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Optimizing Routing Algorithms for Next-Generation Networks:A Resilience-Driven Framework for Space-Air-Ground Integrated Networks 认领 引用
1
作者 Peiying Zhang Yihong Yu +3 位作者 Jia Luo Nguyen Gia Ba Lizhuang Tan Lei Shi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1177-1190,共14页
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ... Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field. 展开更多
关键词 Space-air-ground integrated network next-generation networks routing optimization resiliencedriven routing dynamic weighting multi-metric assessment
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I-AOMDV:QoS-Aware Multipath Routing Protocol for Enhanced in Dynamic Multi-Node Vehicle Area Network 认领 引用
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作者 Zhu Mingqiang Zhou Ying +4 位作者 Chu Jianxiang Chen Ziyang Ai Huilin Li Jundi Han Jinze 《China Communications》 SCIE EI CSCD 2026年第3期286-297,共12页
In vehicular networking applications,Mobile Ad hoc networks(MANETs)enable dynamic,infrastructure-free connectivity for multi-node mobile scenarios.Frequent topology changes,however,challenge routing protocols in deliv... In vehicular networking applications,Mobile Ad hoc networks(MANETs)enable dynamic,infrastructure-free connectivity for multi-node mobile scenarios.Frequent topology changes,however,challenge routing protocols in delivering quality of service(QoS)for diverse applications.We propose inhanced Ad-hoc on-demand distance vector multipath(I-AOMDV),an enhanced multipath routing protocol using a primary-backup strategy to meet stringent QoS demands in dynamic vehicular environments.Whereas AOMDV relies on hop count,I-AOMDV integrates hop count,bandwidth,and path stability into a QoS-aware framework for optimized path selection.Extensive NS-2 simulations demonstrate that IAOMDV surpasses AOMDV under high mobility,improving data packet delivery by up to 22%and cutting voice service latency by 8–15%,even in high-mobility scenarios.By addressing vehicular networking needs for latency,reliability,and bandwidth,I-AOMDV delivers a scalable,efficient routing solution. 展开更多
关键词 mobile ad hoc networks multipath routing path selection primary-cackup strategy
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Satellite handover strategies based on minimum routing hops for mega LEO satellite networks 认领 引用
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作者 ZHU Hongtao WANG Xinyu +2 位作者 WANG Zhenyong LI Dezhi GUO Qing 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期64-74,共11页
Mega low Earth orbit(LEO)satellite networks serve as effective complements to terrestrial networks.However,the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users.Moreover... Mega low Earth orbit(LEO)satellite networks serve as effective complements to terrestrial networks.However,the dual mobility of users and LEO satellites makes inter-satellite handovers more frequent for users.Moreover,there are both ascending and descending segments in widely deployed walker-delta constellations.Even if the locations of users do not change,when the access satellites of the communicating parties are not in the same ascending or descending segment,the end-to-end latency between them will increase.To address this challenge,the self-decision handover(SDH)strategy and the joint decision handover(JDH)strategy are proposed,and they both incorporate the routing hops as a crucial handover criterion to minimize the end-to-end latency.In addition,the shortest route hop-count algorithm is designed to assist in the handover decision-making process.Simulations demonstrate that the proposed handover strategies outperform the traditional handover strategies in terms of the number of handovers and end-to-end latency. 展开更多
关键词 mega low Earth orbit(LEO)satellite network walker-delta constellation self-decision handover joint decision handover minimum routing hop
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Photonic spiking reinforcement learning for intelligent routing 认领 引用
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作者 Shuiying Xiang Yonghang Chen +8 位作者 Ling Zheng Zhicong Tu Xintao Zeng Mengting Yu Shuai Wang Yahui Zhang Xingxing Guo Weitao Pan Yue Hao 《Opto-Electronic Science》 CAS 2026年第5期11-26,共16页
