Aiming at excessive users existing in a pico base station(PBS)in the multi-layer heterogeneous networks,the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper...Aiming at excessive users existing in a pico base station(PBS)in the multi-layer heterogeneous networks,the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper.By deploying a relay node with energy harvesting function,the data of some users in the PBS can be transferred to an adjacent idle PBS.The bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization problem.The objective is to maximize the energy eficiency of the whole heterogeneous networks under the constraints of the user's minimum data rate and energy consumption.The suboptimal solution is obtained by using the particle swarm optimization(PSO)algorithm and quantum-behaved particle swarm optimization(QPSO)algorithm.Simulation results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth method.In addition,the time complexity of the adopted methods is relatively low.展开更多
The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.展开更多
The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.展开更多
Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing...Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.展开更多
This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid s...This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),...The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),which can be abstracted as a heterogeneous combat network(HCN).It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS.To this end,this paper proposes an integrated framework called HCN disintegration based on double deep Q-learning(HCN-DDQL).Firstly,the enemy’s CSoS is abstracted as an HCN,and an evaluation index based on the capability and attack costs of nodes is proposed.Meanwhile,a mathematical optimization model for HCN disintegration is established.Secondly,the learning environment and double deep Q-network model of HCN-DDQL are established to train the HCN’s disintegration strategy.Then,based on the learned HCN-DDQL model,an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed.Finally,a case study is used to demonstrate the reliability and effectiveness of HCNDDQL,and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.展开更多
Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic...Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.展开更多
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra...To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.展开更多
With the increasing deployment of Unmanned Aerial Vehicle-Hangar(UAV-H)clusters in dynamic environments such as disaster response and precision agriculture,existing networking schemes often struggle with adaptability ...With the increasing deployment of Unmanned Aerial Vehicle-Hangar(UAV-H)clusters in dynamic environments such as disaster response and precision agriculture,existing networking schemes often struggle with adaptability to complex scenarios,while traditional Vertical Handoff(VHO)algorithms fail to fully address the unique challenges of UAV-H systems,including high-speed mobility and limited computational resources.To bridge this gap,this paper proposes a heterogeneous network architecture integrating 5th Generation Mobile Communication Technology(5G)cellular networks and self-organizing mesh networks for UAV-H clusters,accompanied by a novel VHO algorithm.The proposed algorithm leverages Multi-Attribute Decision-Making(MADM)theory combined with Genetic Algorithm(GA)optimization,incorporating edge computing to enable real-time decision-making and offload computational tasks efficiently.By constructing a utility function through attribute and weight matrices,the algorithm ensures UAV-H clusters dynamically select the optimal network access with the highest utility value.Simulation results demonstrate that the proposed method reduces network handoff times by 26.13%compared to the Decision Tree VHO(DT-VHO),effectively mitigating the ping-pong effect,and enhancing total system throughput by 19.99%under the same conditions.In terms of handoff delay,it outperforms the Artificial Neural Network VHO(ANN-VHO),significantly improving the Quality of Service(QoS).Finally,real-world hardware platform experiments validate the algorithm’s feasibility and superior performance in practical UAV-H cluster operations.This work provides a robust solution for seamless network connectivity in high-mobility UAV clusters,offering critical support for emerging applications requiring reliable and efficient wireless communication.展开更多
This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading d...This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.展开更多
Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching...Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching typically works on one single protocol.For protocol-heterogeneous users,existing packet switch architectures have to construct multiple protocol-specific switching instances,resulting in severe resource waste.In this article,we propose the heterogeneous protocol-independent packet switch architecture(HISA).HISA employs a fast parsing structure to achieve efficient heterogeneous packet parsing and a novel match-action pipeline to achieve shared packet processing among heterogeneous users.HISA can also support the online configuration of switching behaviors.Use cases illustrate the effectiveness of applying HISA in SDSN.Numerical results show that compared to existing packet switching,HISA can significantly improve the resource utilization of SDSN.展开更多
