based on Homomorphic Encryption

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Computers, Materials & Continua 2024年第5期79卷 2361-2373页

作者： Hao Sun Xiubo Chen Kaiguo Yuan Information Security Center State Key Laboratory ofNetworking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext ***,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference ***,safeguarding the privacy of model parameters becomes *** proposed solution involves incorporating homomorphic encryption algorithms into the federated learning ***,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal *** solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption ***,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel ***,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each *** addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data ***,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.

关键词： Federated learning homomorphic encryption privacy security stochastic gradient descent

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Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

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Computers, Materials & Continua 2024年第5期79卷 2625-2646页

作者： Mingliang Sun Jie Yuan Xiaoyong Li Dongxiao Liu Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing100876China

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

关键词： Image encryption chaotic system compressive sensing arnold transform

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Improvement of Ga_(2)O_(3)vertical Schottky barrier diode by constructing NiO/Ga_(2)O_(3)heterojunction

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Journal of Semiconductors 2024年第4期45卷 63-68页

作者： Xueqiang Ji Jinjin Wang Song Qi Yijie Liang Shengrun Hu Haochen Zheng Sai Zhang Jianying Yue Xiaohui Qi Shan Li Zeng Liu Lei Shu Weihua Tang Peigang Li School of Integrated Circuits&State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China College of Integrated Circuit Science and Engineering Nanjing University of Posts and TelecommunicationsNanjing 210023China beijing Microelectronics Technology Institute Beijing 100076China

The high critical electric field strength of Ga_(2)O_(3)enables higher operating voltages and reduced switching losses in power electronic *** Schottky metals and epitaxial films are essential for further enhancing device *** this work,the fabrication of vertical Ga_(2)O_(3)barrier diodes with three different barrier metals was carried out on an n--Ga_(2)O_(3)homogeneous epitaxial film deposited on an n+-β-Ga_(2)O_(3)substrate by metal-organic chemical vapor deposition,excluding the use of edge *** ideal factor,barrier height,specific on-resistance,and breakdown voltage characteristics of all devices were investigated at room *** addition,the vertical Ga_(2)O_(3)barrier diodes achieve a higher breakdown volt-age and exhibit a reverse leakage as low as 4.82×10^(-8)A/cm^(2)by constructing a NiO/Ga_(2)O_(3)***,Ga_(2)O_(3)power detailed investigations into Schottky barrier metal and NiO/Ga_(2)O_(3)heterojunction of Ga_(2)O_(3)homogeneous epitaxial films are of great research potential in high-efficiency,high-power,and high-reliability applications.

关键词： Ga_(2)O_(3) Schottky barrier diode NiO/Ga_(2)O_(3)heterojunction

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