TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Jiawei Dong, Mohsen Ghafouri, Chadi Assi	Cybersecurity Risks in Electric Vehicle Charging Management Systems	2026	Early Access	IP networks Security Internet of Things	Electric Vehicle Charging Stations (EVCSs) have become the heart of electrified transportation infrastructure. Their increasing Internet exposure makes them high-value targets for cyberattacks with potential implications for grid stability. This study presents a comprehensive, data-driven security analysis of globally exposed EV Charging Management Systems (EVCMS), bridging theoretical vulnerability concerns with quantified real-world threats. We discover and assess Internet-exposed EVCMS infrastructure, then deploy multi-component honeypot systems to capture live attack intelligence. Our novel EVCS discovery technique identifies 59,359 EVCMS IPs from 798 vendors, with notable improvements in non-English-speaking regions, overcoming traditional scanning limitations. Security assessment of 17 vulnerability types reveals most critical vulnerabilities remain unaddressed three years after coordinated disclosure. Darknet intelligence analysis reveals 10,647 unique IPs that also appeared in our EVCMS dataset participating in unsolicited scanning throughout 2024 with periodic surges. We deploy a comprehensive honeypot system with proactive engagement that actively induces compromised EVCMS devices to attack our infrastructure, spanning two monitoring phases: a ten-day period with proactive engagement and an eight-month period of long-term passive monitoring. We observed generic IoT threats and found no EVCMS-specific malware in the samples examined. A small portion of human exploration activities is found in the web portal honeypot. We observed liveness probes on both the Linux server honeypots and the OCPP backend, serving as a critical warning of imminent threats demanding immediate vendor attention before escalation to destructive campaigns.	10.1109/TNSM.2026.3751489
Zhaogang Shu, Dawei Lin, Haihui Xie, Shuwu Chen	PeriSTNet: A Topology-Anchored Residual Graph Evolution Model for Multi-Step Traffic Matrix Forecasting	2026	Early Access	Forecasting Modeling Matrices	Accurate multi-step Traffic Matrix (TM) forecasting enables Wide Area Network (WAN) operators to plan capacity, anticipate congestion, and assess traffic-engineering actions before service quality is affected. Existing spatio-temporal graph neural networks often rely on a fixed physical topology, whereas fully data-driven and long-sequence models may not explicitly retain network-structure priors. These choices can miss demand correlations induced by routing and application behavior, while recursive decoding can accumulate error at longer horizons. We propose PeriSTNet, a topology-anchored residual graph evolution framework that learns a sparse, phase-conditioned predictive dependency graph around a physical topology prior. This graph is integrated into a Dual-Graph Fusion Gated Recurrent Unit, and a Spatio-Temporal Shortcut stabilizes recursive rollouts. Across Abilene, GÉANT, and CERNET, PeriSTNet achieves lower aggregate MAE, MAPE, and RMSE than the evaluated graph-based and Transformer baselines; horizon-wise, multi-seed, and efficiency analyses support this result under one reproducible protocol. By coupling dynamic predictive dependencies with a stable physical backbone, PeriSTNet provides a lightweight forecasting component for proactive capacity planning and traffic-engineering workflows.	10.1109/TNSM.2026.3753266
Mohammad Saberi, Ali Ahmadi Esfidi, Mahdi Dolati, Naeimeh Omidvar	FlexINA: In-Network Aggregation for Accelerating Distributed Machine Learning with Flexible Routing	2026	Early Access	Modeling Switches Timing	The growing scale and complexity of modern machine learning (ML) models have rendered distributed training indispensable. However, as model dimensions expand, communication overhead increasingly dominates the overall training time, creating a critical performance bottleneck. While techniques such as gradient compression offer partial relief, they often incur substantial computational overhead or require specialized hardware. In-network aggregation (INA) has emerged as a compelling alternative, offloading partial computations—such as gradient reduction—directly onto programmable network switches. Yet, current INA frameworks predominantly rely on rigid, static routing schemes or uncoordinated, opportunistic aggregation, fundamentally limiting their capacity to exploit the full potential of programmable data planes. To address this limitation, we formulate the Joint Scheduling-Routing for In-Network Aggregation (JINA) problem. JINA introduces a time-slotted execution model to enable fine-grained temporal scheduling synchronously with spatial routing. By leveraging source routing, it dictates perpacket forwarding paths while explicitly coordinating multi-hop partial aggregation across the network. Furthermore, we propose FlexINA, a scalable optimization framework that solves the JINA problem through a three-phase dimensionality reduction strategy: host-level pre-aggregation, topological switch filtration, and temporal window partitioning. Extensive evaluations across fifteen diverse network topologies demonstrate that FlexINA reduces the total network load by up to 8.3% compared to INA baselines, while maintaining high computational efficiency.	10.1109/TNSM.2026.3740503
