IET Networks最新文献_第2页

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IET Networks Pub Date : 2026-05-26

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IET Networks Pub Date : 2026-05-22

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IET Networks Pub Date : 2026-05-15

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IET Networks Pub Date : 2026-05-12

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Steady-State Spread Bounds for Graph Diffusion via Laplacian Regularisation in Networked Systems 网络系统中拉普拉斯正则化图扩散的稳态扩展界

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IET Networks Pub Date : 2026-02-20 DOI: 10.1049/ntw2.70026

Ardavan Rahimian

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IET Networks Pub Date : 2026-02-20

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Performance Optimisation Framework for Wireless Sensor Networks Based on Multi-Algorithm Collaboration 基于多算法协作的无线传感器网络性能优化框架

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IET Networks Pub Date : 2026-01-24 DOI: 10.1049/ntw2.70024

Youhai Zhang, Yujie Ma

{"title":"Performance Optimisation Framework for Wireless Sensor Networks Based on Multi-Algorithm Collaboration","authors":"Youhai Zhang, Yujie Ma","doi":"10.1049/ntw2.70024","DOIUrl":"https://doi.org/10.1049/ntw2.70024","url":null,"abstract":"<p>This paper presents an integrated optimisation framework for wireless sensor networks (WSNs) designed to manage the competing demands of energy efficiency, latency reduction, throughput improvement and communication reliability under dynamic and large-scale deployment conditions. The framework reorganises and enhances three core optimisation methods—genetic algorithm (GA), particle swarm optimisation (PSO) and an improved NSGA-II—by embedding adaptive behaviours and topology-aware decision logic. The GA is strengthened through a zone-oriented crossover mechanism and a sink-distribution-based initialisation strategy, which enhance coverage robustness and fault tolerance. The PSO module applies self-adjusting learning coefficients and QoS-aware routing constraints to maintain efficient path selection under varying load conditions. The improved NSGA-II incorporates an adaptive selection mechanism and a direction-guided crossover operator to better balance energy consumption and delay in multi-objective optimisation. Simulation results show that the proposed framework consistently outperforms federated DDQN and adaptive MOPSO across all performance indicators. It also demonstrates superior multi-objective convergence quality, achieving an IGD of 0.03 and an HV of 0.87. Overall, the framework enhances the scalability, resilience and operational efficiency of WSNs and provides practical guidance for adaptive scheduling in complex real-world environments.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146091328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Deep Reinforcement Learning Reconfigurable Smart Surface Sensing MAC Protocol for Terahertz Mesh Networks 用于太赫兹网状网络的深度强化学习可重构智能表面传感MAC协议

IF 1.4

IET Networks Pub Date : 2026-01-24 DOI: 10.1049/ntw2.70025

Wenjian Zhang, Ping Li

{"title":"Deep Reinforcement Learning Reconfigurable Smart Surface Sensing MAC Protocol for Terahertz Mesh Networks","authors":"Wenjian Zhang, Ping Li","doi":"10.1049/ntw2.70025","DOIUrl":"https://doi.org/10.1049/ntw2.70025","url":null,"abstract":"<p>Terahertz (THz) communication is a key enabler for 6G wireless networks but suffers from severe path loss and dynamic blockage, making conventional MAC protocols inefficient. This paper proposes a reconfigurable intelligent surface–based deep reinforcement learning MAC (RIS-DRL-MAC) framework that enables cross-layer optimisation between the physical and MAC layers. By embedding RIS perception features—such as equivalent channel gain and link stability—into the state space and jointly optimising beam direction, channel access and RIS phase configuration through a distributed twin delayed deep deterministic policy gradient (TD3) algorithm, the protocol achieves adaptive environment control. Simulation results show that, under dynamic blockage and high-load conditions, RIS-DRL-MAC improves network throughput by up to 90%, reduces access delay by 50% and maintains over 90% link availability compared with baseline schemes. The proposed method establishes a closed loop of sensing, decision and environment reconfiguration, providing an effective solution for reliable and energy-efficient THz mesh networking.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IF 1.4

IET Networks Pub Date : 2026-01-24

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IF 1.4

IET Networks Pub Date : 2026-01-24

引用次数: 0