IET Networks最新文献

{"title":"MT-OPMNet: Attention-Enhanced Multi-Task Deep Learning for Joint OSNR Estimation and Modulation Format Recognition in Elastic Optical Networks","authors":"Yassir Al-Karawi,&nbsp;Ahmed M. Jasim,&nbsp;Raad S. Alhumaima,&nbsp;Hamed S. Al-Raweshidy","doi":"10.1049/ntw2.70038","DOIUrl":"https://doi.org/10.1049/ntw2.70038","url":null,"abstract":"<p>Optical performance monitoring (OPM) underpins quality of transmission in modern elastic optical networks. This paper introduces MT-OPMNet, a multi-task deep learning framework for joint optical signal-to-noise ratio (OSNR) estimation and modulation format identification (MFI) from asynchronous amplitude histograms extracted at coherent receivers. The model combines a shared one-dimensional convolutional backbone, a channel-aware attention module and two task-specific heads for parameter-efficient inference. Validation uses a corrected nine-channel wavelength-division-multiplexed simulation dataset covering distances from 500 to 3000 km, symbol rates of 28 GBd and 64 GBd, five launch-power levels and five modulation formats. At 28 GBd, MT-OPMNet achieves an OSNR root-mean-square error of approximately 2.4 dB; over the grouped mixed-rate test split, it achieves an average OSNR RMSE of 3.4 dB and an MFI accuracy of 97.5%, while reducing trainable parameters by 23.8% relative to two separate single-task models. The analytical channel model is further cross-validated using a split-step Fourier waveform simulator, achieving mean histogram cosine similarity of 0.976 and zero-shot transfer to SSFM-generated waveforms with 100% MFI accuracy and 2.1 dB OSNR RMSE over 14–22 dB OSNR. CPU inference latency remains suitable for low-latency monitoring studies.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994328","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}

{"title":"Attention-Enhanced Hybrid Deep Learning for Intrusion Detection in IoT Networks","authors":"Umair Ali Zaki,&nbsp;Muhammad Usman Yaseen,&nbsp;Muhammad Imran,&nbsp;Muhammad Kazim","doi":"10.1049/ntw2.70037","DOIUrl":"https://doi.org/10.1049/ntw2.70037","url":null,"abstract":"<p>The escalating deployment of Internet of Things (IoT) devices across critical infrastructure demands intrusion detection systems (IDS) capable of reliably discriminating hierarchically structured cyber threats such as denial-of-service, man-in-the-middle, Mirai, and scan attacks. This study proposes a hybrid CNN-GRU-attention architecture in which parallel convolutional and recurrent branches are fused through a cross-modal attention mechanism computing joint scores from spatial and temporal features via a shared scoring function, enabling adaptive per-sample prioritisation of the more discriminative branch. ADASYN and ProWRAS oversampling strategies are compared within a leakage-free pipeline across binary, category and sub-category classification on IoTID20, and validated independently on TON-IoT against six deep learning baselines. On IoTID20, test accuracies reach 1.0000, 0.9995 and 0.8228/0.8225 (ADASYN/ProWRAS). McNemar's tests show no significant difference between samplers, although per-class analysis reveals a precision–recall trade-off at the sub-category tier. On TON-IoT, the model achieves 0.9788 accuracy and F1-score, exceeding six baselines on most metrics, trailing only in balanced accuracy due to one underrepresented class. Attention-weight analysis shows concentration decreasing with granularity, with ProWRAS inducing a more distributed pattern that explains this trade-off. An ablation study confirms attention and oversampling chiefly improve precision (0.8019–0.9005) over accuracy, though residual Mirai confusion reflects a feature-space limitation.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942469","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}

{"title":"WITHDRAWAL: An Adaptive Elephant Herd Optimisation-Based Load Balancing Algorithm for Energy Efficient Networks","authors":"","doi":"10.1049/ntw2.12072","DOIUrl":"https://doi.org/10.1049/ntw2.12072","url":null,"abstract":"<p><b>WITHDRAWAL:</b> S. Venkatasubramanian, P.L, RajaRajeshwari, S. Hariprasath, A. N. Ahmed, “An adaptive elephant herd optimisation-based load balancing algorithm for energy efficient networks”, <i>IET Networks</i> (2025): e12072. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ntw2.12072</p><p>The above article, published online on 15 January 2026, on Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the Institution of Engineering and Technology and John Wiley &amp; Sons Ltd. UK. The withdrawal has been agreed as this article was published due to a technical error on the part of Wiley, the publisher, and without author agreement. The manuscript was submitted to and published in a different journal prior to this accidental publication.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943425","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}

