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Deep Reinforcement Learning-Based AoI Optimization for AUV-Assisted Data Collection in IoUT Networks 基于深度强化学习的auv辅助数据采集的AoI优化
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-06-24 DOI: 10.1109/LNET.2026.3706766
Vasireddy Venu;Nuthan Chingeetham;Saurabh Kumar Srivastava;Om Jee Pandey;Prasenjit Chanak
{"title":"Deep Reinforcement Learning-Based AoI Optimization for AUV-Assisted Data Collection in IoUT Networks","authors":"Vasireddy Venu;Nuthan Chingeetham;Saurabh Kumar Srivastava;Om Jee Pandey;Prasenjit Chanak","doi":"10.1109/LNET.2026.3706766","DOIUrl":"https://doi.org/10.1109/LNET.2026.3706766","url":null,"abstract":"Autonomous Underwater Vehicles (AUVs) are widely utilized for ocean exploration and data collection in the Internet of Underwater Things (IoUT). However, designing efficient AUV trajectories that jointly optimize data freshness and energy efficiency under dynamic underwater conditions remains a challenging problem. Additionally, in large-scale deployments, homogeneous AUV-based systems struggle to ensure timely, reliable data collection. This article proposes a heterogeneous multi-AUV-assisted framework for data collection in IoUT networks. First, the network is partitioned into multiple regions using a clustering approach. Within each region, optimal rendezvous points are selected using a greedy approach to efficiently collect data. Each horizontal AUV determines its trajectory using a deep Q-network based on Age of Information (AoI), energy consumption, and data queue. Furthermore, a time-aware decision mechanism is incorporated by considering the remaining time until the vertical AUV’s arrival, enabling deadline-constrained data collection. Simulation results demonstrate that the proposed approach effectively reduces AoI while maintaining energy efficiency compared to baseline strategies. The results show that the proposed scheme outperforms the existing methods.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"311-314"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Delay-Aware Reinforcement Learning for O-RAN Control Under Stochastic Reward Feedback 随机奖励反馈下O-RAN控制的延迟感知强化学习
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-06-02 DOI: 10.1109/LNET.2026.3699505
Xingqi Wu;Junaid Farooq;Tao Li;Juntao Chen;Ying Wang
{"title":"Delay-Aware Reinforcement Learning for O-RAN Control Under Stochastic Reward Feedback","authors":"Xingqi Wu;Junaid Farooq;Tao Li;Juntao Chen;Ying Wang","doi":"10.1109/LNET.2026.3699505","DOIUrl":"https://doi.org/10.1109/LNET.2026.3699505","url":null,"abstract":"Reinforcement learning (RL) is increasingly emplo-yed for real-time control in Open Radio Access Networks (O-RANs). However, the asynchronous nature of O-RAN feedback introduces stochastic delays between actions and their observable rewards, leading to mis-aligned credit assignment and unstable training. This letter formulates the O-RAN control problem as a Markov decision process (MDP) with delayed and stochastic rewards, where the true reward corresponding to an action is received after an unknown delay. To address this, we propose a delay-aware deep RL framework that integrates a long short-term memory (LSTM)-based delay predictor and a temporal reward re-alignment module within an off-policy learning pipeline. Using empirical delay traces collected from the Open AI Cellular (OAIC) testbed, we demonstrate that the proposed framework achieves faster convergence and significantly reduces latency violations and packet drops compared to conventional RL methods that ignore feedback delay. The results confirm that reward-delay compensation is critical for reliable AI-driven O-RAN control.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"263-267"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Game-Theoretic and Multi-Agent Reinforcement Learning Framework for Cooperative Merging of Platoons in Multi-Lane On-Ramp 多车道匝道队列协同合并的博弈论多智能体强化学习框架
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-03-11 DOI: 10.1109/LNET.2026.3692211
Ben Liu;Hanyu Li;Junling Shi
{"title":"A Game-Theoretic and Multi-Agent Reinforcement Learning Framework for Cooperative Merging of Platoons in Multi-Lane On-Ramp","authors":"Ben Liu;Hanyu Li;Junling Shi","doi":"10.1109/LNET.2026.3692211","DOIUrl":"https://doi.org/10.1109/LNET.2026.3692211","url":null,"abstract":"In complex highway on-ramp scenarios, the intense interaction between platoons makes it difficult for traditional decoupled decision-making and planning logic to resolve conflicts within mixed platoons. This letter proposes a Bi-level Coordinated Control Framework that achieves a deep coupling of macro-level game-theoretic decisions and micro-level trajectory planning. The upper layer employs a greedy multi-round Stackelberg game to determine the optimal merging sequence by balancing vehicle costs and benefits. The lower layer utilizes a game-guided Multi-Agent Proximal Policy Optimization algorithm to generate smooth trajectories. Simulation results demonstrate that the proposed framework outperforms existing baseline methods in terms of reward convergence, zero-collision safety, and system stability.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"268-272"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
More Tiers, Greater Welfare: Revisiting Transit ISP Pricing in Competitive Markets 层级越多，福利越大：在竞争市场中重新审视运输ISP定价
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-04-27 DOI: 10.1109/LNET.2026.3687802