Intelligent routing plays a key role in modern communication infrastructure,including data centers,computing networks,and future 6G networks.Although reinforcement learning(RL)has shown great potential for intelligent... Intelligent routing plays a key role in modern communication infrastructure,including data centers,computing networks,and future 6G networks.Although reinforcement learning(RL)has shown great potential for intelligent routing,its practical deployment remains constrained by high energy consumption and decision latency.Here,we propose a photonic spiking RL architecture that implements a proximal policy optimization(PPO)–based intelligent routing algorithm.The performance of the proposed approach is systematically evaluated on a softwaredefined network(SDN)with a fat-tree topology.The results demonstrate that,under various baseline traffic rate conditions,the PPO-based routing strategy significantly outperforms the conventional Dijkstra algorithm in key performance metrics,including throughput,packet loss rate,average latency,and load balance.Furthermore,a hardware-software collaborative framework of the spiking Actor network is realized for three typical baseline traffic rates,utilizing a photonic synapse chip based on a Mach-Zehnder interferometer(MZI)array and a photonic spiking neuron chip based on distributed feedback lasers with a saturable absorber(DFB-SAs).Experimental validation on 640 state–action pairs shows that the inference accuracy of the hardware-software collaborative framework is consistent with that of the pure algorithmic implementation.The impacts of different hidden-layer scales in the spiking Actor network and varying network size of fat-tree topology are further analyzed.The integration of photonic spiking RL with SDN-based routing establishes a novel paradigm for intelligent routing optimization,featuring ultralow latency and high energy efficiency.This approach exhibits broad application prospects in real-time network optimization scenarios,including large-scale data centers,computing networks,satellite Internet systems,and future 6G networks. 展开更多
关键词 photonic spiking neural network spiking reinforcement learning intelligent routing SDN
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Joint Access Node Selection and Fast Routing Optimization for LEO Satellite Networks 认领 引用
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作者 Zhang Yifan Dong Tao +2 位作者 Liu Zhihui Di Hang Zhou Jianming 《China Communications》 SCIE EI CSCD 2026年第6期146-164,共19页
The low Earth orbit(LEO)satellite networks play an important role in the future communication networks.However,under the end-to-end(E2E)transmission background,inter-satellite routing has been widely studied,but the i... The low Earth orbit(LEO)satellite networks play an important role in the future communication networks.However,under the end-to-end(E2E)transmission background,inter-satellite routing has been widely studied,but the influence of ground-satellite links(GSL)on routing has received less attention.In this paper,a fast E2E satellite routing algorithm based on access node selection is proposed.Firstly,the delay of four path modes generated by users accessing the network from different satellites is analyzed,and the influence of delay on E2E routing performance is presented.Then,jointly considering routing delay and node load,an access node selection strategy is proposed by using the shortest E2E delay to determine the access source and destination node within satellites.Finally,an optimization domain is divided from the network topology by using the shortest delay path based on hops constraints.And a routing optimization algorithm based on Q-learning has been proposed in the optimization domain,realizing high computational speed and stable results.The simulation results show that the access node selection strategy can decrease E2E delay by up to 10 ms and enhance the performance of node load balancing.And the routing optimization algorithm can reduce the average computation time. 展开更多
关键词 access selection fast routing LEO satellite networks load balancing routing algorithm
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Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks:A Methodological Survey 认领 引用
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作者 Mohammad Shokouhifar Fakhrosadat Fanian +4 位作者 Mehdi Hosseinzadeh Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期191-255,共65页
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
关键词 Wireless sensor networks data transmission energy efficiency lifetime clustering routing optimization metaheuristic algorithms grey wolf optimizer
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iPAFAR: An Adaptive Pareto-Based NS-AAA Energy-Stable Fuzzy Clustering and Routing Framework for Smart City IoT-Enabled WSNs 认领 引用
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作者 Bhanu Talwar Puneet Thapar +3 位作者 Tahani Alsubait Mai Alduailij Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第8期785-805,共21页
Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery cap... Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery capacity,uneven energy consumption,and inefficient clustering and routing mechanisms significantly reduce network lifetime,reliability,and scalability,especially in large-scale IoT deployments.Traditional routing protocols often rely on single-objective optimization or static clustering strategies,which fail to maintain long-term energy balance and stable communication performance.To address these challenges,this paper proposes iPAFAR,a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs.The proposed model formulates cluster-head selection as a multi-objective optimization problem that considers residual energy,node centrality,load variance,and fairness.A Non-Dominated Sorting Artificial Algae Algorithm(NS-AAA)is used to obtain Pareto-optimal cluster-head configurations,followed by a fuzzy inference system for refined decision-making.To ensure long-term energy stability,a Lyapunov-based routing model is incorporated,and an adaptive re-clustering mechanism is introduced to reduce unnecessary control overhead under dynamic network conditions.The performance of the proposed framework is evaluated through MATLAB-based simulations and compared with existing protocols,including LEACH-M,ME-LEACH,FQA,MKNDPC,RANP-PSO,and BKA-TOA.Experimental results show that iPAFAR achieves approximately 40%lower end-to-end delay,15%–20%higher packet delivery ratio,and 45%–50%improvement in residual energy while maintaining nearly twice the number of active nodes after 1000 simulation rounds.These results confirm that the proposed framework provides improved energy efficiency,load balancing,and routing stability,making it suitable for long-term smart city IoT deployments. 展开更多
关键词 Wireless sensor networks smart cities multi-objective optimization artificial algae algorithm energy-aware clustering pareto optimization IoT routing
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Solving Multi-Depot Vehicle Routing Problems with Dynamic Customer Demand Using a Scheduling System TS-DPU Based on TS-ACO 认领 引用
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作者 Tsu-Yang Wu Chengyuan Yu +2 位作者 Yanan Zhao Saru Kumari Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2026年第3期2244-2268,共25页
With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used ... With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance. 展开更多
关键词 Dynamic vehicle routing multiple depots ant colony optimization temporal-spatial distance time slice
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Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks 认领 引用
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作者 Mehran Tarif Mohammadhossein Homaei +1 位作者 Abbas Mirzaei Babak Nouri-Moghaddam 《Computers, Materials & Continua》 SCIE EI 2026年第4期2095-2126,共32页
The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy... The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy usage,unstable links,and inefficient routing decisions,which reduce the overall network performance and lifetime.In this work,we introduce TABURPL,an improved routing method that applies Tabu Search(TS)to optimize the parent selection process.The method uses a combined cost function that considers Residual Energy,Transmission Energy,Distance to the Sink,Hop Count,Expected Transmission Count(ETX),and Link Stability Rate(LSR).Simulation results show that TABURPL improves link stability,lowers energy consumption,and increases the packet delivery ratio compared with standard RPL and other existing approaches.These results indicate that Tabu Search can handle the complex trade-offs in IoT routing and can provide a more reliable solution for extending the network lifetime. 展开更多
关键词 Internet of things RPL protocol tabu search energy efficiency link stability multi-metric routing
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A routing strategy for non-uniform spatial network 认领 引用
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作者 Yicong Ye Yongxiang Xia Haicheng Tu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期875-885,共11页
As network scales continue to expand,congestion has emerged as a critical issue in the study of complex networks,particularly in spatial networks such as transportation,aviation,and communication systems,where transmi... As network scales continue to expand,congestion has emerged as a critical issue in the study of complex networks,particularly in spatial networks such as transportation,aviation,and communication systems,where transmission is the primary function.To address this challenge,various routing strategies have been proposed to alleviate congestion by adjusting transmission paths.However,most of these strategies are based on network models that assume a uniform spatial distribution of nodes,which fails to accurately represent the non-uniform distributions observed in real-world networks.In this paper,we construct a more realistic non-uniform spatial network model and propose a novel routing strategy,termed the FH routing strategy,which integrates distance-based degree and harmonic centrality.Simulation results show that the FH strategy effectively avoids high-load nodes,promotes a more balanced load distribution,and significantly improves traffic throughput compared to traditional routing strategies.These findings provide theoretical support and practical guidance for optimizing information transmission in real-world non-uniform spatial networks. 展开更多
关键词 spatial network routing strategy network congestion complex network