Huiying Jin, Jianyong Zhang, Jiajia Li, Hai Dong, Pengcheng Zhang, Jian Zhou	Dynamic Resource Allocation for Multi-Type Tasks in Mobile Edge Computing	2026	Early Access	Resource management Optimization Algorithms	We propose MD-SFLAPSO (/ˌemdi ˈsflæpsəʊ/), a Multi-type task-oriented Dynamic resource allocation method based on Shuffled Frog Leaping Algorithm and Particle Swarm Optimization for the mobile edge computing environment. MD-SFLAPSO offers an optimized resource allocation strategy. It incorporates a precise matching scheme that accounts for task requirements, collaborative node capabilities, spatial location correlation, latency constraint satisfaction, and resource supply-demand matching degree, to achieve effective collaboration. Additionally, we introduce a multi-node collaborative dynamic resource allocation strategy by leveraging user mobility patterns. Our approach is validated through a series of experiments using public data sets. The results show that MD-SFLAPSO reduces the average SLA violation rate by 8.8 percentage points from the pre-optimization level, improves average resource utilization by 9.3 percentage points over SFLA-RA, and adapts well to different resource configuration scenarios.	10.1109/TNSM.2026.3751416
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Gustavo Z. Bruno, Gabriel M. Almeida, Aloízio Da Silva, Luiz A. DaSilva, Joao F. Santos, Alexandre Huff, Kleber V. Cardoso, Cristiano B. Both	Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency	2026	Early Access	Open RAN Modeling Timing	The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the Open Radio Access Network (O-RAN) architecture to optimize energy efficiency while maintaining Quality of Service (QoS). The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-Real-Time RAN Intelligent Controller (Non-RT RIC) periodically computes energy-aware policies from long-term historical data, while a Near-Real-Time RIC enforces those policies through per-User Equipment (UE) handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a Mixed-Integer Non-Linear Programming (MINLP) model. This model optimizes UE association across E2 Nodes to minimize transmission power and autonomously deactivate underutilized cells, while satisfying strict QoS constraints. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123–0.204 s/UE), confirming the solution’s feasibility for autonomous and sustainable 5G Advanced networks. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.	10.1109/TNSM.2026.3751565
Ivette Kembely Carrera Manosalvas, Paulo Freitas De Araujo-Filho, Georges Kaddoum, Hadi Otrok	Unsupervised Hybrid Quantum-Classical Generative Adversarial Network-Based Intrusion Detection System for 6G-enabled Internet of Things	2026	Early Access	Modeling Signal detection Generative adversarial networks	The emergence of sixth-generation (6G) mobile networks contributes to the widespread adoption of critical Internet-of-Things (IoT) applications, such as telesurgery and autonomous vehicles. However, such advancements expand attack surfaces and make traditional security mechanisms insufficient. While machine learning (ML)-based intrusion detection systems (IDSs) have been increasingly adopted to detect attacks in such heterogeneous and dynamic environments, their complex architectures often result in high computational costs and latency, making them unsuitable for the real-time demands of 6G-enabled IoT applications. To address these challenges, we propose a novel unsupervised quantum generative adversarial network (QGAN)-based IDS. Our solution enhances the GAN generator’s ability to model complex data distributions by combining a parametrized quantum circuit with a classical neural network (NN), while maintaining a lightweight classical discriminator (fewer than 2.000 parameters) at the edge. This design significantly reduces computational complexity and minimizes detection times, since only the classical discriminator is used during inference. Experimental results demonstrate that our solution achieves competitive detection rates with an ultra-low inference latency of 0,22 ms, outperforming state-of-the-art GAN-based and transformer-based IDSs by over 2,81 times. Therefore, our architecture provides a resource-efficient security framework that delivers the ultra-low latency and high detection rates essential for next-generation IoT applications.	10.1109/TNSM.2026.3747752
