The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the...The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.展开更多
High-speed wireless networks such as IEEE 802.11n have been introduced based on IEEE 802.11 to meet the growing demand for high-throughput and multimedia applications. It is known that the medium access control （MAC...High-speed wireless networks such as IEEE 802.11n have been introduced based on IEEE 802.11 to meet the growing demand for high-throughput and multimedia applications. It is known that the medium access control （MAC） efficiency of IEEE 802.11 decreases with increasing the physical rate. To improve efficiency, few solutions have been proposed such as Aggregation to concatenate a number of packets into a larger frame and send it at once to reduce the protocol overhead. Since transmitting larger frames eventuates to dramatic delay and jitter increase in other nodes, bounding the maxi- mum aggregated frame size is important to satisfy delay requirements of especially multimedia applications. In this paper, we propose a scheme called Optimized Packet Aggregation （OPA） which models the network by constrained convex optimization to obtain the optimal aggregation size of each node regarding to delay constraints of other nodes. OPA attains proportionally fair sharing of the channel while satisfying delay constrains. Furthermore, reaching the optimal point is guaranteed in OPA with low complexity. Simulation results show that OPA can successfully bound delay and meet the requirements of nodes with only an insignificant throughput penalty due to limiting the aggregation size even in dynamic conditions.展开更多
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in...Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
In order to ensure the uninterrupted communication between high-speed train and base station,driving safety and satisfying online experience of passengers,a dual-link switching algorithm based on CNN-WaveNet decision ...In order to ensure the uninterrupted communication between high-speed train and base station,driving safety and satisfying online experience of passengers,a dual-link switching algorithm based on CNN-WaveNet decision parameter multi-step prediction model is proposed to establish a two-hop relay communication system model between the high-speed train and the base station.Firstly,the switching algorithm uses convolution neural network(CNN)to extract the time sequence characteristics of decision parameters.Then,it learns the mapping relationship between feature information and decision parameters based on WaveNet and combining with rolling prediction method to realize multi-step prediction of decision parameters.Finally,dual-antenna communication mode is adopted to realize dual-link communication.The simulation results show that the proposed handover algorithm can improve handover trigger rate and handover success rate.展开更多
With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks e...With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.展开更多
Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial...Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial pattern of tourism accessibility in Chinese cities, thus substan- tially increasing their power to attract tourists and their radiation force. This paper examines the evolution and spatial characteristics of the power to attract tourism of cities linked by China＇s HSR network by measuring the influence of accessibility of 338 HSR-linked cities using GIS analysis. The results show the following. （1） The accessibility of Chinese cities is optimized by the HSR network, whose spatial pattern of accessibility exhibits an obvious traf- fic direction and causes a high-speed rail-corridor effect. （2） The spatial pattern of tourism field strength in Chinese cities exhibits the dual characteristics of multi-center annular diver- gence and dendritic diffusion. Dendritic diffusion is particularly more obvious along the HSR line. The change rate of urban tourism field strength forms a high-value corridor along the HSR line and exhibits a spatial pattern of decreasing area from the center to the outer limit along the HSR line. （3） The influence of the higher and highest tourism field strength areas along the HSR line is most significant, and the number of cities that distribute into these two types of tourism field strengths significantly increases： their area expands by more than 100% HSR enhances the tourism field strength value of regional central cities, and the radiation range of tourism attraction extends along the HSR line.展开更多
Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been...Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.展开更多
High-speed trains often use temperature sensors to monitor the motion state of bearings.However,the temperature of bearings can be affected by factors such as weather and faults.Therefore,it is necessary to analyze in...High-speed trains often use temperature sensors to monitor the motion state of bearings.However,the temperature of bearings can be affected by factors such as weather and faults.Therefore,it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors.In this study,a dynamics model of the axle box bearing of high-speed trains is established.The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring,inner-ring,and rolling-element faults.Based on the model,a thermal network method is introduced to study the temperature field distribution of the axle box bearings of high-speed trains.In this model,the heat generation,conduction,and dispersion of the isothermal nodes can be solved.The results show that the temperature of the contact point between the outer-ring raceway and rolling-elements is the highest.The relationships between the node temperature and the speed,fault type,and fault size are analyzed,finding that the higher the speed,the higher the node temperature.Under different fault types,the node temperature first increases and then decreases as the fault size increases.The effectiveness of the model is demonstrated using the actual temperature data of a high-speed train.This study proposes a thermal network model that can predict the temperature of each component of the bearings on a high-speed train under various speed and fault conditions.展开更多
