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
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
Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and e...Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.展开更多
Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices...Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.展开更多
Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) se...Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) sequestration.As the injection rate of water displacing oil increases and the displacement pattern transits from capillary fingering to viscous fingering,there is a broad crossover zone between the two that can adversely affect the oil displacement efficiency.While previous studies have utilized phase diagrams to investigate the influence of the viscosity ratio and wettability of the crossover zone,fewer have studied the impact of rock heterogeneity.In this study,we created pore network models with varying degrees of heterogeneity to simulate water flooding at different injection rates.Our model quantifies capillary and viscous fingering characteristics while investigating porous media heterogeneity's role in the crossover zone.Analysis of simulation results reveals that a higher characteristic front flow rate within the crossover zone leads to earlier breakthrough and reduced displacement efficiency.Increased heterogeneity in the porous media raises injection-site pressure,lowers water saturation,and elevates the characteristic front flow rate,thereby expanding the extent of crossover zone.展开更多
This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based h...This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based heterogeneous networks.In this setup,the RF links obey κ-μ fading,while the UOWC links undergo the generalized Gamma fading with the pointing error impairments.The relay operates under an Amplify-and-Forward(AF)protocol.Additionally,the attenuation caused by the Absorption and Scattering(AaS)is considered in UOWC links.The work yields precise results for the Average Channel Capacity(ACC),Outage Probability(OP),and average Bit Error Rate(BER).Furthermore,to reveal deeper insights,bounds on the ACC and asymptotic results for the OP and average BER are derived.The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output(SISO)-RF/UOWC AF systems.Various factors affecting the Diversity Gain(DG)of the MIMO-RF/UOWC AF system include the number of antennas/apertures,fading parameters of both links,and pointing error parameters.Moreover,while an increase in the AaS effect can result in significant attenuation,it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete hetero...Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
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financial support provided by the Future Energy System at University of Alberta and NSERC Discovery Grant RGPIN-2023-04084。
文摘Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.
基金This work is funded in part by the Science and Technology Development Fund,Macao SAR(Grant Nos.0093/2022/A2,0076/2022/A2 and 0008/2022/AGJ)in part by the National Nature Science Foundation of China(Grant No.61872452)+3 种基金in part by Special fund for Dongguan’s Rural Revitalization Strategy in 2021(Grant No.20211800400102)in part by Dongguan Special Commissioner Project(Grant No.20211800500182)in part by Guangdong-Dongguan Joint Fund for Basic and Applied Research of Guangdong Province(Grant No.2020A1515110162)in part by University Special Fund of Guangdong Provincial Department of Education(Grant No.2022ZDZX1073).
文摘Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.
基金supported by the Research and Innovation Fund for Graduate Students of Southwest Petroleum University(No.2022KYCX027)supported by the National Natural Science Foundation for Youth Grant(No.41902157).
文摘Studying immiscible fluid displacement patterns can provide a better understanding of displacement processes within heterogeneous porous media,thereby helping improving oil recovery and optimizing geological CO_(2) sequestration.As the injection rate of water displacing oil increases and the displacement pattern transits from capillary fingering to viscous fingering,there is a broad crossover zone between the two that can adversely affect the oil displacement efficiency.While previous studies have utilized phase diagrams to investigate the influence of the viscosity ratio and wettability of the crossover zone,fewer have studied the impact of rock heterogeneity.In this study,we created pore network models with varying degrees of heterogeneity to simulate water flooding at different injection rates.Our model quantifies capillary and viscous fingering characteristics while investigating porous media heterogeneity's role in the crossover zone.Analysis of simulation results reveals that a higher characteristic front flow rate within the crossover zone leads to earlier breakthrough and reduced displacement efficiency.Increased heterogeneity in the porous media raises injection-site pressure,lowers water saturation,and elevates the characteristic front flow rate,thereby expanding the extent of crossover zone.
基金supported in part by the National Natural Science Foundation of China under Grant 62301272the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grants NY223023 and NY223027.
文摘This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based heterogeneous networks.In this setup,the RF links obey κ-μ fading,while the UOWC links undergo the generalized Gamma fading with the pointing error impairments.The relay operates under an Amplify-and-Forward(AF)protocol.Additionally,the attenuation caused by the Absorption and Scattering(AaS)is considered in UOWC links.The work yields precise results for the Average Channel Capacity(ACC),Outage Probability(OP),and average Bit Error Rate(BER).Furthermore,to reveal deeper insights,bounds on the ACC and asymptotic results for the OP and average BER are derived.The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output(SISO)-RF/UOWC AF systems.Various factors affecting the Diversity Gain(DG)of the MIMO-RF/UOWC AF system include the number of antennas/apertures,fading parameters of both links,and pointing error parameters.Moreover,while an increase in the AaS effect can result in significant attenuation,it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金Project supported by the National Natural Science Foundations of China(Grant Nos.62171401 and 62071411).
文摘Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
