TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Raeed Al-Sabri, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha	DMGCRL: Dynamic Multi-Scale Graph Contrastive Representation Learning For Network Intrusion Detection	2026	Early Access	Modeling Intrusion detection Labeling	Graph neural networks (GNNs) have recently attracted significant attention in network intrusion detection systems (NIDS) due to their ability to model network traffic as graphs and capture complex relationships within network flows. However, existing GNN-based methods face critical limitations: they rely on limited or noisy labeled data and struggle to detect threats at various scales, ranging from local anomalies (e.g., port scanning) to coordinated subnetwork attacks (e.g., botnets) and global network-wide campaigns (e.g., DDoS attacks). To bridge this gap, we propose Dynamic Multiscale Graph Contrastive Representation Learning (DMGCRL), a self-supervised framework that hierarchically models network intrusions at different levels. At the node level, DMGCRL constructs structure-aware subnetworks around individual traffic flows to capture fine-grained behavioral deviations. For subnetwork-level threats, it employs substructure-aware pooling to identify coordinated anomalies among clustered malicious nodes. Finally, at the global level, DMGCRL derives representations that reflect the holistic state of the network, enabling detection of large-scale threats, such as distributed malware propagation. DMGCRL designs a shared GNN encoder with a multi-level contrastive loss to align multiscale representations while largely eliminating label dependence. It learns discriminative features from unlabeled traffic, refines decision boundaries without supervision, and reveals anomalies by contrasting related and unrelated nodes across scales. Performance evaluation was conducted on five publicly available network traffic datasets for binary and multiclass detection. Results show that DMGCRL consistently outperforms SOTA methods, achieving an F1 score of 99.86% on NF-CSECIC-IDS2018-V2 and 96.11% on NF-UNSW-NB15-V2 under binary detection and the lowest mean false positive rates, 1.28% and 2.33% under multiclass detection on the two datasets.	10.1109/TNSM.2026.3726282
Ryota Nakamura, Kengo Urata, Shigeaki Harada	A Virtual Network Embedding Method Enhancing Self-consumption of Renewable Energy	2026	Early Access	Timing Joining processes Real-time systems	To achieve carbon neutrality in telecommunications networks, renewable energy (RE) must be utilized more efficiently despite its inherent fluctuations. These fluctuations often cause mismatches between power generation and demand, leading to surplus power in some sites and shortages in others. To address this issue, we formulate the problem of maximizing RE use in the allocation of virtualized ICT workloads, which can be realized through techniques such as VM live migration across sites and time periods. To efficiently solve the formulated problem, we propose a heuristic search method that leverages virtualization to enable VM workload migration across sites and time periods, thereby optimizing resource allocation according to RE availability. The proposed method is designed to increase RE consumption while managing trade-offs with communication quality and equipment efficiency. Simulation results demonstrate that our approach can improve RE utilization by up to 30% while maintaining acceptable performance, even under prediction errors. These findings highlight the potential of virtualization-based control frameworks for building more sustainable and environmentally conscious telecommunications networks.	10.1109/TNSM.2026.3726977
Liang Liu, Wenbin Zhai, Feng Wang, Youwei Ding, Wanying Lu, Weizhi Meng	Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks	2026	Early Access	Labeling Modeling Internet of Things	The expansive attack surfaces and device heterogeneity of Internet of Things (IoT) networks pose significant challenges for anomaly detection. While Federated Learning (FL) enables privacy-preserving detection, existing FL methods typically assume fully labeled client data, which is unrealistic for practical IoT deployments. Resource constraints and network heterogeneity further complicate the trade-off among training efficiency, detection accuracy, and communication overhead. To address these challenges, we propose FedS3A, a Federated Semi-Supervised and Semi-Asynchronous learning framework for IoT anomaly detection. FedS3A operates in a practical disjoint semi-supervised setting where the server holds limited labeled data and clients possess extensive unlabeled data. We apply pseudo-labeling with a dynamically decaying weight to balance server-side supervised training and client-side unsupervised learning. To improve round efficiency, we introduce a semi-asynchronous model update and staleness-tolerant distribution scheme that scales client contributions to the global model based on local model staleness and participation frequency. We also adopt a group-based aggregation function to mitigate the impact of non-IID client data, and utilize sparse difference transmission to reduce communication overhead. We evaluate FedS3A on the CIC-IDS2017 and Edge-IIoTset datasets, and the results demonstrate that FedS3A consistently outperforms representative FL approaches in detection performance and round efficiency. FedS3A achieves over 98% accuracy even under non-IID settings while reducing communication costs by approximately 50%.	10.1109/TNSM.2026.3727228
