Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re...Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.展开更多
Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources...Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.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
With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further impro...With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.展开更多
Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser(SL)networks is essential in photonics research.In this study,we propose a spatiotemporal multiplexed photonic reservoir computing(STM...Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser(SL)networks is essential in photonics research.In this study,we propose a spatiotemporal multiplexed photonic reservoir computing(STM-PRC)architecture,specifically designed for parallel prediction of the high-dimensional chaotic dynamics in complex SL networks.This is accomplished by decomposing the prediction task into multiple simplified reservoirs,leveraging the intrinsic topological characteristics of the network.Additionally,we introduce a dimensionality reduction technique for high-dimensional chaotic datasets,which exploits the symmetrical properties of the network topology and cluster synchronization patterns derived from complex network theory.This approach further simplifies the prediction process and enhances the computational efficiency of the parallel STM-PRC system.The feasibility and effectiveness of the proposed framework are demonstrated through numerical simulations and corroborated by experimental validation.Our results expand the application potential of SL networks in all-optical communication systems and suggest new directions for optical information processing.展开更多
Commercial ultra-dense low-Earth-orbit(LEO)satellite constellations have recently been deployed to provide seamless global Internet services.To improve the satellite network transmission efficiency and provide robust ...Commercial ultra-dense low-Earth-orbit(LEO)satellite constellations have recently been deployed to provide seamless global Internet services.To improve the satellite network transmission efficiency and provide robust wide-coverage computing services for future sixth-generation(6G)users,growing attention has been focused on LEO-satellite-based computing networks,to which ground users can offload computation tasks.However,how to design a LEO satellite constellation for computing networks,while considering discrepancies in the computing requirements of different regions,remains an open question.In this paper,we investigate an ultra-dense LEO-satellite-based computing network to which ground user terminals(UTs)offload part of their computing tasks to satellites.We formulate the ultra-dense constellation design problem as a multi-objective optimization problem(MOOP)to maximize the average coverage rate,transmission capacity,and computational capability,while minimizing the number of satellites.In order to depict the connectivity characteristics of satellite-based computing networks,we propose a terrestrial-satellite connectivity model to determine the coverage rate in different regions.We design a priority-adaptive algorithm to design the optimal inclined-orbit constellation by solving this MOOP.Simulation results verify the accuracy of our theoretical connectivity model and show the optimal constellation deployment,given quality-of-service(QoS)requirements.For the same number of deployed LEO satellites,the proposed constellation outperforms its existing counterparts;in particular,it achieves 25%-45%performance improvements in the average coverage rate.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
In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s...In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.展开更多
Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce different...Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.展开更多
In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging ...In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.展开更多
In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computi...In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.展开更多
With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these...With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.展开更多
Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the afford...Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring.The scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis.Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server.Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.展开更多
The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resourc...The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.展开更多
Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-int...Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.展开更多
In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as we...In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE(VUE),a Joint Allocation of Wireless resource and MEC Computing resource(JAWC)algorithm is proposed.The JAWC algorithm includes two steps:V2X links clustering and MEC computation resource scheduling.In the V2X links clustering,a Spectral Radius based Interference Cancellation scheme(SR-IC)is proposed to obtain the optimal resource allocation matrix.By converting the calculation of SINR into the calculation of matrix maximum row sum,the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced.In the MEC computation resource scheduling,by transforming the original optimization problem into a convex problem,the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained.The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network.展开更多
With the rapid development of wireless networks,the Ad Hoc networks are widely used in many fields,but the current network security solutions for the Ad Hoc network are not competitive enough.So the critical technolog...With the rapid development of wireless networks,the Ad Hoc networks are widely used in many fields,but the current network security solutions for the Ad Hoc network are not competitive enough.So the critical technology of Ad Hoc network applications shall be how to implement the security scheme.Here the discussions are focused on the specific solution against the security threats which the Ad Hoc networks will face,the methodology of a management model which uses trusted computing technology to solve Ad Hoc network security problems,and the analysis and verification for the security of this model.展开更多
基金supported in part by the Chongqing Postgraduate Research and Innovation Project(CYB22250)National Natural Science Foundation of China(62271096,U20A20157)+2 种基金Natural Science Foundation of Chongqing-China(CSTB2023NSCQ-LZX0134,CSTB2024NSCQ-LZX0124)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)。
文摘Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting.