Hongyuan Cheng, Lihua Yin, Tianqing Zhu, Hongyu Yang, Dexin Zhu, Weixiang Jiang, Nan Wei	Pattern2Bot: A malicious server IP detection model based on self-trained heterogeneous graph neural network	2026	Early Access	Labeling Modeling Signal detection	Botnets play a key role in network attacks such as Distributed Denial-of-Service (DDoS) attacks, spamming and click fraud. Command & Control server is an important node for botmaster to control bots and distribute attack commands. Bot disguise as normal domain requests through DNS traffic, but leave malicious records in DNS logs containing client IP, query domain, server IP and other complex interactions. This information provides important clues for detecting those botnets. In the current study, researchers detect botnets through multiple perspectives, including abstracting DNS communication records into graphs and using graph neural network models for detection. Conventional approaches often necessitate massive labeled datasets for model training, which substantially incurs training costs. Moreover, despite enriching node embeddings via topological relations, graph models often suffer performance degradation when confronted with structural semantic obfuscation from evasion techniques like double-flux. In order to understand the abstract and complex relationship patterns in DNS records while reducing label usage to lower training costs, we propose a botnet Command & Control server detection model based on DNS traffic. This is a self-training heterogeneous graph neural network model that does not require a large number of labels. By integrating the high-order relational semantics derived from multiple meta-paths with the local topologies of target nodes, the model achieves finer-grained structural awareness and semantic abstraction, thereby enhancing detection performance. We validate the effectiveness of the model in a real dataset, and the results show that the model has a better understanding of the special structure possessed by the IPs of Command & Control servers.	10.1109/TNSM.2026.3741111
Shengye Gong, Hui Yang, Qiuyan Yao, Xin Li, Tiankuo Yu, Wenxin Liu, Yang Zhao, Yucong Liu, Jie Zhang, Mohamed Cheriet	Resilient Hierarchical Polyhedron-based Configuration Protection for Large-Scale Multi-Domain Optical Networks	2026	Early Access	High performance computing Joining processes Protection	Large-scale multi-domain optical networks are increasingly exposed to spatially correlated and concurrent failures, while monolithic pre-configured protection structures incur rapidly increasing global search complexity and protection-resource costs under heterogeneous physical constraints. To address this challenge, this paper proposes a resilient Hierarchical Polyhedron-based Configuration (HPC) architecture that decomposes a monolithic global survivability requirement into adaptive intra-domain connectivity constraints and a compact inter-domain connectivity constraint. To alleviate resource imbalance caused by topology-only network partitioning, a Topology-Resource Aware Domain Partitioning (TRADP) algorithm is developed to construct protection-supportable sub-domains by jointly considering topological structure and physical resource distribution. To avoid imposing a rigid protection level on heterogeneous domains, HPC assigns adaptive intra-domain connectivity targets according to local failure exposure and physical resource supportability. To reduce the complexity of cross-domain protection while preserving physical feasibility, a super-node abstraction and physical bridge-link mapping mechanism is employed to construct the inter-domain protection backbone under capacity, spectrum, and SRLG constraints. Extensive evaluations on networks of up to 1000 nodes and representative GEANT and NSFNET topologies demonstrate that HPC-TRADP maintains a recovery rate of 92.94% at 1000 nodes and 85.82% even when 30% of physical links fail concurrently. It further reduces protection overhead by up to 79.60% relative to p-KECS while maintaining efficient recovery paths, demonstrating a favorable balance between survivability, resource efficiency, and scalability.	10.1109/TNSM.2026.3750417
Mario Chahoud, Hani Sami, Ahmad Hammoud, Rabeb Mizouni, Hadi Otrok, Jamal Bentahar, Azzam Mourad, Chamseddine Talhi	Multi-agent Deep Reinforcement Learning Scheme for On-Demand Federated Learning	2026	Early Access	Modeling Federated learning Training	The rapid growth of intelligent IoT devices and mobile edge infrastructures has generated massive decentralized data, necessitating efficient and privacy-preserving learning. Federated Learning (FL) enables distributed model training without sharing raw data; however, its performance degrades in dynamic mobile environments due to unpredictable availability, stragglers, and frequent dropouts. In On-Demand FL, training services and models are deployed only when needed rather than relying on always-active infrastructure, requiring orchestrators to adapt to client arrivals, departures, and mobility while efficiently allocating limited resources and maintaining training continuity. Existing client-selection methods rely on static heuristics or centralized Reinforcement Learning models that struggle to scale and adapt to such dynamics. To address these challenges, we propose a cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for client selection in On-Demand FL. Multiple orchestrators act as agents under a Centralized Training and Decentralized Execution (CTDE) paradigm. The problem is formulated as a Partially Observable Markov Game (POMG), capturing mobility, partial observability, and distributed decision-making. A mobility-aware reward integrates accuracy, resource efficiency, and a Useful Handoff Bonus to preserve learning continuity. Containerization further enhances adaptability. Experiments demonstrate faster convergence, improved resource utilization, and enhanced robustness under real mobility traces.	10.1109/TNSM.2026.3750310