This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:...This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this neces...In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.展开更多
It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sens...It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.展开更多
To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is propos...To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.展开更多
In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of ...In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of high-speed milling(HSM)pro cess,lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters.It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism.In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN)so as to have the most effective knowledge-base for given set of data.Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters.A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed.After training process,raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules.The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.展开更多
With the increasing enlargement of network scale and the rapid development of network techniques, large numbers of the network applications begin to appear. Packet capture plays an important role as one basic techniqu...With the increasing enlargement of network scale and the rapid development of network techniques, large numbers of the network applications begin to appear. Packet capture plays an important role as one basic technique used in each field of the network applications. In a high-speed network, the heavy traffic of network transmission challenges the packet capture techniques. This paper does an in-depth analysis on the traditional packet capture mechanisms in Linux, and then measures the performance bottleneck in the process of packet capture. The methods for improving the packet capture performance are presented and an optimized packet capture scheme is also designed and implemented. The test demonstrates that the new packet capture mechanism (Libpacket) can greatly improve the packet capture performance of the network application systems in a high-speed network.展开更多
With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network str...With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.展开更多
The congestion control mechanisms of the current standard TCP constrain the congestion windows that can be achieved by TCP in high-speed networks, which leads to low link utilization. HSTCP is one solution to solve th...The congestion control mechanisms of the current standard TCP constrain the congestion windows that can be achieved by TCP in high-speed networks, which leads to low link utilization. HSTCP is one solution to solve this problem by modifying the congestion control mechanism to have the characteristics of TCP friendliness in high loss rate environment and high scalability in low loss rate environment. However, experiments revealed that HSTCP has severe RTT unfairness. After analyzing the RTT unfairness in HSTCP with a model, we proposed CW-HSTCP, which added a fair factor to decrease the difference of congestion window caused by different RTT. Fair factor of long RTT flows can cause a sharp window increment that is easy to cause a bursty traffic, so a method called block-pacing was adopted. Simulation results showed that our new proposal could alleviate the RTT unfairness while keeping advantages of HSTCP.展开更多
China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various rel...China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.展开更多
The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃...The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.展开更多
基金funded in part by the National Natural Science Foundation of China(62261024 and U2001213)in part by National Key Research and Development Project(2020YFB1807204)+2 种基金in part by Science and Technology Project of Education Department of Jiangxi Province(GJJ214606 and GJJ2205201)in part by Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education,P.R.China(KFKT-2022101)in part by the Jiangxi Provincial Natural Science Foundation(20212BAB212001)。
文摘The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development.Due to the considerable mobility of trains and the complex nature of the environment,the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics,which presents significant hurdles in terms of channel estimation.In addition,the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation.To address the aforementioned issues,this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network.In this method,the time-frequency response of the channel is considered as a two-dimensional image.The Fast Super-Resolution Convolution Neural Network(FSRCNN)is used to first reconstruct channel images.Next,the Denoising Convolution Neural Network(DnCNN)is applied to reduce the channel noise and improve the accuracy of channel estimation.Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method,while also exhibiting reduced algorithmic complexity.
文摘High-speed wireless networks such as IEEE 802.11n have been introduced based on IEEE 802.11 to meet the growing demand for high-throughput and multimedia applications. It is known that the medium access control （MAC） efficiency of IEEE 802.11 decreases with increasing the physical rate. To improve efficiency, few solutions have been proposed such as Aggregation to concatenate a number of packets into a larger frame and send it at once to reduce the protocol overhead. Since transmitting larger frames eventuates to dramatic delay and jitter increase in other nodes, bounding the maxi- mum aggregated frame size is important to satisfy delay requirements of especially multimedia applications. In this paper, we propose a scheme called Optimized Packet Aggregation （OPA） which models the network by constrained convex optimization to obtain the optimal aggregation size of each node regarding to delay constraints of other nodes. OPA attains proportionally fair sharing of the channel while satisfying delay constrains. Furthermore, reaching the optimal point is guaranteed in OPA with low complexity. Simulation results show that OPA can successfully bound delay and meet the requirements of nodes with only an insignificant throughput penalty due to limiting the aggregation size even in dynamic conditions.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,grant number RG-2-611-42(A.O.A.).