文摘Nowadays,advances in communication technology and cloud computing have spawned a variety of smart mobile devices,which will generate a great amount of computing-intensive businesses,and require corresponding resources of computation and communication.Multiaccess edge computing(MEC)can offload computing-intensive tasks to the nearby edge servers,which alleviates the pressure of devices.Ultra-dense network(UDN)can provide effective spectrum resources by deploying a large number of micro base stations.Furthermore,network slicing can support various applications in different communication scenarios.Therefore,this paper integrates the ultra-dense network slicing and the MEC technology,and introduces a hybrid computing offloading strategy in order to satisfy various quality of service(QoS)of edge devices.In order to dynamically allocate limited resources,the above problem is formulated as multiagent distributed deep reinforcement learning(DRL),which will achieve low overhead computation offloading strategy and real-time resource allocation decisions.In this context,federated learning is added to train DRL agents in a distributed manner,where each agent is dedicated to exploring actions composed of offloading decisions and allocating resources,so as to jointly optimize system delay and energy consumption.Simulation results show that the proposed learning algorithm has better performance compared with other strategies in literature.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
文摘Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
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supported in part by National Natural Science Foundation of China(No.62071393)Fundamental Research Funds for the Central Universities(2682023ZTPY058).
文摘With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.
基金supported in part by National Natural Science Foundation of China under Grant(Nos.62431024,U22A2089,62375228)Sichuan Science Fund for Distinguished Young Scholars(2023NSFSC1969)Fundamental Research Funds for the Central Universities(2682025ZTPY058)。
文摘Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser(SL)networks is essential in photonics research.In this study,we propose a spatiotemporal multiplexed photonic reservoir computing(STM-PRC)architecture,specifically designed for parallel prediction of the high-dimensional chaotic dynamics in complex SL networks.This is accomplished by decomposing the prediction task into multiple simplified reservoirs,leveraging the intrinsic topological characteristics of the network.Additionally,we introduce a dimensionality reduction technique for high-dimensional chaotic datasets,which exploits the symmetrical properties of the network topology and cluster synchronization patterns derived from complex network theory.This approach further simplifies the prediction process and enhances the computational efficiency of the parallel STM-PRC system.The feasibility and effectiveness of the proposed framework are demonstrated through numerical simulations and corroborated by experimental validation.Our results expand the application potential of SL networks in all-optical communication systems and suggest new directions for optical information processing.
基金support from the National Natural Science Foundation of China(62322101)partial support from the Guangdong Basic and Applied Basic Research Foundation(2023B0303000019).
文摘Commercial ultra-dense low-Earth-orbit(LEO)satellite constellations have recently been deployed to provide seamless global Internet services.To improve the satellite network transmission efficiency and provide robust wide-coverage computing services for future sixth-generation(6G)users,growing attention has been focused on LEO-satellite-based computing networks,to which ground users can offload computation tasks.However,how to design a LEO satellite constellation for computing networks,while considering discrepancies in the computing requirements of different regions,remains an open question.In this paper,we investigate an ultra-dense LEO-satellite-based computing network to which ground user terminals(UTs)offload part of their computing tasks to satellites.We formulate the ultra-dense constellation design problem as a multi-objective optimization problem(MOOP)to maximize the average coverage rate,transmission capacity,and computational capability,while minimizing the number of satellites.In order to depict the connectivity characteristics of satellite-based computing networks,we propose a terrestrial-satellite connectivity model to determine the coverage rate in different regions.We design a priority-adaptive algorithm to design the optimal inclined-orbit constellation by solving this MOOP.Simulation results verify the accuracy of our theoretical connectivity model and show the optimal constellation deployment,given quality-of-service(QoS)requirements.For the same number of deployed LEO satellites,the proposed constellation outperforms its existing counterparts;in particular,it achieves 25%-45%performance improvements in the average coverage rate.
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supported by the National Level Project of China(No.KJSP2023020201)the Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory of China(No.kjw6142210240202)+1 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars of Chinathe Fundamental Research Funds for Central Universities of China。
文摘In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.