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Predictive-Q Learning Based Interference-and-Mobility Aware Dual-Path Routing for UAV Swarm Networks with Mobile Edge Computing 认领 引用
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作者 Zhihao Dong Huakui Sun +1 位作者 Yueyue Tao Daosen Zhai 《Computers, Materials & Continua》 SCIE EI 2026年第9期1774-1791,共18页
High-mobility Unmanned Aerial Vehicle(UAV)swarm networks suffer from fast-varying connectivity and interference,and therefore routing decisions must jointly account for link instability and topology changes.By leverag... High-mobility Unmanned Aerial Vehicle(UAV)swarm networks suffer from fast-varying connectivity and interference,and therefore routing decisions must jointly account for link instability and topology changes.By leveraging mobile edge computing(MEC)capabilities,each UAV can perform online routing decisions locally without relying on centralized controllers.This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility,where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs,predicts link lifetime from relative motion,and anticipates forward connectivity and neighbor redundancy.To further enhance reliability under harsh interference,we extend Predictive-Q with a lightweight dual-path forwarding mechanism.Specifically,it conditionally splits a backup routing when the primary next hop becomes unreliable,and terminates the backup early when continued forwarding is unlikely to be beneficial,thereby controlling overhead.Simulation results demonstrate that,compared with existing methods,the proposed Predictive-Q-Dual improves packet delivery ratio by more than 15%over GPSR under strong interference while maintaining low delay and energy consumption across varying interference intensity,node density,and mobility speed. 展开更多
关键词 UAV swarm networks routing reinforcement learning dual-path forwarding mobile edge computing
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基于solidworks routing的水下管汇布管设计 认领 引用
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作者 王康 杨成鹏 +2 位作者 姬兴达 路淑云 蔡朵 《石油和化工设备》 CAS 2026年第5期80-84,共5页
在水下生产系统中,水下管汇作为核心枢纽,其液压管线的合理设计与布置直接决定了整个系统的控制精度和运行稳定性。液压管线作为传递液压动力、控制管汇阀门启闭的关键通路,其布置方案尤为重要。本文以南海某深水水下管汇工程项目为研... 在水下生产系统中,水下管汇作为核心枢纽,其液压管线的合理设计与布置直接决定了整个系统的控制精度和运行稳定性。液压管线作为传递液压动力、控制管汇阀门启闭的关键通路,其布置方案尤为重要。本文以南海某深水水下管汇工程项目为研究对象,系统应用SolidWorks软件的Routing插件,构建了一套从布置原则制定、接口信息确认,到三维管路建模与优化排布的全流程设计方法。通过对小管尺寸选型、弯曲参数控制、支撑结构优化及空间适配性分析,有效解决了水下管汇有限空间内小管弯制难度大、焊接效率低、振动干扰等工程痛点。 展开更多
关键词 液压管线 布置 routing
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Joint Handover and Routing Optimization via Heterogeneous GNN-Enhanced DRL for Satellite-Ground Integrated Networks 认领 引用
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作者 Jiang Jiayi Deng Xiaofei +1 位作者 Wang Denghao Zhang Xiaoning 《China Communications》 SCIE EI CSCD 2026年第4期196-213,共18页
The satellite-ground integrated Networks(SGIN)emerge as a promising paradigm to extend the coverage and resilience of terrestrial networks.However,the high mobility and intermittent connectivity of satellites lead to ... The satellite-ground integrated Networks(SGIN)emerge as a promising paradigm to extend the coverage and resilience of terrestrial networks.However,the high mobility and intermittent connectivity of satellites lead to inevitable ground-satellite handovers.Existing handover algorithms often overlook the inherent interdependence between ground-satellite handover and inter-satellite routing,resulting in suboptimal performance and degraded quality of service(QoS).To address these issues,we propose a heterogeneous graph neural networks-enhanced deep reinforcement learning(HGRL)algorithm for joint handover and routing optimization.First,we propose the semantic-based heterogeneous graph neural networks(SHGNN)to model SGIN as a heterogeneous graph,capturing the intricate relationships between handover and routing through diverse representations of nodes and edges.Then,we embed the SHGNN into a deep reinforcement learning(DRL)framework,enabling QoS-aware decisions for both ground-satellite handover and inter-satellite routing.Additionally,a non-dominated crowding sorting(NCS)mechanism is proposed to prune alternative paths while balancing multiple QoS objectives.Finally,extensive simulations in NS3 show that HGRL outperforms state-of-the-art algorithms,reducing the handover times and average delay by 63.63%and 36.85%,and improving the average throughput by 26.53%. 展开更多
关键词 deep reinforcement learning graph neural networks handover routing satellite-ground integrated networks
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Joint Optimization of Routing and Resource Allocation in Decentralized UAV Networks Based on DDQN and GNN 认领 引用
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作者 Nawaf Q.H.Othman YANG Qinghai JIANG Xinpei 《电讯技术》 北大核心 2026年第1期1-10,共10页
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin... Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks. 展开更多
关键词 decentralized UAV network resource allocation routing algorithm GNN DDQN DRL
