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A Review of Intelligent Configuration and Its Security for Complex Networks

Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks

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China communications 2023年第4期20卷 356-367页

作者： Yunyun Li Yanjing Sun Qingyan Ren Song Li School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

Autonomous underwater vehicle(AUV)-assisted data collection is an efficient approach to implementing smart ***,the data collection in time-varying ocean currents is plagued by two critical issues:AUV yaw and sensor node *** propose an adaptive AUV-assisted data collection strategy for ocean currents to address these ***,we consider the energy consumption of an AUV in conjunction with the value of information(VoI)over the sensor nodes and formulate an optimization problem to maximize the VoI-energy *** AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target ***,using the predicted VoI-energy ratio,we sequentially design a distributed path planning algorithm to select the next target node for *** simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation,thereby reducing the AUV's energy consumption and ensuring timely data collection.

关键词： underwater sensor networks data collection ocean currents value of information energy consumption

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Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

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Chinese Physics B 2023年第10期32卷 229-240页

作者：曲英杰陈钊王伟杰马鸿洋 School of Sciences Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

Quantum error correction technology is an important method to eliminate errors during the operation of quantum *** order to solve the problem of influence of errors on physical qubits,we propose an approximate error correction scheme that performs dimension mapping operations on surface *** error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three *** to previous error correction schemes,the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction *** reducing the number of ancilla qubits required for error correction,this approach achieves savings in measurement space and reduces resource consumption *** order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping,we employ a reinforcement learning(RL)decoder based on deep Q-learning,which enables faster identification of the optimal syndrome and achieves better thresholds through conditional *** to the minimum weight perfect matching decoding,the threshold of the RL trained model reaches 0.78%,which is 56%higher and enables large-scale fault-tolerant quantum computation.

关键词： fault-tolerant quantum computing surface code approximate error correction reinforcement learning

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Spatial Modeling and Reliability Analyzing of Reconfigurable Intelligent Surfaces-Assisted Fog-RAN with Repulsion

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China communications 2023年第12期20卷 142-155页

作者： Linlin Feng Zhizhong Zhang Haonan Hu Errong Pei Yun Li School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China

Reconfigurable Intelligent Surface(RIS),fog computing,and Cell-Free(CF)network architecture are three promising technologies for application to the Ultra-Reliable Low Latency communication(URLLC)scenario in 6G mobile communication *** paper considers a RIS-assisted FogRadio Access Network(Fog-RAN)architecture where a)the repulsively distributed Fog-Access Points(FAPs)communicate in a CF manner to suppress intercell interference,b)RISs are introduced into the CF network to avoid shadowing and enhance the system performance,and c)fog computing evolved as cloud services providers at the edge of the network and an enabler for constructing a multi-layer computing power ***,we derive and validate the integral form of the maximum F-AP offloading probability and Successful Delivery Probability(SDP)of this RIS-assisted Fog-RAN over composite FisherSnedecor F fading,where the spatial effects are reconsidered with the assumption that the F-APs are modelled as a Beta Ginibre Point Process(β-GPP).The numeric and simulation results indicate that for the investigated RIS-assisted Fog-RAN,theβ-GPP-based deployment of F-APs can increase maximum of 8%of the SDP within the repulsion-effective range,compared with the Matern Cluster Process(MCP)-based ***,deploying more RISs per F-AP offers more significant SDP improvements.

关键词： β-GPP Fog-RAN reliability analysis RIS

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Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1183-1206页

作者： Xinzheng Wang Bing Guo Yan Shen College of Computer Science Sichuan UniversityChengdu610065China School of Information Science and Engineering Guilin University of TechnologyGuilin541004China School of Control Engineering Chengdu University of Information TechnologyChengdu610225China

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,*** with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is *** makes building models to predict students’performance accurately in such an environment even *** paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course *** experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

关键词： Computer education performance prediction deep learning

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Joint Resource Optimization for Secure Cooperative Perception in Vehicular Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1044-1059页

作者： Ya Kang Qingyang Song Jing Song Lei Guo Abbas Jamalipour School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Science and Engineering Shenyang University of TechnologyShenyang 110870China School of Electrical and Information Engineering The University of SydneySydney NSW2006Australia