文摘Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金supported by National Natural Science Foundation of China(Nos.62161016,61661025)Gansu Provincial Science and Technology Plan(No.20JR10RA273)。
文摘In order to ensure the uninterrupted communication between high-speed train and base station,driving safety and satisfying online experience of passengers,a dual-link switching algorithm based on CNN-WaveNet decision parameter multi-step prediction model is proposed to establish a two-hop relay communication system model between the high-speed train and the base station.Firstly,the switching algorithm uses convolution neural network(CNN)to extract the time sequence characteristics of decision parameters.Then,it learns the mapping relationship between feature information and decision parameters based on WaveNet and combining with rolling prediction method to realize multi-step prediction of decision parameters.Finally,dual-antenna communication mode is adopted to realize dual-link communication.The simulation results show that the proposed handover algorithm can improve handover trigger rate and handover success rate.
基金substantially supported by the National Natural Science Foundation of China under Grant No.62002263in part by Tianjin Municipal Education Commission Research Program Project under 2022KJ012Tianjin Science and Technology Program Projects:24YDTPJC00630.
文摘With technological advancements,high-speed rail has emerged as a prevalent mode of transportation.During travel,passengers exhibit a growing demand for streaming media services.However,the high-speed mobile networks environment poses challenges,including frequent base station handoffs,which significantly degrade wireless network transmission performance.Improving transmission efficiency in high-speed mobile networks and optimizing spatiotemporal wireless resource allocation to enhance passengers’media experiences are key research priorities.To address these issues,we propose an Adaptive Cross-Layer Optimization Transmission Method with Environment Awareness(ACOTM-EA)tailored for high-speed rail streaming media.Within this framework,we develop a channel quality prediction model utilizing Kalman filtering and an algorithm to identify packet loss causes.Additionally,we introduce a proactive base station handoffstrategy to minimize handoffrelated disruptions and optimize resource distribution across adjacent base stations.Moreover,this study presents a wireless resource allocation approach based on an enhanced genetic algorithm,coupled with an adaptive bitrate selection mechanism,to maximize passenger Quality of Experience(QoE).To evaluate the proposed method,we designed a simulation experiment and compared ACOTM-EA with established algorithms.Results indicate that ACOTM-EA improves throughput by 11%and enhances passengers’media experience by 5%.
基金Foundation： National Natural Science Foundation of China, No. 41271134
文摘Traffic is an indispensable prerequisite for a tourism system. The ＂four vertical and four horizontal＂ HSR network represents an important milestone of the ＂traffic revolution＂ in China. It will affect the spatial pattern of tourism accessibility in Chinese cities, thus substan- tially increasing their power to attract tourists and their radiation force. This paper examines the evolution and spatial characteristics of the power to attract tourism of cities linked by China＇s HSR network by measuring the influence of accessibility of 338 HSR-linked cities using GIS analysis. The results show the following. （1） The accessibility of Chinese cities is optimized by the HSR network, whose spatial pattern of accessibility exhibits an obvious traf- fic direction and causes a high-speed rail-corridor effect. （2） The spatial pattern of tourism field strength in Chinese cities exhibits the dual characteristics of multi-center annular diver- gence and dendritic diffusion. Dendritic diffusion is particularly more obvious along the HSR line. The change rate of urban tourism field strength forms a high-value corridor along the HSR line and exhibits a spatial pattern of decreasing area from the center to the outer limit along the HSR line. （3） The influence of the higher and highest tourism field strength areas along the HSR line is most significant, and the number of cities that distribute into these two types of tourism field strengths significantly increases： their area expands by more than 100% HSR enhances the tourism field strength value of regional central cities, and the radiation range of tourism attraction extends along the HSR line.