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li...Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.展开更多
Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local an...Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.展开更多
In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic natu...In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.展开更多
Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so...Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.展开更多
This study proposes a tractable approach to analyze the physical-layer security in the downlink of a multi-tier heterogeneous cellular network. This method is based on stochastic geometry, has low computational comple...This study proposes a tractable approach to analyze the physical-layer security in the downlink of a multi-tier heterogeneous cellular network. This method is based on stochastic geometry, has low computational complexity, and uses the two-dimensional Poisson point process to model the locations of K-tier base stations and receivers, including those of legitimate users and eavesdroppers. Then, the achievable secrecy rates for an arbitrary user are determined and the upper and lower bounds of secrecy coverage probability derived on the condition that cross-tier interference is the main contributor to aggregate interference. Finally, our analysis results reveal the innate connections between information-theoretic security and the spatial densities of legitimate and malicious nodes.展开更多
基金the National Natural Science Foundation of China(Nos.61871133 and 61971139)the Natural Science Foundation of Fujian Province(No.2018J01805)。
文摘Aiming at excessive users existing in a pico base station(PBS)in the multi-layer heterogeneous networks,the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper.By deploying a relay node with energy harvesting function,the data of some users in the PBS can be transferred to an adjacent idle PBS.The bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization problem.The objective is to maximize the energy eficiency of the whole heterogeneous networks under the constraints of the user's minimum data rate and energy consumption.The suboptimal solution is obtained by using the particle swarm optimization(PSO)algorithm and quantum-behaved particle swarm optimization(QPSO)algorithm.Simulation results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth method.In addition,the time complexity of the adopted methods is relatively low.
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
文摘Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.
基金Project supported by Jilin Provincial Science and Technology Development Plan(Grant No.20220101137JC).
文摘This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by the National Natural Science Foundation of China(7200120972231011+2 种基金72071206)the Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province(2020RC4046)the Science Foundation for Outstanding Youth Scholars of Hunan Province(2022JJ20047).
文摘The rapid development of military technology has prompted different types of equipment to break the limits of operational domains and emerged through complex interactions to form a vast combat system of systems(CSoS),which can be abstracted as a heterogeneous combat network(HCN).It is of great military significance to study the disintegration strategy of combat networks to achieve the breakdown of the enemy’s CSoS.To this end,this paper proposes an integrated framework called HCN disintegration based on double deep Q-learning(HCN-DDQL).Firstly,the enemy’s CSoS is abstracted as an HCN,and an evaluation index based on the capability and attack costs of nodes is proposed.Meanwhile,a mathematical optimization model for HCN disintegration is established.Secondly,the learning environment and double deep Q-network model of HCN-DDQL are established to train the HCN’s disintegration strategy.Then,based on the learned HCN-DDQL model,an algorithm for calculating the HCN’s optimal disintegration strategy under different states is proposed.Finally,a case study is used to demonstrate the reliability and effectiveness of HCNDDQL,and the results demonstrate that HCN-DDQL can disintegrate HCNs more effectively than baseline methods.
基金supported by the National Natural Science Foundation of China under Grant 62371098the National Key Laboratory ofWireless Communications Foundation under Grant IFN20230203the National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.
文摘To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.
基金supported by the Key R&D Plan of Shandong Province(Major Science and Technology Innovation Project)No.2023CXGC0107012024 City-University Integrated Development Strategic Engineering Project No.JNSX2024066.
