针对传统轨道角动量(Orbital Angular Momentum,OAM)通信系统难以在视距信道受阻塞的非视距环境中正常工作以及无法有效保障多用户的服务质量(Quality of Service,QoS)需求问题,文中基于智能反射表面辅助技术将多用户的非视距信道转化...针对传统轨道角动量(Orbital Angular Momentum,OAM)通信系统难以在视距信道受阻塞的非视距环境中正常工作以及无法有效保障多用户的服务质量(Quality of Service,QoS)需求问题,文中基于智能反射表面辅助技术将多用户的非视距信道转化为等效的视距信道,并在此场景下提出基于太赫兹多用户OAM正交频分多址系统下行资源优化方法。基于双层迭代资源分配算法将非凸联合优化的求解分解成外部和内部两个优化流程,基于交替优化和凸优化理论逐一求解4个核心子问题,实现各用户QoS差异化保障下的系统容量最大化。仿真结果表明,所提方法在通信资源充足时对各用户的QoS需求保障率为100%。在反射单元数量为768时,所提系统比传统OAM系统的系统容量平均提高了19.1%,并且误码率更低。在用户数量为3、信噪比为20 dB时,相较于基于相位补偿的MU(Multiuser)-OAM系统,所提系统的误码率下降了40.5%。展开更多
自由空间无线激光通信网络易受大气湍流、天气变化等动态扰动,使激光波长、功率、发散角等关键参数时变.传统静态参数模型难以精准表征其对信号传输质量(Quality of Service,QoS)的动态影响,进而导致路由决策偏差、链路不稳.为此,该文...自由空间无线激光通信网络易受大气湍流、天气变化等动态扰动,使激光波长、功率、发散角等关键参数时变.传统静态参数模型难以精准表征其对信号传输质量(Quality of Service,QoS)的动态影响,进而导致路由决策偏差、链路不稳.为此,该文提出一种基于QoS的自由空间无线激光通信网络路由设计方法.该方法通过构建二元有向图模型表示节点分布,采用模糊自适应方法确定加权系数,结合灰色预测和空间加权聚类分析算法获得参数识别系数,识别激光器发射关键参数.完成节点定位与参数识别后,进一步分析激光器发射特性参数与QoS的关联性,构建以路径成本最小化为目标的多约束自由空间无线激光通信网络路由模型,引入Adam优化算法实时监测和分析链路特性,自适应调整数据流阈值.引入改进蚁群算法求解多约束优化问题,从而实现网络路由设计.实验结果表明,应用所提方法,网络的带宽利用率达到97%,网络吞吐量达到72.3 MB/s,可保证网络通信的连续性,应用效果较好.展开更多
随着信息技术的快速发展,电视台总控系统的IP化改造已成为必然趋势,但在改造过程中仍面临着诸多挑战,其中服务质量(Quality of Service,QoS)保障尤为关键。本文旨在探讨电视台总控系统IP化改造中的QoS保障策略,首先对电视台总控系统IP...随着信息技术的快速发展,电视台总控系统的IP化改造已成为必然趋势,但在改造过程中仍面临着诸多挑战,其中服务质量(Quality of Service,QoS)保障尤为关键。本文旨在探讨电视台总控系统IP化改造中的QoS保障策略,首先对电视台总控系统IP化改造和QoS保障技术进行概述;其次详细阐述了QoS保障的具体策略,包括网络架构优化、流量分级管理、设备选型与配置等方面,结合嵊州电视台NDI总控系统改造案例分析,验证了所提出策略的有效性和可行性;最后得出结论,为电视台总控系统IP化改造中的QoS保障提供参考和借鉴。展开更多
现有研究在多QoS(quality of service)调度问题中,由于仅依赖即时奖励反馈机制,在资源受限的场景下处理时延敏感数据和具有连续传输需求的媒体数据时,存在可扩展性差和资源浪费的问题。为此,提出了一种基于奖励回溯的DQN(reward backtra...现有研究在多QoS(quality of service)调度问题中,由于仅依赖即时奖励反馈机制,在资源受限的场景下处理时延敏感数据和具有连续传输需求的媒体数据时,存在可扩展性差和资源浪费的问题。为此,提出了一种基于奖励回溯的DQN(reward backtracking based deep Q-network,RB-DQN)算法。该算法通过未来时刻的交互来回溯调整当前状态的策略评估,以更加有效地识别并解决因不合理调度策略导致的丢包。同时,设计了一种时延-吞吐均衡度量(latency throughput trade-off,LTT)指标,该指标综合考虑了时延敏感数据和媒体类型数据的业务需求,并可通过权重调整来突出不同的侧重点。大量仿真结果表明,与其他调度策略相比,所提算法能够有效降低时延敏感数据的延迟和抖动,同时确保媒体类型数据的流畅性与稳定性。展开更多
Wireless sensor network(WSN)technologies have advanced significantly in recent years.With in WSNs,machine learning algorithms are crucial in selecting cluster heads(CHs)based on various quality of service(QoS)metrics....Wireless sensor network(WSN)technologies have advanced significantly in recent years.With in WSNs,machine learning algorithms are crucial in selecting cluster heads(CHs)based on various quality of