文摘Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.
基金National Key R&D Program(Grant No.2020YFB2007700),National Natural Science Foundation of China(Grant Nos.11790282,12032017,12002221 and 11872256)S&T Program of Hebei(Grant No.20310803D)+1 种基金Natural Science Foundation of Hebei Province(Grant No.A2020210028)State Foundation for Studying Abroad.
文摘High-speed trains often use temperature sensors to monitor the motion state of bearings.However,the temperature of bearings can be affected by factors such as weather and faults.Therefore,it is necessary to analyze in detail the relationship between the bearing temperature and influencing factors.In this study,a dynamics model of the axle box bearing of high-speed trains is established.The model can obtain the contact force between the rollers and raceway and its change law when the bearing contains outer-ring,inner-ring,and rolling-element faults.Based on the model,a thermal network method is introduced to study the temperature field distribution of the axle box bearings of high-speed trains.In this model,the heat generation,conduction,and dispersion of the isothermal nodes can be solved.The results show that the temperature of the contact point between the outer-ring raceway and rolling-elements is the highest.The relationships between the node temperature and the speed,fault type,and fault size are analyzed,finding that the higher the speed,the higher the node temperature.Under different fault types,the node temperature first increases and then decreases as the fault size increases.The effectiveness of the model is demonstrated using the actual temperature data of a high-speed train.This study proposes a thermal network model that can predict the temperature of each component of the bearings on a high-speed train under various speed and fault conditions.
基金supported by King Saud University through Researchers Supporting Project number(RSPD2024R1006),King Saud University,Riyadh,Saudi Arabia.
文摘This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900504).
文摘In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.
文摘It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.
基金The National Natural Science Foundation of China(No.51465035)the Natural Science Foundation of Gansu,China(No.20JR5R-A466)。
文摘To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is proposed.First,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is constructed.Then,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN models.The experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and robustness.The presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.
基金supported by International Science and Technology Cooperation project(Grant No.2008DFA71750)
文摘In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate(MRR).After the advent of high-speed milling(HSM)pro cess,lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters.It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism.In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network(FNN)so as to have the most effective knowledge-base for given set of data.Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters.A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed.After training process,raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules.The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.
基金Sponsored by the National High Technology Development Program of China (Grant No. 2002AA142020).
文摘With the increasing enlargement of network scale and the rapid development of network techniques, large numbers of the network applications begin to appear. Packet capture plays an important role as one basic technique used in each field of the network applications. In a high-speed network, the heavy traffic of network transmission challenges the packet capture techniques. This paper does an in-depth analysis on the traditional packet capture mechanisms in Linux, and then measures the performance bottleneck in the process of packet capture. The methods for improving the packet capture performance are presented and an optimized packet capture scheme is also designed and implemented. The test demonstrates that the new packet capture mechanism (Libpacket) can greatly improve the packet capture performance of the network application systems in a high-speed network.
基金National High-Tech Research and Development Program of China (863 Program) (No.2007AA01Z309)
文摘With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.
文摘The congestion control mechanisms of the current standard TCP constrain the congestion windows that can be achieved by TCP in high-speed networks, which leads to low link utilization. HSTCP is one solution to solve this problem by modifying the congestion control mechanism to have the characteristics of TCP friendliness in high loss rate environment and high scalability in low loss rate environment. However, experiments revealed that HSTCP has severe RTT unfairness. After analyzing the RTT unfairness in HSTCP with a model, we proposed CW-HSTCP, which added a fair factor to decrease the difference of congestion window caused by different RTT. Fair factor of long RTT flows can cause a sharp window increment that is easy to cause a bursty traffic, so a method called block-pacing was adopted. Simulation results showed that our new proposal could alleviate the RTT unfairness while keeping advantages of HSTCP.
基金Project supported by the National Key Research and Development Program of China(Grant No.2019YFF0301400)the National Natural Science Foundation of China(Grant Nos.61671031,61722102,41722103,and 61961146005)。
文摘China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.
文摘The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.