In the realm of autonomous driving,cooperative perception serves as a crucial technology for mitigating the inherent constraints of individual vehicle's *** enable cooperative perception,vehicle-to-vehicle(V2V)communication plays an indispensable ***,owing to weak virus protection in V2V networks,the emergence and widespread adoption of V2V communications have also created fertile soil for the breeding and rapid spreading of *** stimulate vehicles to participate in cooperative perception while blocking the spreading of worms through V2V communications,we design an incentive mechanism,in which the utility of each sensory data requester and that of each sensory data provider are defined,respectively,to maximize the total utility of all the *** deal with the highly non-convex problem,we propose a pairing and resource allocation(PRA)scheme based on the Stackelberg game ***,we decompose the problem into two *** subproblem of maximizing the utility of the requester is solved via a two-stage iterative algorithm,while the subproblem of maximizing the utility of the provider is addressed using the linear search *** results demonstrate that our proposed PRA approach addresses the challenges of cooperative perception and worm spreading while efficiently converging to the Stackelberg equilibrium point,jointly maximizing the utilities for both the requester and the provider.

关键词： vehicular networks resource allocation security cooperative perception

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Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

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Computers, Materials & Continua 2024年第6期79卷 5051-5066页

作者： Yuanyao Lu Wei Chen Zhanhe Yu Jingxuan Wang Chaochao Yang School of Information Science and Technology North China University of TechnologyBeijing100144China School of Electrical and Control Engineering North China University of TechnologyBeijing100144China

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing *** to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external *** research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition *** address this,this research has developed a new model for recognizing abnormal traffic *** model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature *** approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network ***,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at *** temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data *** research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test *** results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

关键词： Vehicle abnormal behavior deep learning ResNet dense block soft thresholding

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Outage Behaviors of Active Intelligent Reflecting Surface Enabled NOMA communications

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Computer Modeling in engineering & Sciences 2023年第10期137卷 789-812页

作者： Zhiping Lu Xinwei Yue Shuo Chen Weiguo Ma School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsState Key Laboratory of Wireless Mobile CommunicationsChina Academy of Telecommunications Technology(CATT)BeijingChina School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing100101China

Active intelligent reflecting surface(IRS)is a novel and promising technology that is able to overcome the multiplicative fading introduced by passive *** this paper,we consider the application of active IRS to nonorthogonalmultiple access(NOMA)networks,where the incident signals are amplified actively through integrating amplifier to reflecting *** specifically,the performance of active/passive IRS-NOMA networks is investigated over large and small-scale fading *** to characterize the performance of active IRSNOMA networks,the exact and asymptotic expressions of outage probability for a couple of users,i.e.,near-end user n and far-end user m are derived by exploiting a 1-bit coding *** on approximated analyses,the diversity orders of user n and user m are obtained for active *** addition,the system throughput of active IRS-NOMA is discussed in the delay-sensitive *** simulation results are carried out to verify that:i)The outage behaviors of active IRS-NOMAnetworks are superior to that of passive IRS-NOMAnetworks;ii)As the reflection amplitude factors increase,the active IRS-NOMAnetworks are capable of furnishing the enhanced outage performance;and iii)The active IRS-NOMA has a larger system throughput than passive IRS-NOMA and conventional communications.

关键词： Active intelligent reflecting surface amplification noise non-orthogonal multiple access outage probability

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Research on Sarcasm Detection technology Based on Image-Text Fusion

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Computers, Materials & Continua 2024年第6期79卷 5225-5242页

作者： Xiaofang Jin Yuying Yang YinanWu Ying Xu School of Information and Communication Engineering Communication University of ChinaBeijing100024China Cable Television Technology Research Institute Academy of Broadcasting ScienceBeijing100053China

The emergence of new media in various fields has continuously strengthened the social aspect of social *** tend to express emotions in social interactions,and many people even use satire,metaphors,and other techniques to express some negative emotions,it is necessary to detect sarcasm in social comment *** sarcasm,the more reference data modalities used,the better the experimental *** paper conducts research on sarcasm detection technology based on image-text fusion *** effectively utilize the features of each modality,a feature reconstruction output algorithm is *** algorithm is based on the attention mechanism,learns the low-rank features of another modality through cross-modality,the eigenvectors are reconstructed for the corresponding modality through weighted *** only the image modality in the dataset is used,the preprocessed data has outstanding performance in reconstructing the output model,with an accuracy rate of 87.6%.When using only the text modality data in the dataset,the reconstructed output model is optimal,with an accuracy rate of 85.2%.To improve feature fusion between modalities for effective classification,a weight adaptive learning algorithm is *** algorithm uses a neural network combined with an attention mechanism to calculate the attention weight of each modality to achieve weight adaptive learning purposes,with an accuracy rate of 87.9%.Extensive experiments on a benchmark dataset demonstrate the superiority of our proposed model.

关键词： Sentiment analysis sarcasm detection feature fusion feature reconstruction

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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