service(QoS)metrics.This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem(TSP)to locate the optimal path traversed by the Mobile Data Collector(MDC),in terms of energy and QoS efficiency.To bemore specific,to minimize energy consumption in the CH election stage,we have developed the M-T protocol using the K-Means and the grid clustering algorithms.In addition,to improve the transmission phase of the Low Energy Adaptive Clustering-Grid-KMeans(LEACH-G-K)protocol,the MDC is employed as an intermediary between the CH and the sink to improve the wireless sensor network(WSN)QoS.The results of the experiment demonstrate that the M-T protocol enhances various Low Energy Adaptive Clustering protocol(LEACH)improvements such as the LEACH-G-K,LEACH-C,Threshold sensitive Energy Efficient Sensor Networks(TEEN),MDC maximum residual energy leach protocol.展开更多
Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To sa...Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.展开更多
为了解决现有路由算法无法学习历史路由决策经验导致的网络负载不均衡问题,将强化学习技术引入软件定义网络(Software Defined Network,SDN)的服务质量(Quality of Service,QoS)路由问题,提出一种基于强化学习的多业务智能QoS路由方法MD...为了解决现有路由算法无法学习历史路由决策经验导致的网络负载不均衡问题,将强化学习技术引入软件定义网络(Software Defined Network,SDN)的服务质量(Quality of Service,QoS)路由问题,提出一种基于强化学习的多业务智能QoS路由方法MDQN(Multi-service QoS routing method based on DeepQ Network)。该方法部署在SDN控制器中,能学习历史决策经验,并在网络状态发生变化时及时调整路径。通过在SDN中部署该方法,有效平衡了网络负载,增加了网络的吞吐量,为SDN中的QoS路由问题提供了一种有效的解决方案。展开更多
Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitat...Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters.Efficient network traffic management remains a challenge,with existing approaches often resulting in increased delay and reduced throughput.Additionally,insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios.Several routing strategies and procedures have been developed to effectively reduce communication-related energy consumption based on the selection of relay nodes.The relay node selection is essential for data transmission in WBAN.This paper introduces an Adaptive Relay-Assisted Protocol(ARAP)for WBAN,a hybrid routing protocol designed to optimize energy use and Quality of Service(QoS)metrics such as network longevity,latency,throughput,and residual energy.ARAP employs neutrosophic relay node selection techniques,including the Analytic Hierarchy Process(AHP)and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)to optimally resolve data and decision-making uncertainties.The protocol was compared with existing