文摘With the increasing deployment of Unmanned Aerial Vehicle-Hangar(UAV-H)clusters in dynamic environments such as disaster response and precision agriculture,existing networking schemes often struggle with adaptability to complex scenarios,while traditional Vertical Handoff(VHO)algorithms fail to fully address the unique challenges of UAV-H systems,including high-speed mobility and limited computational resources.To bridge this gap,this paper proposes a heterogeneous network architecture integrating 5th Generation Mobile Communication Technology(5G)cellular networks and self-organizing mesh networks for UAV-H clusters,accompanied by a novel VHO algorithm.The proposed algorithm leverages Multi-Attribute Decision-Making(MADM)theory combined with Genetic Algorithm(GA)optimization,incorporating edge computing to enable real-time decision-making and offload computational tasks efficiently.By constructing a utility function through attribute and weight matrices,the algorithm ensures UAV-H clusters dynamically select the optimal network access with the highest utility value.Simulation results demonstrate that the proposed method reduces network handoff times by 26.13%compared to the Decision Tree VHO(DT-VHO),effectively mitigating the ping-pong effect,and enhancing total system throughput by 19.99%under the same conditions.In terms of handoff delay,it outperforms the Artificial Neural Network VHO(ANN-VHO),significantly improving the Quality of Service(QoS).Finally,real-world hardware platform experiments validate the algorithm’s feasibility and superior performance in practical UAV-H cluster operations.This work provides a robust solution for seamless network connectivity in high-mobility UAV clusters,offering critical support for emerging applications requiring reliable and efficient wireless communication.
基金Project supported the Natural Science Foundation of Zhejiang Province, China (Grant No. LQN25F030011)the Fundamental Research Project of Hangzhou Dianzi University (Grant No. KYS065624391)+1 种基金the National Natural Science Foundation of China (Grant No. 61573148)the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2019A050520001)。
文摘This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.
基金supported by the National Natural Science Foundation of China(62101300,62341130)the Youth Fund Program of the Beijing National Research Center for Information Science and Technology under Grant BNR2021RC01012the Open Research Fund Program of the Beijing National Research Center for Information Science and Technology under Grant BNR2021KF02001.
文摘Software-defined satellite networks(SDSNs)play an essential role in future networks.Due to the diverse service scenarios,SDSN faces the demand of packet processing for heterogeneous protocols.Existing packet switching typically works on one single protocol.For protocol-heterogeneous users,existing packet switch architectures have to construct multiple protocol-specific switching instances,resulting in severe resource waste.In this article,we propose the heterogeneous protocol-independent packet switch architecture(HISA).HISA employs a fast parsing structure to achieve efficient heterogeneous packet parsing and a novel match-action pipeline to achieve shared packet processing among heterogeneous users.HISA can also support the online configuration of switching behaviors.Use cases illustrate the effectiveness of applying HISA in SDSN.Numerical results show that compared to existing packet switching,HISA can significantly improve the resource utilization of SDSN.
基金supported by the Science and Technology Project of State Grid Corporation of China under grant 52094021N010(5400-202199534A-0-5-ZN)。
文摘Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms.
基金the National Natural Science Foundation of China(Grant Nos.:62272288,U22A2041)Fundamental Research Funds for the Central Universities,Shaanxi Normal University(Grant No.:GK202302006)the Scientific Research Fund of Hunan Provincial Education Department of China(Grant No.:22B0097).
文摘Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.
基金supported by the Liaoning Provincial Education Department Fund,grant number JYTZD2023083.
文摘In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.
基金supported in part by the National Natural Science Foundation of China(62273255,62350003,62088101)the Shanghai Science and Technology Cooperation Project(22510712000,21550760900)+1 种基金the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities
文摘Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.
基金supported in part by National Natural Science Foundation of China under Grant No.61401510,61521003National High-tech R&D Program(863 Program)under Grant No.2015AA01A708
文摘This study proposes a tractable approach to analyze the physical-layer security in the downlink of a multi-tier heterogeneous cellular network. This method is based on stochastic geometry, has low computational complexity, and uses the two-dimensional Poisson point process to model the locations of K-tier base stations and receivers, including those of legitimate users and eavesdroppers. Then, the achievable secrecy rates for an arbitrary user are determined and the upper and lower bounds of secrecy coverage probability derived on the condition that cross-tier interference is the main contributor to aggregate interference. Finally, our analysis results reveal the innate connections between information-theoretic security and the spatial densities of legitimate and malicious nodes.