基金funded by National Research Council of Thailand(contract No.N42A671047).
文摘Physics-informed neural networks(PINNs)have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training.Their ability to enforce differential equations,constitutive relations,and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models,particularly for solid and structural mechanics,where data are often limited or noisy.This review offers a comprehensive assessment of recent developments in PINNs,combining bibliometric analysis,theoretical foundations,application-oriented insights,and methodological innovations.A biblio-metric survey indicates a rapid increase in publications on PINNs since 2018,with prominent research clusters focused on numerical methods,structural analysis,and forecasting.Building upon this trend,the review consolidates advance-ments across five principal application domains,including forward structural analysis,inverse modeling and parameter identification,structural and topology optimization,assessment of structural integrity,and manufacturing processes.These applications are propelled by substantial methodological advancements,encompassing rigorous enforcement of boundary conditions,modified loss functions,adaptive training,domain decomposition strategies,multi-fidelity and transfer learning approaches,as well as hybrid finite element–PINN integration.These advances address recurring challenges in solid mechanics,such as high-order governing equations,material heterogeneity,complex geometries,localized phenomena,and limited experimental data.Despite remaining challenges in computational cost,scalability,and experimental validation,PINNs are increasingly evolving into specialized,physics-aware tools for practical solid and structural mechanics applications.
基金supported by the National Key Research and Development Program of China under grant 2021YFA0716600。
文摘In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios.
基金This work was supported by the National Key R&D Program of China No.2019YFB1802800.
文摘In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.
基金supported by the National Science Foundation of China under Grant 62271062 and 62071063by the Zhijiang Laboratory Open Project Fund 2020LCOAB01。
文摘With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.
文摘Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring.The scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis.Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server.Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.
基金supported by National Natural Science Foundation of China(Grant No.61471053,61901052)Fundamental Research Funds for the Central Universities(Grant 2018RC03)Beijing Laboratory of Advanced Information Networks
文摘The computation resources at a single node in Edge Computing(EC)are commonly limited,which cannot execute large scale computation tasks.To face the challenge,an Offloading scheme leveraging on NEighboring node Resources(ONER)for EC over Fiber-Wireless(FiWi)access networks is proposed in this paper.In the ONER scheme,the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly,so that it can support the offloading transmission with low delay and wide bandwidth.Based on the ONER scheme supported by FiWi networks,computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops(e.g.,just one wireless hop),which achieves low delay.Additionally,an efficient Computation Resource Scheduling(CRS)algorithm based on the ONER scheme is also proposed to make offloading decision.The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm.Therefore,the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.
基金This work was supported by the National Natural Science Foundation of China(Grants 61971054 and 61601045)Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Foundation(HHX21641X002 and HHX20641X003).
文摘Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.
基金This work was supported in part by the National Key R&D Program of China under Grant 2019YFE0114000in part by the National Natural Science Foundation of China under Grant 61701042+1 种基金in part by the 111 Project of China(Grant No.B16006)the research foundation of Ministry of EducationChina Mobile under Grant MCM20180101.
文摘In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE(VUE),a Joint Allocation of Wireless resource and MEC Computing resource(JAWC)algorithm is proposed.The JAWC algorithm includes two steps:V2X links clustering and MEC computation resource scheduling.In the V2X links clustering,a Spectral Radius based Interference Cancellation scheme(SR-IC)is proposed to obtain the optimal resource allocation matrix.By converting the calculation of SINR into the calculation of matrix maximum row sum,the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced.In the MEC computation resource scheduling,by transforming the original optimization problem into a convex problem,the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained.The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network.
基金National Natural Science Foundation of China under Grant No. 60970115,National Natural Science Funds Projects of China under Grant No. 91018008
文摘With the rapid development of wireless networks,the Ad Hoc networks are widely used in many fields,but the current network security solutions for the Ad Hoc network are not competitive enough.So the critical technology of Ad Hoc network applications shall be how to implement the security scheme.Here the discussions are focused on the specific solution against the security threats which the Ad Hoc networks will face,the methodology of a management model which uses trusted computing technology to solve Ad Hoc network security problems,and the analysis and verification for the security of this model.