Abdur Rouf;Nowfel Mashnoor;Murat Yuksel
{"title":"More Tiers, Greater Welfare: Revisiting Transit ISP Pricing in Competitive Markets","authors":"Abdur Rouf;Nowfel Mashnoor;Murat Yuksel","doi":"10.1109/LNET.2026.3687802","DOIUrl":"https://doi.org/10.1109/LNET.2026.3687802","url":null,"abstract":"Distance drives cost differences in Internet transit, yet flat pricing obscures them and induces cross-subsidies. We revisit this mismatch under competition: transit Internet Service Provivers (ISPs) strategically choose pricing granularity (number of distance tiers) and compete for flows. Using a NetFlow-calibrated cost–demand model and a non-cooperative game, we compute Nash equilibria and evaluate profit, consumer surplus, and welfare. Our results support the letter’s thesis: more tiers yield greater welfare. As competition intensifies (more providers or higher price sensitivity), equilibrium shifts toward finer tiering, welfare rises, and providers can sustain higher profits than under coarser menus, a practical high-tier “sweet spot”.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"219-223"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148492085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Consistency-Aware Multi-Server Network Design for Delay-Sensitive Applications Under Multiple-Server Failures 多服务器故障下延迟敏感应用的一致性感知多服务器网络设计
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-03-13 DOI: 10.1109/LNET.2026.3674065
Masaki Oda;Imran Ahmed;Akio Kawabata;Eiji Oki
{"title":"Consistency-Aware Multi-Server Network Design for Delay-Sensitive Applications Under Multiple-Server Failures","authors":"Masaki Oda;Imran Ahmed;Akio Kawabata;Eiji Oki","doi":"10.1109/LNET.2026.3674065","DOIUrl":"https://doi.org/10.1109/LNET.2026.3674065","url":null,"abstract":"Delay-sensitive applications demand minimal latency and strict event ordering. Distributing processing to multiple edge servers can reduce access delay, but it is essential to ensure a consistent state across those servers and handle server failures. Existing models address consistency with only single-server failures, but multiple-server failures can occur in real networks. This letter proposes a server allocation model for a consistency-aware multi-server network in delay-sensitive applications with preventive start-time optimization (PSO) under multiple-server failures. PSO determines the assignment to minimize the worst-case delay across all considered failure scenarios without introducing service disruption for users connected to non-failed servers. We formulate the proposed model as an integer linear programming problem. We prove the decision version of the server allocation problem is NP-hard. Numerical results show that the proposed model outperforms start-time optimization in terms of the largest total delay and run-time optimization in terms of avoiding instability.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"291-295"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11434488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442239","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}
引用次数: 0
RSDD: Resilient Statistical Drift Detection for vSDN Hypervisors RSDD：弹性统计漂移检测vSDN管理程序
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-04-14 DOI: 10.1109/LNET.2026.3683210
Mohamed Khalafalla Hassan;Ilias Cherkaoui;Ibrahim Y. Alshareef;Habib Hamam;Indrakshi Dey
{"title":"RSDD: Resilient Statistical Drift Detection for vSDN Hypervisors","authors":"Mohamed Khalafalla Hassan;Ilias Cherkaoui;Ibrahim Y. Alshareef;Habib Hamam;Indrakshi Dey","doi":"10.1109/LNET.2026.3683210","DOIUrl":"https://doi.org/10.1109/LNET.2026.3683210","url":null,"abstract":"Sixth-Generation (6G) virtualized Software-Defined Networks (vSDNs) require hypervisors that remain stable under non-stationary and bursty traffic. Existing systems, including DLVisor, still rely on a single Anderson–Darling test, which reacts poorly to gradual or variance-driven distributional shifts. This work introduces a unified drift-aware hypervisor, the Resilient Statistical Drift Detection (RSDD) framework, combining KSWIN, Page–Hinkley, Jensen–Shannon divergence, and variance monitoring into a consensus drift-severity index that guides an xLSTM forecaster and cost-aware slice reallocator. Experiments on Tier-2 ISP traces show RSDD significantly reduces overutilization events while maintaining SLA-compliant bandwidth allocation.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"301-305"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Adaptive Weighting Mechanism for Missing Data Imputation: An Early Evaluation 缺失数据输入的自适应加权机制：早期评估
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-04-06 DOI: 10.1109/LNET.2026.3681233
Mohammad Reza Nosouhi;Keshav Sood;Robin Doss;Gang Li;Zubair Baig
{"title":"An Adaptive Weighting Mechanism for Missing Data Imputation: An Early Evaluation","authors":"Mohammad Reza Nosouhi;Keshav Sood;Robin Doss;Gang Li;Zubair Baig","doi":"10.1109/LNET.2026.3681233","DOIUrl":"https://doi.org/10.1109/LNET.2026.3681233","url":null,"abstract":"We propose a novel adaptive weighting mechanism for missing data imputation using Generative Adversarial Networks (GANs) that dynamically adjusts the contribution of each imputed value to the training objective. Our approach integrates three complementary factors into the weighting scheme: 1) the confidence of the generator in each imputed value; 2) the temporal proximity of the missing data to nearby observed data; and 3) feedback from the discriminator, which reflects the realism of the imputation. Experiments on benchmark time series dataset demonstrate that our method significantly improves imputation accuracy and temporal consistency.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"229-233"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148493163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An End-to-End Intelligent Control Architecture for 6G Networks: A Dual-SDN Approach With DRL, QNN and Cognitive Interface 面向6G网络的端到端智能控制体系结构：基于DRL、QNN和认知接口的双sdn方法