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基于Segment Routing的“一网双平面”网络架构研究 认领 引用
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作者 施雪峰 《今日自动化》 2026年第8期6-8,共3页
针对金融、政务及工业互联网等领域对承载网络高可靠性、业务隔离性与运维简化的迫切需求,传统单平面架构在故障恢复时效、业务带宽保障及控制平面复杂度方面存在显著瓶颈。文章提出了一种基于SR的“一网双平面”网络架构,通过构建双平... 针对金融、政务及工业互联网等领域对承载网络高可靠性、业务隔离性与运维简化的迫切需求,传统单平面架构在故障恢复时效、业务带宽保障及控制平面复杂度方面存在显著瓶颈。文章提出了一种基于SR的“一网双平面”网络架构,通过构建双平面物理拓扑与差异化业务承载策略,实现关键业务与普通业务的跨平面精细化隔离调度与路径可预期控制,以期在保障业务连续性的同时简化网络运维,为高可靠承载网络提供兼具理论创新性与工程实用性的解决方案。 展开更多
关键词 一网双平面 Segment Routing 网络架构 SR Policy
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CrowdRouting:Trustworthy and customized cross-domain routing based on crowdsourcing 认领 引用
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作者 Jialei Zhang Zheng Yan +1 位作者 Huidong Dong Peng Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第3期734-756,共23页
Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current so... Cross-domain routing in Integrated Heterogeneous Networks(Inte-HetNet)should ensure efficient and secure data transmission across different network domains by satisfying diverse routing requirements.However,current solutions face numerous challenges in continuously ensuring trustworthy routing,fulfilling diverse requirements,achieving reasonable resource allocation,and safeguarding against malicious behaviors of network operators.We propose CrowdRouting,a novel cross-domain routing scheme based on crowdsourcing,dedicated to establishing sustained trust in cross-domain routing,comprehensively considering and fulfilling various customized routing requirements,while ensuring reasonable resource allocation and effectively curbing malicious behavior of network operators.Concretely,CrowdRouting employs blockchain technology to verify the trustworthiness of border routers in different network domains,thereby establishing sustainable and trustworthy crossdomain routing based on sustained trust in these routers.In addition,CrowdRouting ingeniously integrates a crowdsourcing mechanism into the auction for routing,achieving fair and impartial allocation of routing rights by flexibly embedding various customized routing requirements into each auction phase.Moreover,CrowdRouting leverages incentive mechanisms and routing settlement to encourage network domains to actively participate in cross-domain routing,thereby promoting optimal resource allocation and efficient utilization.Furthermore,CrowdRouting introduces a supervisory agency(e.g.,undercover agent)to effectively suppress the malicious behavior of network operators through the game and interaction between the agent and the network operators.Through comprehensive experimental evaluations and comparisons with existing works,we demonstrate that CrowdRouting excels in providing trustworthy and fine-grained customized routing services,stimulating active participation in cross-domain routing,inhibiting malicious operator behavior,and maintaining reasonable resource allocation,all of which outperform baseline schemes. 展开更多
关键词 Trustworthy routing Cross-domain routing Customized routing Crowdsourcing
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C-SPPO:A deep reinforcement learning framework for large-scale dynamic logistics UAV routing problem 认领 引用 被引量：2
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作者 Fei WANG Honghai ZHANG +2 位作者 Sen DU Mingzhuang HUA Gang ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第5期296-316,共21页
Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-S... Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-SPPO)based on centralized decision process and considering policy entropy(S)is proposed.The proposed framework aims to plan the best scheduling scheme with the objective of minimizing both the timeout of order requests and the flight impact of UAVs that may lead to conflicts.In this framework,the intents of matching act are generated through the observations of UAV agents,and the ultimate conflict-free matching results are output under the guidance of a centralized decision maker.Concurrently,a pre-activation operation is introduced to further enhance the cooperation among UAV agents.Simulation experiments based on real-world data from New York City are conducted.The results indicate that the proposed CSPPO outperforms the baseline algorithms in the Average Delay Time(ADT),the Maximum Delay Time(MDT),the Order Delay Rate(ODR),the Average Flight Distance(AFD),and the Flight Impact Ratio(FIR).Furthermore,the framework demonstrates scalability to scenarios of different sizes without requiring additional training. 展开更多
关键词 Unmanned aerial vehicle Vehicle routing problem Orderdelivery Reinforcement learning Multi-agent Proximal policy optimization
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An Energy Optimization Algorithm for WRSN Nodes Based on Regional Partitioning and Inter-Layer Routing 认领 引用 被引量：1
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作者 Cui Zhang Lieping Zhang +2 位作者 Huaquan Gan Hongyuan Chen Zhihao Li 《Computers, Materials & Continua》 SCIE EI 2025年第8期3125-3148,共24页