{"title":"WITHDRAWAL: Data Sharing and Information Security Under the Software-Defined Network","authors":"","doi":"10.1049/ntw2.12074","DOIUrl":"https://doi.org/10.1049/ntw2.12074","url":null,"abstract":"<p><b>WITHDRAWAL:</b> J. Sheng, Y. Fang, M. Li, L. Zhang, J. Xu, X. Ding, “Data Sharing and Information Security Under the Software-Defined Network”, <i>IET Networks</i> (2025): e12074. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ntw2.12074</p><p>The above article, published online on 15 January 2026, on Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the Institution of Engineering and Technology and John Wiley &amp; Sons Ltd. UK. The withdrawal has been agreed as this manuscript was under consideration but the journal decided not to proceed with publication. Despite this decision, the article was published due to a technical error on the part of Wiley, the publisher, as an unedited accepted article prior to the version of record.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148943424","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}

{"title":"WITHDRAWAL: Big Data Technology Fusion Back Propagation Neural Network in Product Innovation Design Method","authors":"","doi":"10.1049/ntw2.12073","DOIUrl":"10.1049/ntw2.12073","url":null,"abstract":"<p><b>WITHDRAWAL:</b> R. Li, Q. Zeng, “Big Data Technology Fusion Back Propagation Neural Network in Product Innovation Design Method”, <i>IET Networks</i> (2022): e12073. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ntw2.12073</p><p>The above article, published online on 05 December 2022, on Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the Institution of Engineering and Technology and John Wiley &amp; Sons Ltd. UK. The withdrawal has been agreed as this manuscript was under consideration, but the journal decided not to proceed with publication. However, the article was accidentally published due to a technical error on the part of Wiley, the publisher, as an unedited accepted article prior to the version of record.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41549323","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}

{"title":"IETO: An Improved Exponential–Trigonometric Optimisation Algorithm for Dynamic 3D UAV Trajectory Planning","authors":"Baofeng Ji,&nbsp;Chuyang Li,&nbsp;Ji Zhang,&nbsp;Shizhong Wei,&nbsp;Huitao Fan","doi":"10.1049/ntw2.70035","DOIUrl":"https://doi.org/10.1049/ntw2.70035","url":null,"abstract":"<p>Three-dimensional unmanned aerial vehicle (UAV) path planning in cluttered terrain requires a planner to search a continuous waypoint space, whereas the actual safety of a candidate can be assessed only after decoding the waypoint vector into a flight path. The decoded path must satisfy clearance, no-fly-zone, altitude, segment-length and turning constraints, which makes the practical fitness landscape nonconvex, nonsmooth and strongly affected by narrow feasible corridors. This paper therefore studies UAV trajectory generation through a finite-budget derivative-free planning framework. An improved Exponential–Trigonometric Optimisation algorithm (IETO) is proposed for this real-coded UAV waypoint model. The method combines optimised Latin hypercube sampling for initial spatial coverage, adaptive exploration–exploitation control for search-radius regulation and probabilistic local refinement for terminal path improvement. Candidate paths are ranked by a nonsmooth exterior-penalty fitness and are finally accepted only after explicit decoded-path feasibility checks. Benchmark tests and deterministic, uncertain and long-range UAV scenarios show that IETO produces high-quality feasible trajectories and achieves the lowest mean planning fitness in the reported main UAV comparison under the same evaluation budget.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784692","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}

{"title":"A Game-Theoretic Reinforcement Learning Approach to Energy-Balanced Cluster-Head Selection in Wireless Sensor Networks","authors":"Mehrshad Eskandarpour,&nbsp;Saba Pirahmadian,&nbsp;Parham Soltani,&nbsp;Hossein Soleimani","doi":"10.1049/ntw2.70036","DOIUrl":"https://doi.org/10.1049/ntw2.70036","url":null,"abstract":"<p>Wireless sensor networks (WSNs) are constrained by limited node energy, making balanced cluster-head (CH) selection essential for extending network lifetime and maintaining reliable data delivery. This paper proposes an energy-aware hierarchical clustering framework that integrates game theory (GT) and reinforcement learning (RL) for adaptive CH selection. The GT component employs a normalised utility function incorporating residual energy, local connectivity, communication distance and expected cluster load, thereby enabling decentralised and dimensionally consistent clustering decisions. The RL component uses a compact discrete state representation, executable node-level actions, a transition-based scalar reward and off-policy Q-learning to optimise long-term energy balance, connectivity, delay and packet-delivery performance. Four configurations—full GT, full RL, GT + RL and RL + GT—are evaluated under identical simulation conditions. The proposed GT + RL framework is additionally compared with DEEC, LEACH, Q-learning-based routing and Dijkstra-based shortest-path routing using 30 paired independent runs. Among the internal configurations, full RL achieves the highest final success rate and the lowest residual-energy variance, whereas the hybrid approaches provide a favourable balance between structured decision-making and adaptability. In the external comparison, GT + RL achieves mean first-node-death, half-node-death and last-node-death values of 35.1, 63.9 and 91.1 rounds, respectively, corresponding to improvements of approximately 27.6%, 12.3% and 7.3% over the Q-learning routing baseline. These results demonstrate that combining utility-driven clustering with adaptive learning can improve energy distribution, delay premature node failure and enhance WSN stability.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784556","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}