Arshee Naz, Sakshi Maurya, Karan Verma, Geeta Sikka	GRLDR: Space Debris-Aware Adaptive Routing for LEO Satellite Networks Using Graph Reinforcement Learning	2026	Early Access	Routing Satellites Low earth orbit satellites	The growing density of space debris in Low Earth Orbit (LEO) poses a serious challenge to the reliability of satellite communication networks. Existing LEO routing protocols primarily optimize hop count or latency, overlooking debris-induced risks that lead to frequent link disruptions. This paper proposes a Graph Reinforcement Learning-based Debris-Aware Adaptive Routing (GRLDR) framework that integrates Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) to enable intelligent, risk-aware routing in dynamic LEO satellite networks. Real satellite and debris orbital data from Space-Track.org are propagated using the SGP4 model to construct a debris-aware dynamic network graph. Debris risk is quantified using interruption time and radar cross-section features and categorized into low, medium, and high-risk levels. A Graph Attention Network extracts spatial and risk-aware features, while a DRL agent learns routing policies that jointly minimize debris risk, hop count, and end-to-end delay. Simulation results show that the proposed approach significantly reduces debris-exposed routing paths while maintaining low delay, achieving 98.8% hop optimality, up to 28% risk reduction, and a 100% routing success rate compared with conventional routing schemes. This work provides a scalable learning-driven solution for safe and resilient routing in LEO satellite networks.	10.1109/TNSM.2026.3753808
Yichen Zhang, Di Chen, Kaijie Zhu, Zekun Tao, Wei Lin, Rui Zheng	Beyond Valley-Free: Transformer-based AS Path Inference Using Path Embedding	2026	Early Access	Border Gateway Protocol Routing Ranking (statistics)	Gaining insight into autonomous system (AS)-level paths is essential for understanding Internet routing behaviors and enhancing inter-domain routing security. Due to increasingly complex Internet topology and limited measurement access (e.g., router-side ICMP restrictions), few AS paths can be accurately observed, making AS path inference a necessity. However, most existing inference methods are developed based on the valley-free constraint. This limits their ability to capture real-world dynamic routing behaviors. In this paper, we propose PathRank, a Transformer-based method that infers AS paths from a source-AS to a destination-prefix through probability-based ranking, thereby closely reflecting real-world inter-domain routing complexities. From observable BGP data, PathRank generates candidate paths, encodes them as embeddings for a self-attention mechanism to capture cross-hop dependencies, and employs a prefix-length-weighted loss function during training to reflect real-world traffic impacts.We conduct experiments on large-scale public BGP datasets covering IPv4 and IPv6. PathRank places the observed BGP path among the top-10 ranked candidates in 96.82% (IPv4) and 96.65% (IPv6) of test cases, outperforming the strongest external baseline by 4.20 and 11.72 percentage points, respectively. On non-valley-free cases, where valley-free-constrained methods degrade significantly, PathRank maintains inference performance.	10.1109/TNSM.2026.3752819
Wuzhao Li, Weian Guo	Switch-Coupled Scheduling for Energy-Bounded Heterogeneous Edge AI on Commodity Ethernet	2026	Early Access	Energy Modeling Joining processes	Heterogeneous edge-AI clusters behind commodity Ethernet must jointly manage compute queues, shared links, runtime residency, and energy budgets. We formulate request admission and assignment with per-hop FCFS resources, byte-limited TTL–LRU caches, temperature-dependent service, power uncertainty, deadlines, and tenant shares. Switch-Coupled Dual Scheduling (SCD) combines deadline feasibility, cost-aware energy credits, and device plus tenant virtual debts. Analysis establishes restricted-problem hardness, a state-correct finite-horizon certificate, and guarded commitment bounds. Nominal trace-driven simulations show higher offered on-time success and lower mean response than compute-first. These gains accompany higher task energy, admitted tail latency, and mean peak age of information. The success gain over matched drift-plus-penalty scheduling is incremental. Shared link state limits queue escalation under severe simulated bottlenecks. Elastic tenant allocation restores service under tight budgets while retaining device commitment bounds. Repeated hardware experiments compare scheduling under common resource constraints, execute trained neural and tree models on heterogeneous boards, and measure Ethernet path contention. The results support competitive deadline-oriented control with explicit service and energy trade-offs.	10.1109/TNSM.2026.3752827