In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorith... In large-scaleWireless Rechargeable SensorNetworks(WRSN),traditional forward routingmechanisms often lead to reduced energy efficiency.To address this issue,this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing.The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area.Then,the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission.Relay nodes are selected layer by layer,starting from the outermost cluster heads.Finally,the inter-layer routing mechanism is integrated with regional partitioning and clustering methods to develop the WRSN energy optimization algorithm.To further optimize the algorithm’s performance,we conduct parameter optimization experiments on the relay routing selection function,cluster head rotation energy threshold,and inter-layer relay structure selection,ensuring the best configurations for energy efficiency and network lifespan.Based on these optimizations,simulation results demonstrate that the proposed algorithm outperforms traditional forward routing,K-CHRA,and K-CLP algorithms in terms of node mortality rate and energy consumption,extending the number of rounds to 50%node death by 11.9%,19.3%,and 8.3%in a 500-node network,respectively. 展开更多
关键词 Wireless rechargeable sensor network regional partitioning inter-layer routing energy optimization
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Real-Time and Energy-Aware UAV Routing:A Scalable DAR Approach for Future 6G Systems 认领 引用
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作者 Khadija Slimani Samira Khoulji +1 位作者 Hamed Taherdoost Mohamed Larbi Kerkeb 《Computers, Materials & Continua》 SCIE EI 2025年第12期4667-4686,共20页
The integration of the dynamic adaptive routing(DAR)algorithm in unmanned aerial vehicle(UAV)networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6... The integration of the dynamic adaptive routing(DAR)algorithm in unmanned aerial vehicle(UAV)networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G.DAR’s innovative framework incorporates real-time path adjustments,energy-aware routing,and predictive models,optimizing reliability,latency,and energy efficiency in UAV operations.This study demonstrated DAR’s superior performance in dynamic,large-scale environments,proving its adaptability and scalability for real-time applications.As 6G networks evolve,challenges such as bandwidth demands,global spectrum management,security vulnerabilities,and financial feasibility become prominent.DAR aligns with these demands by offering robust solutions that enhance data transmission while ensuring network reliability.However,obstacles like global route optimization and signal interference in urban areas necessitate further refinement.Future directions should explore hybrid approaches,the integration of machine learning,and comprehensive real-world testing to maximize DAR’s capabilities.The findings underscore DAR’s pivotal role in enabling efficient and sustainable UAV communication systems,contributing to the broader landscape of wireless technology and laying a foundation for the seamless transition to 6G networks. 展开更多
关键词 Dynamic adaptive routing(DAR) energy-aware routing scalability and reliability UAV networks 6G communication systems
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A distributed on-demand bandwidth-constrained multicast routing protocol for wireless ad hoc networks 认领 引用 被引量：1
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作者 余燕平 倪玲玲 郑元琰 《Journal of Southeast University(English Edition)》 EI CAS 2015年第1期5-11,共7页
To meet the bandwidth requirement for the multicasting data flow in ad hoc networks, a distributed on- demand bandwidth-constrained multicast routing （BCMR） protocol for wireless ad hoc networks is proposed. With th... To meet the bandwidth requirement for the multicasting data flow in ad hoc networks, a distributed on- demand bandwidth-constrained multicast routing （BCMR） protocol for wireless ad hoc networks is proposed. With this protocol, the resource reservation table of each node will record the bandwidth requirements of data flows, which access itself, its neighbor nodes and hidden nodes, and every node calculates the remaining available bandwidth by deducting the bandwidth reserved in the resource reservation table from the total available bandwidth of the node. Moreover, the BCMR searches in a distributed manner for the paths with the shortest delay conditioned by the bandwidth constraint. Simulation results demonstrate the good performance of BCMR in terms of packet delivery reliability and the delay. BCMR can meet the requirements of real time communication and can be used in the multicast applications with low mobility in wireless ad hoc networks. 展开更多
关键词 wireless ad hoc networks multicast routing bandwidth satisfaction on-demand multicast routing distributed multicast routing QoS multicast routing
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