Guiyan Liu, Ji Li, Kaixin Qin, Songtao Guo, Liang Liu, Li Yin	AdpVDLTS: Adaptive Spatio-Temporal VNF Placement and Load Balanced Traffic Scheduling in Edge Computing Networks	2026	Early Access	Loading Modeling Timing	The rise of network function virtualization (NFV) technology has enabled virtual network functions (VNF) and service function chains (SFCs) to develop into standard paradigms for service delivery. The uncertain traffic brought about by edge computing has made it a key issue to figure out how to deploy VNFs for network load balancing. However, traditional methods are limited to SFC embedding solutions and resource management and pay less attention to traffic. To address the above issue, this paper takes into account the spatio-temporal characteristics of network traffic and the traffic scheduling after VNF deployment to solve the load balanced VNF deployment problem. We formalize the problem into an NP-hard nonlinear integer programming problem, which will be solved with the proposed algorithm adaptive VNF deployment and load balanced traffic scheduling (AdpVDLTS). AdpVDLTS divides network time into large and small time slots to operate on traffic and VNFs simultaneously, and achieves load balancing through traffic prediction and collaboration with VNF deployment. Compared with the excellent existing algorithms, AdpVDLTS can maintain more stable load balancing, higher throughput, lower deployment cost, and lower latency. In addition, the effectiveness of the traffic prediction algorithm is proved by ablation experiments.	10.1109/TNSM.2026.3729261
Abderrahmane Boulahdour, Miloud Bagaa, Adlen Ksentini, Ahmed Ouameur Messaoud, Daniel Massicotte	Towards Software-Defined TSN Scheduling: An eBPF Approach for Stream Processing and Delay Analysis in Industry 5.0	2026	Early Access	Streams Timing Bridges	This paper presents a software-defined Time-Sensitive Networking (TSN) architecture that implements the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) using Extended Berkeley Packet Filter (eBPF) technology within Linux-based TSN bridges. By moving traffic shaping logic to the kernel level, our solution eliminates the need for dedicated hardware and enables dynamic, programmable control of frame filtering, metering, and queuing. A Software-Defined Networking (SDN) controller complements the design, providing centralized orchestration of TSN behavior through standardized interfaces and a unified network view. We implement the ATS scheduling model to compute and enforce per-stream eligibility times, supporting time-aware scheduling of concurrent streams within the same priority class. This enables deterministic traffic delivery, which is critical for industrial automation and control. Our approach allows seamless integration into existing infrastructures and aligns with the flexibility objectives of Industry 5.0. Performance evaluations demonstrate accurate scheduling behavior under heterogeneous traffic conditions and quantify the delay introduced by a TSN bridge for multiple coexisting streams.	10.1109/TNSM.2026.3729563
Alex Piccioni, Andrea Marotta, Claudia Rinaldi, Dajana Cassioli, Fabio Graziosi	From Ground to Air: Making Mobile Networks Ready for Urban Air Mobility	2026	Early Access	Urban air mobility Modeling Optimization	Urban Air Mobility (UAM) is expected to be an innovative component of future transportation systems. It introduces great flexibility (additional degrees of freedom) to mobility in 3D space and paves the way to novel design methodologies of wireless communication networks to support both critical and non-critical communications. Main requirements for these networks are high reliability, low latency, and high throughput, barely addressable with existing terrestrial networks unless these are upgraded on the basis of a thorough assessment of coverage capabilities and network planning methodologies. This work investigates the suitability of modern mobile networks, such as 5G and forthcoming 6G, to enable the delicate UAM operations and identifies the gaps to be filled in. The study introduces an enhanced evaluation framework based on a system-level simulator tailored to UAM-specific characteristics in realistic scenarios. Our methodology allows us to determine the minimum number of enhanced Base Stations (eBSs) that must be upgraded to satisfy the considered throughput and BLock Error Rate (BLER) requirements under the adopted system assumptions. The framework integrates a channel-aware selection strategy that prioritizes the upgrades in the subset of base stations that allow the provisioning of the most favorable aggregated link conditions. We explore a variety of realistic deployment scenarios, analyzing the impact of parameters such as UAM Vehicle (UV) population, performance requirements, transmit power, and target BLER on achievable network performance. The results highlight the trade-offs between infrastructure density and service quality, offering practical guidelines for network operators to design UAM-ready deployments along the evolution path toward 6G systems.	10.1109/TNSM.2026.3728202