Aleksandar Avdalović, Joseph Khoury, Ibrahem AlJabea, Mustafa Hajij, Elias Bou-Harb	Beyond Flat Models: Preserving ICS Topology using Combinatorial Complexes for Hierarchy-Aware Intrusion Detection	2026	Early Access	Modeling Cells (biology) Integrated circuits	Industrial Control Systems (ICS) follow the Purdue model hierarchy, organizing sensors, actuators, Programmable Logic Controllers (PLCs), and supervisory control into distinct hierarchical layers. Despite this inherent structure, existing data-driven intrusion detectors still fail to model this intricate property. To address this gap, we introduce a Hierarchy-Aware Intrusion Detector, HAID, a topology-preserving framework that models an ICS as a Combinatorial Complex (CC), operating solely on process telemetry and trained on benign traces to detect falsified sensor measurements and unauthorized actuator commands. The complex captures devices, functional interactions, and PLC control zones within an ICS. CCs (unlike flat models) encode telemetry across hierarchical levels for training a Topological Neural Network (TNN). As such, HAID enables the embedding of the ICS Purdue model into the learning process, yielding anomaly scores and localization at each of the aforementioned levels. Evaluated on two water testbeds, SWaT and WADI, and on a simulated chemical process, TEP, HAID achieves competitive detection performance (82.7% F1 on SWaT and 50.1% on WADI, averaged over five runs), while providing explicit hierarchical localization unavailable in prior work. Through ablations, we confirm the added-value of hierarchical modeling, as removing the PLC-zone representation reduces F1 by 5.8 percentage points on SWaT and causes detection on WADI to collapse. The largest losses are on attacks that manipulate several devices at once, which device- and interactionlevel signals usually miss.	10.1109/TNSM.2026.3746967
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua	Adapting Large Language Models for TCP Fairness over Wi-Fi	2026	Early Access	Large language models TCP Modeling	Designing Transmission Control Protocol (TCP) Congestion Control Algorithms (CCAs) for heterogeneous Wi-Fi networks remains a challenge due to rapidly varying delay, loss, and contention dynamics. These effects often lead to CCA incompatibility, flow unfairness, and starvation among competing TCP flows. Although Deep Reinforcement Learning (DRL) has shown promise in mitigating these challenges, its slow convergence, high training costs, and limited generalization hinder practical deployments. In this paper, we propose TCP-LLM, a learning-augmented transport framework that leverages Large Language Models (LLMs) for compatibility-aware CCA selection. TCP-LLM encodes multivariate TCP time-series into token embeddings and performs single-step discrete CCA decisions through a lightweight LLM decision head. Parameter-efficient low-rank adaptation (LoRA) reduces the number of trainable parameters by approximately two orders of magnitude. Evaluation using traces collected from a physicalWi-Fi testbed with competing TCP Cubic, Bottleneck Bandwidth and Round-trip propagation time (BBR), and Performance-oriented Congestion Control (PCC) flows shows that TCP-LLM achieves the highest mean, geometric-mean, and minimum per-flow throughput among the evaluated schemes. TCP-LLM also exhibits faster and more stable training convergence and supports low-latency bounded inference. These results demonstrate the feasibility of LLM-based sequence adaptation for compatibility-aware transport-layer supervision under heterogeneous wireless conditions.	10.1109/TNSM.2026.3739364
Vincent Sheh, Yao Chiang, Hung-Yu Wei	Two-Timescale Service Orchestration for Dependable Multi-Region Edge Analytics under Adversarial Workloads	2026	Early Access	Modeling Delays Routing	Adversarial workloads can saturate the shared CPU budget of edge nodes, undermining the continuous availability that safety-critical analytics require. Co-locating an inline intrusion detection system (IDS) can filter malicious traffic but creates contention with analytics inference over the same budget, producing a defense-service resource tradeoff. This tradeoff is destabilized by two mechanisms: adaptive Yo-Yo attacks that exploit container scaling delay, and load-based offloading that ignores cross-region IDS mismatch. We propose a two-timescale orchestration framework that jointly minimizes service-level objective (SLO) violations, residual malicious leakage, and benign quality degradation. A long-timescale multi-agent reinforcement learning policy partitions CPU between inspection and inference, internalizing scaling delay and coordinating across regions to suppress oscillatory reallocation. A short-timescale heuristic routes inspection traffic to receivers with compatible detection models, preventing the mismatched-IDS errors that load-based routing would induce. Compared with a representative state-of-the-art baseline, the framework achieves up to 68.8% lower SLO violation rate, 90.9% lower residual malicious leakage, and 64.6% fewer reallocations.	10.1109/TNSM.2026.3753027