protocols such as Low-Energy Adaptive Clustering Hierarchy(LEACH),Modified-Adaptive Threshold Testing and Evaluation Methodology for Performance Testing(M-ATTEMPT),Wireless Adaptive Sampling Protocol(WASP),and Tree-Based Multicast Quality of Service(TMQoS).The comparative results show that the ARAP significantly outperformed these protocols in terms of network longevity and energy efficiency.ARAP has lower communication cost,better throughput,reduced delay,increased network lifetime,and enhanced residual energy.The simulation results indicate that the proposed approach performed better than the conventional methods,with 68%,62%,25%,and 50%improvements in network longevity,residual energy,throughput,and latency,respectively.This significantly improves the functional lifespan of WBAN and makes them promising candidates for sophisticated health monitoring systems.展开更多
以广东省某智慧城市项目为例,探讨业务网中基于资源预留与拥塞控制的混合服务质量(quality of service, QoS)保障机制设计。该项目通过构建“静态资源预留+动态拥塞控制”架构,结合多协议标签交换-流量工程带宽预留、差异服务标记及人...以广东省某智慧城市项目为例,探讨业务网中基于资源预留与拥塞控制的混合服务质量(quality of service, QoS)保障机制设计。该项目通过构建“静态资源预留+动态拥塞控制”架构,结合多协议标签交换-流量工程带宽预留、差异服务标记及人工智能驱动的主动拥塞避免算法,实现关键业务时延降低40%、拥塞发生率下降65%,有效平衡资源利用率与服务可靠性,可为智慧城市、工业互联网等场景提供QoS保障范式。展开更多
Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,...Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.展开更多
文摘自由空间无线激光通信网络易受大气湍流、天气变化等动态扰动,使激光波长、功率、发散角等关键参数时变.传统静态参数模型难以精准表征其对信号传输质量(Quality of Service,QoS)的动态影响,进而导致路由决策偏差、链路不稳.为此,该文提出一种基于QoS的自由空间无线激光通信网络路由设计方法.该方法通过构建二元有向图模型表示节点分布,采用模糊自适应方法确定加权系数,结合灰色预测和空间加权聚类分析算法获得参数识别系数,识别激光器发射关键参数.完成节点定位与参数识别后,进一步分析激光器发射特性参数与QoS的关联性,构建以路径成本最小化为目标的多约束自由空间无线激光通信网络路由模型,引入Adam优化算法实时监测和分析链路特性,自适应调整数据流阈值.引入改进蚁群算法求解多约束优化问题,从而实现网络路由设计.实验结果表明,应用所提方法,网络的带宽利用率达到97%,网络吞吐量达到72.3 MB/s,可保证网络通信的连续性,应用效果较好.
文摘随着信息技术的快速发展,电视台总控系统的IP化改造已成为必然趋势,但在改造过程中仍面临着诸多挑战,其中服务质量(Quality of Service,QoS)保障尤为关键。本文旨在探讨电视台总控系统IP化改造中的QoS保障策略,首先对电视台总控系统IP化改造和QoS保障技术进行概述;其次详细阐述了QoS保障的具体策略,包括网络架构优化、流量分级管理、设备选型与配置等方面,结合嵊州电视台NDI总控系统改造案例分析,验证了所提出策略的有效性和可行性;最后得出结论,为电视台总控系统IP化改造中的QoS保障提供参考和借鉴。
文摘现有研究在多QoS(quality of service)调度问题中,由于仅依赖即时奖励反馈机制,在资源受限的场景下处理时延敏感数据和具有连续传输需求的媒体数据时,存在可扩展性差和资源浪费的问题。为此,提出了一种基于奖励回溯的DQN(reward backtracking based deep Q-network,RB-DQN)算法。该算法通过未来时刻的交互来回溯调整当前状态的策略评估,以更加有效地识别并解决因不合理调度策略导致的丢包。同时,设计了一种时延-吞吐均衡度量(latency throughput trade-off,LTT)指标,该指标综合考虑了时延敏感数据和媒体类型数据的业务需求,并可通过权重调整来突出不同的侧重点。大量仿真结果表明,与其他调度策略相比,所提算法能够有效降低时延敏感数据的延迟和抖动,同时确保媒体类型数据的流畅性与稳定性。
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5C2A07096111).