Yuqiang Wen, K. L. Eddie Law	Selected Group Broadcasting: Modeling and Applications	2026	Early Access	Modeling Broadcasting Timing	Modern decentralized applications frequently employ one-to-many messaging over dynamic recipient groups, a paradigm we term Selected Group Broadcasting (SGB). Though one-to-one traffic models (e.g., M/M/1 queue) are well studied, traffic characterization for one-to-many connectivity remains underdeveloped. In this paper, we investigate the delay distribution of one-to-many traffic over an SGB in a wide-area communication system. Depending on the underlying network input, the SGB problem naturally decomposes into two distinct paradigms: SGBs (for Sequential inputs) and SGBp (for Parallel inputs). Asymptotic analysis reveals that SGBs and SGBp are governed by fundamentally different stochastic dynamics, necessitating dedicated modeling approaches for each. This paper focuses exclusively on the SGBs paradigm, providing a rigorous analytical treatment and establishing an operatortheoretic performance framework. For first-order statistics of SGBs, we derive exact closed-form solutions under exponential inter-ACK interval (ACK Interval) inputs. By exploiting the rational Laplace-domain structure of the forward operator, we extend the model to precisely compute output distributions for any non-negative distribution input. Furthermore, we introduce an equivalent SGBs analysis scheme via the inverse operator, enabling the transformation of continuous commit-time distributions into physically interpretable ACK Interval profiles. For second-order statistics, we establish a formalized functional hypothesis under exponential ACK Intervals, validated via simulation. We verify the derived probability density functions with simulated delays; the results are confirmed via Kolmogorov-Smirnov tests.	10.1109/TNSM.2026.3729017
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
Yali Yuan, Yu Huang, Xingjian Zeng, Hantao Mei, Guang Cheng	M3S-UPD: Efficient Multi-Stage Self-Supervised Learning for Fine-Grained Encrypted Traffic Classification with Unknown Pattern Discovery	2026	Early Access	Labeling Modeling Electronic mail	The growing complexity of encrypted network traffic presents dual challenges for modern network management: accurate multiclass classification of known applications and reliable discovery of unknown traffic patterns. Although deep learning models show promise in controlled environments, their real-world deployment is hindered by data scarcity, concept drift, and operational constraints. This paper proposes M3S-UPD, a novel Multi-Stage Self-Supervised learning framework for encrypted traffic classification and unknown pattern discovery that synergistically integrates semi-supervised learning with representation analysis. Our approach provides a unified framework for known-class classification and unknown pattern discovery through a four-phase iterative process: 1) probabilistic embedding generation, 2) clustering-based structure discovery, 3) distribution-aligned outlier identification, and 4) confidence-aware model updating. Key innovations include a self-supervised mechanism for unknown pattern discovery that requires neither synthetic samples nor prior knowledge, and a continuous learning framework designed for reliable model updating. Experimental results show that M3S-UPD not only outperforms existing methods on the few-shot encrypted traffic classification task, but also simultaneously achieves competitive performance on the zero-shot unknown pattern discovery task. The code is available at https://github.com/fatmo666/M3S-UPD/.	10.1109/TNSM.2026.3729337
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Yuya Miyaoka, Masaki Inoue, Kengo Urata, Shigeaki Harada	Chat-Driven Optimal Management for Virtual Network Services	2026	Early Access	Modeling Large language models Central Processing Unit	This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent, but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two distinct Interpreter implementations are introduced: a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM). Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based approach achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. We also compare our cascade structure method with an end-to-end LLM approach, highlighting our proposed method’s high level of reliability.	10.1109/TNSM.2026.3726950
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Mustafa Türk, Müge Sayıt, Ali C. Begen, Andreas J. Kassler	FROG: Fast Response to Optimization Goals for HTTP Adaptive Streaming over SDN	2026	Early Access	Optimization Videos Servers	In modern video streaming systems, clients typically make independent bitrate decisions without full knowledge of network conditions, often leading to inefficient resource usage and unstable quality. Network-assisted adaptive streaming is rapidly gaining importance in Network and Service Management (NSM), as operators strive to deliver consistently high Quality of Experience (QoE) under dynamic traffic conditions. This paper introduces FROG, a novel, real-time Software-Defined Networking (SDN)-assisted framework designed to coordinate multi-server and multipath HTTP Adaptive Streaming (HAS) using standardized metrics carriage: Common Media Client Data (CMCD) and Common Media Server Data (CMSD). FROG employs an innovative two-stage optimization workflow in which an initial Linear Programming (LP) model rapidly determines