{"title":"Reinforcement Learning–Enhanced Influence Maximisation via Credibility-Based Clustering and Metaheuristic Optimisation","authors":"Mohammad Mehdi Karimi,&nbsp;Abbas Karimi,&nbsp;Faraneh Zarafshan,&nbsp;Javad Mohammadzadeh","doi":"10.1049/ntw2.70034","DOIUrl":"https://doi.org/10.1049/ntw2.70034","url":null,"abstract":"<p>Influence maximisation (IM) seeks to identify a limited set of seed nodes that maximises information diffusion in social networks. Traditional centrality-based and greedy approaches often rely on static structural assumptions and overlook community heterogeneity, behavioural credibility and adaptive optimisation. To address these limitations, this study integrates credibility-based community detection, metaheuristic optimisation, and reinforcement learning-driven parameter adaptation in a three-phase framework. Evaluated on four datasets (Caida, Cond-Mat, Douban and route views), the framework achieves 29%–68% influence spread and 11%–25% runtime reduction compared to degree centrality baselines. Ablation analysis confirms the incremental contribution of each component, demonstrating improvements from the base model (20% Caida, 3% route views) to the full model (29% Caida, 53% route views). First, type-2 fuzzy clustering combines structural and behavioural features to identify cooperative communities and prioritise credible nodes. Second, the Ali Baba and the 40 Thieves (AFT) metaheuristic selects diverse and high-impact seed nodes using a multi-criteria fitness function that balances preferential attachment, neighbourhood overlap reduction and structural similarity. Third, a reward-adaptive mechanism dynamically adjusts the fitness weights (<i>α</i>, <i>β</i> and <i>γ</i>) based on diffusion feedback, allowing the framework to adapt to varying network conditions within the tested regimes (subcritical, near-critical and supercritical). The framework is evaluated on four real-world datasets under a weighted independent cascade model across subcritical, near-critical and supercritical regimes. Compared to the IMBC baseline, the framework achieves higher influence spread across all regimes, with ablation analysis quantifying each component's contribution: base model (3%–20% spread) → full dodel (29%–68% spread), representing 1.45× to 17.67× relative improvement.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752796","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}

{"title":"Instance-Adaptive Deviation Bounds for Graph-Diffused Allocation in Networked Systems","authors":"Ardavan Rahimian","doi":"10.1049/ntw2.70033","DOIUrl":"https://doi.org/10.1049/ntw2.70033","url":null,"abstract":"<p>Graph-structured designs often require a seeded nonnegative profile to remain well controlled under diffusion, so that spreading across the graph is limited while smoothness or regularity is preserved. This raises a practical question: how large must the Laplacian regularisation weight be to certify that the eventual diffused steady state stays within a prescribed deviation from the original design? For row-stochastic and generally nonsymmetric diffusion operators, standard worst-case certificates are often conservative as they rely on global mismatch and dimensional surrogates rather than on the realised profile itself. The work derives a proof-consistent instance-adaptive certificate that combines a symmetric certificate operator, a non-symmetry remainder, a profile-dependent diagonal-mismatch term and an effective support size. The resulting expression remains closed form, supports fixed-instance inversion and one-shot pilot-based tuning, and is compatible with practically relevant nonsymmetric diffusion models. The affine diffusion model is interpreted as a graph-side mixing/restart mechanism rather than as a mass-conserving physical transport law; thus, norm stability and conservation are kept conceptually separate. The proposed framework can further be applied to a number of applications, including graph-based beamforming, wireless resource allocation and other networked diffusion settings where certified control of spread is required without repeated parameter sweeps.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615691","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}

{"title":"Correction to “Improvising the Performance of Nomadic Vehicular Cloud by Reducing Task Migrations”","authors":"","doi":"10.1049/ntw2.70031","DOIUrl":"https://doi.org/10.1049/ntw2.70031","url":null,"abstract":"<p>[IET Networks, Volume 14, Issue 1, 2025, https://doi.org/10.1049/ntw2.70006]</p><p>[Author details of Swathi Agarwal are incorrect. Update to</p><p>Swathi Agarwal, Department of Information Technology, CVR College of Engineering, Hyderabad, Telangana, India.]</p><p>We apologize for this error.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":"15 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173706","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}