文摘Wireless sensor network(WSN)technologies have advanced significantly in recent years.With in WSNs,machine learning algorithms are crucial in selecting cluster heads(CHs)based on various quality of service(QoS)metrics.This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem(TSP)to locate the optimal path traversed by the Mobile Data Collector(MDC),in terms of energy and QoS efficiency.To bemore specific,to minimize energy consumption in the CH election stage,we have developed the M-T protocol using the K-Means and the grid clustering algorithms.In addition,to improve the transmission phase of the Low Energy Adaptive Clustering-Grid-KMeans(LEACH-G-K)protocol,the MDC is employed as an intermediary between the CH and the sink to improve the wireless sensor network(WSN)QoS.The results of the experiment demonstrate that the M-T protocol enhances various Low Energy Adaptive Clustering protocol(LEACH)improvements such as the LEACH-G-K,LEACH-C,Threshold sensitive Energy Efficient Sensor Networks(TEEN),MDC maximum residual energy leach protocol.
基金National Key Research and Development Program(2021YFB2900604)。
文摘Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
文摘为了解决现有路由算法无法学习历史路由决策经验导致的网络负载不均衡问题,将强化学习技术引入软件定义网络(Software Defined Network,SDN)的服务质量(Quality of Service,QoS)路由问题,提出一种基于强化学习的多业务智能QoS路由方法MDQN(Multi-service QoS routing method based on DeepQ Network)。该方法部署在SDN控制器中,能学习历史决策经验,并在网络状态发生变化时及时调整路径。通过在SDN中部署该方法,有效平衡了网络负载,增加了网络的吞吐量,为SDN中的QoS路由问题提供了一种有效的解决方案。
文摘Wireless Body Area Network(WBAN)is essential for continuous health monitoring.However,they face energy efficiency challenges due to the low power consumption of sensor nodes.Current WBAN routing protocols face limitations in strategically minimizing energy consumption during the retrieval of vital health parameters.Efficient network traffic management remains a challenge,with existing approaches often resulting in increased delay and reduced throughput.Additionally,insufficient attention has been paid to enhancing channel capacity to maintain signal strength and mitigate fading effects under dynamic and robust operating scenarios.Several routing strategies and procedures have been developed to effectively reduce communication-related energy consumption based on the selection of relay nodes.The relay node selection is essential for data transmission in WBAN.This paper introduces an Adaptive Relay-Assisted Protocol(ARAP)for WBAN,a hybrid routing protocol designed to optimize energy use and Quality of Service(QoS)metrics such as network longevity,latency,throughput,and residual energy.ARAP employs neutrosophic relay node selection techniques,including the Analytic Hierarchy Process(AHP)and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)to optimally resolve data and decision-making uncertainties.The protocol was compared with existing protocols such as Low-Energy Adaptive Clustering Hierarchy(LEACH),Modified-Adaptive Threshold Testing and Evaluation Methodology for Performance Testing(M-ATTEMPT),Wireless Adaptive Sampling Protocol(WASP),and Tree-Based Multicast Quality of Service(TMQoS).The comparative results show that the ARAP significantly outperformed these protocols in terms of network longevity and energy efficiency.ARAP has lower communication cost,better throughput,reduced delay,increased network lifetime,and enhanced residual energy.The simulation results indicate that the proposed approach performed better than the conventional methods,with 68%,62%,25%,and 50%improvements in network longevity,residual energy,throughput,and latency,respectively.This significantly improves the functional lifespan of WBAN and makes them promising candidates for sophisticated health monitoring systems.
文摘以广东省某智慧城市项目为例,探讨业务网中基于资源预留与拥塞控制的混合服务质量(quality of service, QoS)保障机制设计。该项目通过构建“静态资源预留+动态拥塞控制”架构,结合多协议标签交换-流量工程带宽预留、差异服务标记及人工智能驱动的主动拥塞避免算法,实现关键业务时延降低40%、拥塞发生率下降65%,有效平衡资源利用率与服务可靠性,可为智慧城市、工业互联网等场景提供QoS保障范式。
文摘Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.