feasible bandwidth bounds, server selection, and path capacities, thereby transforming the remaining optimization into a sequential layer-selection process for coordinated quality selection and flow allocation. This decomposition enables sub-second optimization at the scale of thousands of users, while buffer-aware client feedback is integrated to proactively prevent stalls and maintain system stability. Experiments on an emulated SDN testbed demonstrate that FROG achieves QoE comparable to that of the optimal MILP solution on tractable instances while virtually eliminating video stalls, and significantly reduces quality oscillations, outperforming state-of-the-art network-assisted approaches by up to 2.32× under playback-driven evaluation scenarios. Scalability experiments with up to 4,000 clients further demonstrate sub-second optimization runtimes, confirming the practicality of FROG for large-scale deployments.	10.1109/TNSM.2026.3729597
Ali Alfatemi, Ahmed Alfaqeer, Mohamed Rahouti, Zakirul Alam Bhuiyan, Abdellah Chehri	Sentinel: A Neuro-Symbolic Co-Evolutionary Framework for Trustworthy Network and Service Management Against DDoS Attacks	2026	Early Access	Modeling Distributed denial-of-service attack Management	Distributed Denial of Service (DDoS) attacks threaten service continuity in next-generation networks, where autonomous mitigation must suppress attack traffic without unnecessarily degrading legitimate service. This paper presents Sentinel, a neuro-symbolic co-evolutionary framework for DDoS-aware network and service management. Sentinel combines a Proximal Policy Optimization (PPO) defender with a symbolic safety layer that repairs unsafe actions, enforces domain-specific constraints, and supports interpretable rule crystallization. Training further uses a Hall-of-Fame archive of historical attackers and benchmark checkpoint selection to mitigate late-stage co-evolutionary degradation. We evaluate Sentinel over 2,000 generations and 10 independent seeds across benign, mild-attack, strong-attack, chaos/flash-crowd, and held-out ICMP flood scenarios. Results show that Sentinel Pareto-dominates the ShieldOnly ablation on mild, strong, and chaos scenarios under a joint service-quality, leakage, and outage criterion, and Pareto-dominates unshielded PPO on zero-shot ICMP. In the ICMP setting, Sentinel eliminates severe outages in the evaluated scenario (0 vs. 145.7 for PPO) and reduces leakage by 8.6 percentage points through symbolic ICMP overrides. Benchmark checkpointing reduces strong-attack leakage by 8.1 percentage points and severe outages by 98.3 steps relative to the final checkpoint. Rule crystallization achieves 97.97% held-out accuracy using a depth-4 decision tree over four interpretable traffic features. Strict SLA compliance is not achieved under attack conditions, and all results are simulation-based within the modeled traffic and attack distributions.	10.1109/TNSM.2026.3729146
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
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
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
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
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
Pingping Dong, Liying Chen, Xuan Yao, Kai Wang, Lianming Zhang, Jiawei Huang	Fumer: Proactive Time-Shifting for Synchronized Periodic Traffic in Distributed Training	2026	Early Access	Training Timing Modeling	The growth of distributed training models, with parameters now reaching the billion-scale, has shifted the system bottleneck from computation to communication. While Remote Direct Memory Access (RDMA) is widely deployed to improve network performance by circumventing the kernel mechanism, the synchronization-computation cycles under the synchronous parallel mode introduce a highly synchronized and periodic “on-off” bursty traffic pattern, which poses significant challenges to data center networking. Consequently, distributed training suffers from two critical bottlenecks: instantaneous congestion during communication and persistent link idleness during computation. These issues lead to severe bandwidth contention and resource underutilization, ultimately hindering overall training efficiency. To address these challenges, this paper proposes Fumer, a proactive periodic traffic optimization framework that shifts the congestion control paradigm from reactive rate adjustment to proactive time-shifting. Specifically, Fumer leverages In-band Network Telemetry (INT) and Fast Fourier Transform (FFT) with signal-wave separation to decompose interleaved traffic signals, aiming to overcome the lack of periodic awareness. Furthermore, Fumer employs an off-peak transmission optimization algorithm to calculate optimal time-shift values, thereby tackling synchronized congestion and link idleness. By executing proactive off-peak scheduling, Fumer shifts overlapping communication windows into idle periods to smooth traffic peaks in the time domain. Experimental results show that Fumer boosts average path throughput across all workloads to 86.3 Gbps, improving upon DCQCN (42.6 Gbps) by 102.6% and RECC by 22.1%. Furthermore, it reduces the average and 99.9th-percentile iteration times by up to 25.0%-45.4% and 25.5%-49.3%, respectively, demonstrating its efficacy and robustness across diverse large-scale training workloads.	10.1109/TNSM.2026.3728016