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1109/LNET.2026.3652911
Mohamed Amine Hechmi;Sonia Ben Rejeb;Sami Tabbane
{"title":"An End-to-End Intelligent Control Architecture for 6G Networks: A Dual-SDN Approach With DRL, QNN and Cognitive Interface","authors":"Mohamed Amine Hechmi;Sonia Ben Rejeb;Sami Tabbane","doi":"10.1109/LNET.2026.3652911","DOIUrl":"https://doi.org/10.1109/LNET.2026.3652911","url":null,"abstract":"6G networks require ultra-reliable, low-latency, and context-aware orchestration of heterogeneous resources spanning radio access and core domains. This letter proposes an end-to-end (E2E) intelligent architecture leveraging two cooperative SDN-based control planes: one at the Radio Access Network (SDN-RAN) and one at the Core Network (SDN-CN), interconnected via a cognitive East–West interface. The SDN-RAN integrates a DRL agent based on Proximal Policy Optimization (PPO) and Graph Neural Networks (GNN) for dynamic resource allocation and access mode switching (NOMA/RSMA). The SDN-CN, embedded within a Quantum-empowered RIC (Q-RIC), orchestrates virtualized core functions using DRL and Quantum Neural Networks (QNN). A digital twin supervises and simulates decisions in real time. Experimental scenarios show the model’s ability to ensure latency-sensitive service continuity, proactive load balancing, and coordinated migration toward MEC resources.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"110-114"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148491844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Hardware-Aware Methodology for Distributed In-Network Intelligence on FPGA-Based SmartNICs 基于fpga的智能网卡分布式网络智能的硬件感知方法
IF 3.3
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-03-05 DOI: 10.1109/LNET.2026.3671081
Fanny Spagnolo;Mattia Giovanni Spina;Stefania Perri;Antonio Iera;Pasquale Corsonello
{"title":"A Hardware-Aware Methodology for Distributed In-Network Intelligence on FPGA-Based SmartNICs","authors":"Fanny Spagnolo;Mattia Giovanni Spina;Stefania Perri;Antonio Iera;Pasquale Corsonello","doi":"10.1109/LNET.2026.3671081","DOIUrl":"https://doi.org/10.1109/LNET.2026.3671081","url":null,"abstract":"Future network infrastructures will increasingly leverage artificial intelligence (AI) models for automation of internal management procedures and for service offering. To this aim, distributing learning capabilities within Programmable Data Planes (PDPs) can offer clear advantages, but at the cost of complex design challenges. In this letter, we introduce an effective methodology for distributing AI capabilities across PDP devices, considering hardware resource utilization and energy consumption. Experimental evaluations conducted on FPGA-based programmable platforms demonstrate that the proposed design, when applied to anomalous network traffic detection, attains very high precision, high throughput, and improved power efficiency compared to state-of-the-art solutions in the literature.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"96-100"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148493155","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}
引用次数: 0
Experimental Study of Low-Latency Video Streaming in an ORAN Setup With Generative AI 基于生成式人工智能的ORAN低延迟视频流实验研究
IEEE Networking Letters Pub Date : 2026-01-01 Epub Date: 2026-01-12 DOI: 10.1109/LNET.2026.3653203
Andreas Casparsen;Van-Phuc Bui;Shashi Raj Pandey;Jimmy Jessen Nielsen;Petar Popovski
{"title":"Experimental Study of Low-Latency Video Streaming in an ORAN Setup With Generative AI","authors":"Andreas Casparsen;Van-Phuc Bui;Shashi Raj Pandey;Jimmy Jessen Nielsen;Petar Popovski","doi":"10.1109/LNET.2026.3653203","DOIUrl":"https://doi.org/10.1109/LNET.2026.3653203","url":null,"abstract":"Current feedback-based congestion control methods, such as probe-and-adapt solution, for live video streaming react after it occurs, causing buffering and latency spikes. We introduce a proactive semantic control channel enabling coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) for seamless mobile video streaming. When the transmitting UE experiences poor Uplink (UL) conditions, the MEC proactively instructs to downscale video based on low-level RAN metrics–such as millisecond SNR updates, preventing buffering before it fully manifests. A Generative AI (GAI) module at the MEC reconstructs high-quality frames from downscaled video before forwarding them over the typically stronger Downlink (DL). Experiments on a live ORAN testbed with 50 video streams show reduced latency tails and up to 4 dB PSNR and 15 Video Multi-Method Assessment Fusion (VMAF) gains over reactive congestion control. The proactive control eliminates latency spikes over 600 ms, demonstrating effective cross-layer coordination for latency-critical streaming.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"8 ","pages":"159-163"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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